From 83b6e6a0c609e5abb21050b4ceeb6df3d84cf35c Mon Sep 17 00:00:00 2001 From: njzjz Date: Sat, 2 Dec 2023 03:38:39 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20deepmode?= =?UTF-8?q?ling/blog@cf9e8a1580f1e38956f0c45431dc54e9fd109dbd=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 2022_csi_workshop/index.html | 2 +- archives/2021/06/index.html | 2 +- archives/2021/07/index.html | 2 +- archives/2021/index.html | 2 +- archives/2022/07/index.html | 2 +- archives/2022/index.html | 2 +- archives/2023/11/index.html | 1 + archives/2023/index.html | 1 + archives/index.html | 2 +- atom.xml | 4 +-- categories/index.html | 2 +- categories/tutorial/index.html | 2 +- index.html | 4 +-- manifesto/index.html | 2 +- openlam/index.html | 1 + papers/deepmd-kit/index.html | 2 +- papers/dpgen/index.html | 2 +- papers/index.html | 2 +- papers/others.html | 2 +- papers/reviews.html | 2 +- search.xml | 45 +++++++++++++++++++++++++++++++++- sitemap.xml | 2 +- tags/DeePMD-kit/index.html | 2 +- tutorial1/index.html | 4 +-- tutorial2/index.html | 2 +- 25 files changed, 71 insertions(+), 25 deletions(-) create mode 100644 archives/2023/11/index.html create mode 100644 archives/2023/index.html create mode 100644 openlam/index.html diff --git a/2022_csi_workshop/index.html b/2022_csi_workshop/index.html index 9c34ebf..51632d4 100644 --- a/2022_csi_workshop/index.html +++ b/2022_csi_workshop/index.html @@ -1 +1 @@ -2022 CSI Workshop: Deep Modeling for Molecular Simulation | DeepModeling

DeepModeling

Define the future of scientific computing together

2022 CSI Workshop: Deep Modeling for Molecular Simulation

Posted on Word count in article: 124 Reading time ≈ 1 mins.

Lecture 1: Deep Potential Method for Molecular Simulation, Roberto Car

Lecture 2: Deep Potential at Scale, Linfeng Zhang

Lecture 3: Towards a Realistic Description of H3O+ and OH- Transport, Robert A. DiStasio Jr.

Lecture 4: Next Generation Quantum and Deep Learning Potentials, Darrin York

Lecture 5: Linear Response Theory of Transport in Condensed Matter, Stefano Baroni

Lecture 6: Deep Modeling with Long-Range Electrostatic Interactions, Chunyi Zhang

Hands-on session 4: Machine learning of Wannier centers and dipoles

Hands-on session 5: Long range electrostatic interactions with DPLR

Hands-on session 6: Concurrent learning with DP-GEN

0%
\ No newline at end of file +2022 CSI Workshop: Deep Modeling for Molecular Simulation | DeepModeling

DeepModeling

Define the future of scientific computing together

2022 CSI Workshop: Deep Modeling for Molecular Simulation

Posted on Word count in article: 124 Reading time ≈ 1 mins.

Lecture 1: Deep Potential Method for Molecular Simulation, Roberto Car

Lecture 2: Deep Potential at Scale, Linfeng Zhang

Lecture 3: Towards a Realistic Description of H3O+ and OH- Transport, Robert A. DiStasio Jr.

Lecture 4: Next Generation Quantum and Deep Learning Potentials, Darrin York

Lecture 5: Linear Response Theory of Transport in Condensed Matter, Stefano Baroni

Lecture 6: Deep Modeling with Long-Range Electrostatic Interactions, Chunyi Zhang

Hands-on session 4: Machine learning of Wannier centers and dipoles

Hands-on session 5: Long range electrostatic interactions with DPLR

Hands-on session 6: Concurrent learning with DP-GEN

0%
\ No newline at end of file diff --git a/archives/2021/06/index.html b/archives/2021/06/index.html index 4f959f9..d424749 100644 --- a/archives/2021/06/index.html +++ b/archives/2021/06/index.html @@ -1 +1 @@ -Archive | DeepModeling

DeepModeling

Define the future of scientific computing together

Um..! 4 posts in total. Keep on posting.
2021
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\ No newline at end of file +Archive | DeepModeling

DeepModeling

Define the future of scientific computing together

Um..! 5 posts in total. Keep on posting.
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DeepModeling

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Um..! 4 posts in total. Keep on posting.
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DeepModeling

Define the future of scientific computing together

Um..! 4 posts in total. Keep on posting.
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DeepModeling

Define the future of scientific computing together

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DeepModeling

Define the future of scientific computing together

Um..! 4 posts in total. Keep on posting.
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DeepModeling

Define the future of scientific computing together

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DeepModeling

Define the future of scientific computing together

Um..! 4 posts in total. Keep on posting.
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DeepModeling

Define the future of scientific computing together

Um..! 5 posts in total. Keep on posting.
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DeepModeling

Define the future of scientific computing together

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DeepModeling

Define the future of scientific computing together

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\ No newline at end of file diff --git a/atom.xml b/atom.xml index 8ca232e..2395058 100644 --- a/atom.xml +++ b/atom.xml @@ -1,4 +1,4 @@ -DeepModelingDefine the future of scientific computing together2022-07-07T16:00:00.000Zhttps://deepmodeling.com/blog/DeepModelingHexo2022 CSI Workshop: Deep Modeling for Molecular Simulationhttps://deepmodeling.com/blog/2022_csi_workshop/2022-07-07T16:00:00.000Z2022-07-07T16:00:00.000Z

Lecture 1: Deep Potential Method for Molecular Simulation, Roberto Car

Lecture 2: Deep Potential at Scale, Linfeng Zhang

Lecture 3: Towards a Realistic Description of H3O+ and OH- Transport, Robert A. DiStasio Jr.

Lecture 4: Next Generation Quantum and Deep Learning Potentials, Darrin York

Lecture 5: Linear Response Theory of Transport in Condensed Matter, Stefano Baroni

Lecture 6: Deep Modeling with Long-Range Electrostatic Interactions, Chunyi Zhang

Hands-on session 4: Machine learning of Wannier centers and dipoles

Hands-on session 5: Long range electrostatic interactions with DPLR

Hands-on session 6: Concurrent learning with DP-GEN

]]><link rel="stylesheet" type="text&#x2F;css" href="https://cdn.jsdelivr.net/hint.css/2.4.1/hint.min.css"><h2 id="Lecture-1-Deep-Potential-MetDP Tutorial 2: DeePMD-kit: Install with Conda & Offline Packages & Dockerhttps://deepmodeling.com/blog/tutorial2/2021-07-05T16:00:00.000Z2021-07-05T16:00:00.000Z

Do you prepare to read a long article before clicking the tutorial? Since we can teach you how to setup a DeePMD-kit training in 5 minutes, we can also teach you how to install DeePMD-kit in 5 minutes. The installation manual will be introduced as follows:

Install with conda

After you install conda, you can install the CPU version with the following command:

1
conda install deepmd-kit=*=*cpu lammps-dp=*=*cpu -c deepmodeling

To install the GPU version containing CUDA 10.1:

1
conda install deepmd-kit=*=*gpu lammps-dp=*=*gpu -c deepmodeling

If you want to use the specific version, just replace * with the version:

1
conda install deepmd-kit=1.3.3=*cpu lammps-dp=1.3.3=*cpu -c deepmodeling

Install with offline packages

Download offline packages in the Releases page, or use wget:

1
wget https://github.com/deepmodeling/deepmd-kit/releases/download/v1.3.3/deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh -O deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh

Take an example of v1.3.3. Execuate the following commands and just follow the prompts.

1
sh deepmd-kit-1.3.1-cuda10.1_gpu-Linux-x86_64.sh

With Docker

To pull the CPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cpu
To pull the GPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cuda10.1_gpu

Tips

dp is the program of DeePMD-kit and lmp is the program of LAMMPS.

1
2
dp -h
lmp -h

GPU version has contained CUDA Toolkit. Note that different CUDA versions support different NVIDIA driver versions. See NVIDIA documents for details.

Don't hurry up and try such a convenient installation process. But I still want to remind everyone that the above installation methods only support the official version released by DeePMD-kit. If you need to use the devel version, you still need to go through a long compilation process. Please refer to the installation manual.

]]><link rel="stylesheet" type="text&#x2F;css" href="https://cdn.jsdelivr.net/hint.css/2.4.1/hint.min.css"><p>Do you prepare to read a long artDP Tutorial 1: How to Setup a DeePMD-kit Training within 5 Minutes?https://deepmodeling.com/blog/tutorial1/2021-06-11T16:00:00.000Z2021-06-11T16:00:00.000Z

DeePMD-kit is a software to implement Deep Potential. There is a lot of information on the Internet, but there are not so many tutorials for the new hand, and the official guide is too long. Today, I'll take you 5 minutes to get started with DeePMD-kit.

Let's take a look at the training process of DeePMD-kit:

graph LRA[Prepare data] --> B[Training]B --> C[Freeze the model]

What? Only three steps? Yes, it's that simple.

  1. Preparing data is converting the computational results of DFT to data that can be recognized by the DeePMD-kit.
  2. Training is train a Deep Potential model using the DeePMD-kit with data prepared in the previous step.
  3. Finally, what we need to do is to freeze the restart file in the training process into a model, in other words is to extract the neural network parameters into a file for subsequent use. I believe you can't wait to get started. Let's go!

1. Preparing Data

The data format of the DeePMD-kit is introduced in the official document but seems complex. Don't worry, I'd like to introduce a data processing tool: dpdata! You can use only one line Python scripts to process data. So easy!

1
2
import dpdata
dpdata.LabeledSystem('OUTCAR').to('deepmd/npy', 'data', set_size=200)

In this example, we converted the computational results of the VASP in the OUTCAR to the data format of the DeePMD-kit and saved in to a directory named data, where npy is the compressed format of the numpy, which is required by the DeePMD-kit training. We assume OUTCAR stores 1000 frames of molecular dynamics trajectory, then where will be 1000 points after converting. set_size=200 means these 1000 points will be divided into 5 subsets, which is named as data/set.000~data/set.004, respectively. The size of each set is 200. In these 5 sets, data/set.000~data/set.003 will be considered as the training set by the DeePMD-kit, and data/set.004 will be considered as the test set. The last set will be considered as the test set by the DeePMD-kit by default. If there is only one set, the set will be both the training set and the test set. (Of course, such test set is meaningless.)

2. Training

It's required to prepare an input script to start the DeePMD-kit training. Are you still out of the fear of being dominated by INCAR script? Don't worry, it's much easier to configure the DeePMD-kit than configuring the VASP. First, let's download an example and save to input.json:

1
wget https://raw.githubusercontent.com/deepmodeling/deepmd-kit/v1.3.3/examples/water/train/water_se_a.json -O input.json

The strength of the DeePMD-kit is that the same training parameters are suitable for different systems, so we only need to slightly modify input.json to start training. Here is the first parameter to modify:

1
"type_map":     ["O", "H"],

In the DeePMD-kit data, each atom type is numbered as an integer starting from 0. The parameter gives an element name to each atom in the numbering system. Here, we can copy from the content of data/type_map.raw. For example,

1
"type_map":    ["A", "B","C"],

Next, we are going to modify the neighbour searching parameter:

1
"sel":       [46, 92],

Each number in this list gives the maximum number of atoms of each type among neighbor atoms of an atom. For example, 46 means there are at most 46 O (type 0) neighbours. Here, our elements were modified to A, B, and C, so this parameters is also required to modify. What to do if you don't know the maximum number of neighbors? You can be roughly estimate one by the density of the system, or try a number blindly. If it is not big enough, the DeePMD-kit will shoot WARNINGS. Below we changed it to 

1
"sel":       [64, 64, 64]

In addtion, we need to modify

1
"systems":     ["../data/"],

to

1
"systems":     ["./data/"],

It is because that the directory to write to is ./data/ in the current directory. Here I'd like to introduce the definition of the data system. The DeePMD-kit considers that data with corresponding element types and atomic numbers form a system. Our data is generated from a molecular dynamics simulation and meets this condition, so we can put them into one system. Dpdata works the same way. If data cannot be put into a system, multiple systems is required to be set as a list here:

1
2
"training": {
       "systems": ["system1", "system2"]

Finnally, we are likely to modify another two parameters:

1
2
"stop_batch":   1000000,
"batch_size":   1,

stop_batch is the numebr of training step using the SGD method of deep learning, and batch_size is the mini-batch size of data in each step.
If we want to reduce stop_batch and use batch_size that the DeePMD-kit recommends, we can use

1
2
"stop_batch":   500000,
"batch_size":   "auto",

Now we have succesfully set a input file! To start training, we execuate

1
dp train input.json

and wait for results. During the training process, we can see lcurve.out to observe the error reduction.Among them, Column 4 and 5 are the test and training errors of energy (normalized by the number of atoms), and Column 6 and 7 are the test and training errors of the force.

3. Freeze the Model

After training, we can use the following script to freeze the model:

1
dp freeze

The default filename of the output model is frozen_model.pb. As so, we have got a good or bad DP model. As for the reliability of this model and how to use it, I will give you a detailed tutorial in the next post.

]]><p>DeePMD-kit is a software to implement Deep Potential. There is a lot of information on the Internet, but there are not so many tutorials for the new hand, and the official guide is too long. Today, I&#39;ll take you 5 minutes to get started with DeePMD-kit. </p> +DeepModelingDefine the future of scientific computing together2023-11-30T16:00:00.000Zhttps://deepmodeling.com/blog/DeepModelingHexoThe OpenLAM Initiativehttps://deepmodeling.com/blog/openlam/2023-11-30T16:00:00.000Z2023-11-30T16:00:00.000Z

Peter Thiel once said, "We wanted flying cars, instead we got 140 characters (Twitter)." Over the past decade, we have made great strides at the bit level (internet), but progress at the atomic level (cutting-edge technology) has been relatively slow.

The accumulation of linguistic data has propelled the development of machine learning and ultimately led to the emergence of Large Language Models (LLMs). With the push from AI, progress at the atomic level is also accelerating. Methods like Deep Potential, by learning quantum mechanical data, have increased the space-time scale of microscopic simulations by several orders of magnitude and have made significant progress in fields like drug design, material design, and chemical engineering.

The accumulation of quantum mechanical data is gradually covering the entire periodic table, and the Deep Potential team has also begun the practice of the DPA pre-training model. Analogous to the progress of LLMs, we are on the eve of the emergence of a general Large Atom Model (LAM). At the same time, we believe that open-source and openness will play an increasingly important role in the development of LAM.

Against this backdrop, the core developer team of Deep Potential is launching the OpenLAM Initiative to the community. This plan is still in the draft stage and is set to officially start on January 1, 2024. We warmly and openly welcome opinions and support from all parties.

The slogan for OpenLAM is "Conquer the Periodic Table!" We hope to provide a new infrastructure for microscale scientific research and drive the transformation of microscale industrial design in fields such as materials, energy, and biopharmaceuticals by establishing an open-source ecosystem around large microscale models. Relevant models, data, and workflows will be consolidated around the AIS Square; related software development will take place in the DeepModeling open-source community. At the same time, we welcome open interaction from different communities in model development, data sharing, evaluation, and testing.

OpenLAM's goals for the next three years are: In 2024, to effectively cover the periodic table with first-principles data and achieve a universal property learning capability; in 2025, to combine large-scale experimental characterization data and literature data to achieve a universal cross-modal capability; and in 2026, to realize a target-oriented atomic scale universal generation and planning capability. Ultimately, within 5-10 years, we aim to achieve "Large Atom Embodied Intelligence" for atomic-scale intelligent scientific discovery and synthetic design.

OpenLAM's specific plans for 2024 include:

  • Model Update and Evaluation Report Release:

    • Starting from January 1, 2024, driven by the Deep Potential team, with participation from all LAM developers welcomed.
    • Every three months, a major model version update will take place, with updates that may include model architecture, related data, training strategies, and evaluation test criteria.

  • AIS Cup Competition:

    • Initiated by the Deep Potential team and supported by the Bohrium Cloud Platform, starting in March 2024 and concluding at the end of the year;
    • The goal is to promote the creation of a benchmarking system focused on several application-oriented metrics.

  • Domain Data Contribution:

    • Seeking collaboration with domain developers to establish "LAM-ready" datasets for pre-training and evaluation.
    • Domain datasets for iterative training of the latest models will be updated every three months.

  • Domain Application and Evaluation Workflow Contribution:

    • The domain application and evaluation workflows will be updated and released every three months.

  • Education and Training:

    • Planning a series of educational and training events aimed at LAM developers, domain developers, and users to encourage advancement in the field.

  • How to Contact Us:

    • Direct discussions are encouraged in the DeepModeling community.
    • For more complex inquiries, please contact the project lead, Han Wang (王涵, wang_han@iapcm.ac.cn), Linfeng Zhang (张林峰, zhanglf@aisi.ac.cn), for the new future of Science!
]]><link rel="stylesheet" type="text&#x2F;css" href="https://cdn.jsdelivr.net/hint.css/2.4.1/hint.min.css"><p>Peter Thiel once said, &quot;We w2022 CSI Workshop: Deep Modeling for Molecular Simulationhttps://deepmodeling.com/blog/2022_csi_workshop/2022-07-07T16:00:00.000Z2022-07-07T16:00:00.000Z

Lecture 1: Deep Potential Method for Molecular Simulation, Roberto Car

Lecture 2: Deep Potential at Scale, Linfeng Zhang

Lecture 3: Towards a Realistic Description of H3O+ and OH- Transport, Robert A. DiStasio Jr.

Lecture 4: Next Generation Quantum and Deep Learning Potentials, Darrin York

Lecture 5: Linear Response Theory of Transport in Condensed Matter, Stefano Baroni

Lecture 6: Deep Modeling with Long-Range Electrostatic Interactions, Chunyi Zhang

Hands-on session 4: Machine learning of Wannier centers and dipoles

Hands-on session 5: Long range electrostatic interactions with DPLR

Hands-on session 6: Concurrent learning with DP-GEN

]]><link rel="stylesheet" type="text&#x2F;css" href="https://cdn.jsdelivr.net/hint.css/2.4.1/hint.min.css"><h2 id="Lecture-1-Deep-Potential-MetDP Tutorial 2: DeePMD-kit: Install with Conda & Offline Packages & Dockerhttps://deepmodeling.com/blog/tutorial2/2021-07-05T16:00:00.000Z2021-07-05T16:00:00.000Z

Do you prepare to read a long article before clicking the tutorial? Since we can teach you how to setup a DeePMD-kit training in 5 minutes, we can also teach you how to install DeePMD-kit in 5 minutes. The installation manual will be introduced as follows:

Install with conda

After you install conda, you can install the CPU version with the following command:

1
conda install deepmd-kit=*=*cpu lammps-dp=*=*cpu -c deepmodeling

To install the GPU version containing CUDA 10.1:

1
conda install deepmd-kit=*=*gpu lammps-dp=*=*gpu -c deepmodeling

If you want to use the specific version, just replace * with the version:

1
conda install deepmd-kit=1.3.3=*cpu lammps-dp=1.3.3=*cpu -c deepmodeling

Install with offline packages

Download offline packages in the Releases page, or use wget:

1
wget https://github.com/deepmodeling/deepmd-kit/releases/download/v1.3.3/deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh -O deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh

Take an example of v1.3.3. Execuate the following commands and just follow the prompts.

1
sh deepmd-kit-1.3.1-cuda10.1_gpu-Linux-x86_64.sh

With Docker

To pull the CPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cpu
To pull the GPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cuda10.1_gpu

Tips

dp is the program of DeePMD-kit and lmp is the program of LAMMPS.

1
2
dp -h
lmp -h

GPU version has contained CUDA Toolkit. Note that different CUDA versions support different NVIDIA driver versions. See NVIDIA documents for details.

Don't hurry up and try such a convenient installation process. But I still want to remind everyone that the above installation methods only support the official version released by DeePMD-kit. If you need to use the devel version, you still need to go through a long compilation process. Please refer to the installation manual.

]]><link rel="stylesheet" type="text&#x2F;css" href="https://cdn.jsdelivr.net/hint.css/2.4.1/hint.min.css"><p>Do you prepare to read a long artDP Tutorial 1: How to Setup a DeePMD-kit Training within 5 Minutes?https://deepmodeling.com/blog/tutorial1/2021-06-11T16:00:00.000Z2021-06-11T16:00:00.000Z

DeePMD-kit is a software to implement Deep Potential. There is a lot of information on the Internet, but there are not so many tutorials for the new hand, and the official guide is too long. Today, I'll take you 5 minutes to get started with DeePMD-kit.

Let's take a look at the training process of DeePMD-kit:

graph LRA[Prepare data] --> B[Training]B --> C[Freeze the model]

What? Only three steps? Yes, it's that simple.

  1. Preparing data is converting the computational results of DFT to data that can be recognized by the DeePMD-kit.
  2. Training is train a Deep Potential model using the DeePMD-kit with data prepared in the previous step.
  3. Finally, what we need to do is to freeze the restart file in the training process into a model, in other words is to extract the neural network parameters into a file for subsequent use. I believe you can't wait to get started. Let's go!

1. Preparing Data

The data format of the DeePMD-kit is introduced in the official document but seems complex. Don't worry, I'd like to introduce a data processing tool: dpdata! You can use only one line Python scripts to process data. So easy!

1
2
import dpdata
dpdata.LabeledSystem('OUTCAR').to('deepmd/npy', 'data', set_size=200)

In this example, we converted the computational results of the VASP in the OUTCAR to the data format of the DeePMD-kit and saved in to a directory named data, where npy is the compressed format of the numpy, which is required by the DeePMD-kit training. We assume OUTCAR stores 1000 frames of molecular dynamics trajectory, then where will be 1000 points after converting. set_size=200 means these 1000 points will be divided into 5 subsets, which is named as data/set.000~data/set.004, respectively. The size of each set is 200. In these 5 sets, data/set.000~data/set.003 will be considered as the training set by the DeePMD-kit, and data/set.004 will be considered as the test set. The last set will be considered as the test set by the DeePMD-kit by default. If there is only one set, the set will be both the training set and the test set. (Of course, such test set is meaningless.)

2. Training

It's required to prepare an input script to start the DeePMD-kit training. Are you still out of the fear of being dominated by INCAR script? Don't worry, it's much easier to configure the DeePMD-kit than configuring the VASP. First, let's download an example and save to input.json:

1
wget https://raw.githubusercontent.com/deepmodeling/deepmd-kit/v1.3.3/examples/water/train/water_se_a.json -O input.json

The strength of the DeePMD-kit is that the same training parameters are suitable for different systems, so we only need to slightly modify input.json to start training. Here is the first parameter to modify:

1
"type_map":     ["O", "H"],

In the DeePMD-kit data, each atom type is numbered as an integer starting from 0. The parameter gives an element name to each atom in the numbering system. Here, we can copy from the content of data/type_map.raw. For example,

1
"type_map":    ["A", "B","C"],

Next, we are going to modify the neighbour searching parameter:

1
"sel":       [46, 92],

Each number in this list gives the maximum number of atoms of each type among neighbor atoms of an atom. For example, 46 means there are at most 46 O (type 0) neighbours. Here, our elements were modified to A, B, and C, so this parameters is also required to modify. What to do if you don't know the maximum number of neighbors? You can be roughly estimate one by the density of the system, or try a number blindly. If it is not big enough, the DeePMD-kit will shoot WARNINGS. Below we changed it to 

1
"sel":       [64, 64, 64]

In addtion, we need to modify

1
"systems":     ["../data/"],

to

1
"systems":     ["./data/"],

It is because that the directory to write to is ./data/ in the current directory. Here I'd like to introduce the definition of the data system. The DeePMD-kit considers that data with corresponding element types and atomic numbers form a system. Our data is generated from a molecular dynamics simulation and meets this condition, so we can put them into one system. Dpdata works the same way. If data cannot be put into a system, multiple systems is required to be set as a list here:

1
2
"training": {
       "systems": ["system1", "system2"]

Finnally, we are likely to modify another two parameters:

1
2
"stop_batch":   1000000,
"batch_size":   1,

stop_batch is the numebr of training step using the SGD method of deep learning, and batch_size is the mini-batch size of data in each step.
If we want to reduce stop_batch and use batch_size that the DeePMD-kit recommends, we can use

1
2
"stop_batch":   500000,
"batch_size":   "auto",

Now we have succesfully set a input file! To start training, we execuate

1
dp train input.json

and wait for results. During the training process, we can see lcurve.out to observe the error reduction.Among them, Column 4 and 5 are the test and training errors of energy (normalized by the number of atoms), and Column 6 and 7 are the test and training errors of the force.

3. Freeze the Model

After training, we can use the following script to freeze the model:

1
dp freeze

The default filename of the output model is frozen_model.pb. As so, we have got a good or bad DP model. As for the reliability of this model and how to use it, I will give you a detailed tutorial in the next post.

]]><p>DeePMD-kit is a software to implement Deep Potential. There is a lot of information on the Internet, but there are not so many tutorials for the new hand, and the official guide is too long. Today, I&#39;ll take you 5 minutes to get started with DeePMD-kit. </p> <p>Let&#39;s take a look at the training process of DeePMD-kit:</p> <pre class="mermaid"> graph LR @@ -6,5 +6,5 @@ A[Prepare data] --&gt; B[Training] B --&gt; C[Freeze the model] </pre> -<p>What? Only three steps? Yes, it&#39;s that simple.The DeepModeling Manifestohttps://deepmodeling.com/blog/manifesto/2021-06-09T16:00:00.000Z2021-06-09T16:00:00.000Z

The integration of machine learning and physical modeling is changing the paradigm of scientific research. Those who hope to extend the frontier of science and solve challenging practical problems through computational modeling are coming together in new ways never seen before. This calls for a new infrastructure--new platforms for collaboration, new coding
frameworks, new data processing schemes, and new ways of using the computing power. It also calls for a new culture—the culture of working together closely for the benefit of all, of free exchange and sharing of knowledge and tools, of respect and appreciation of each other's work, and of the pursuit of harmony among diversity.

The DeepModeling community is a community of such a group of people.

What is DeepModeling?

The two most important applications of computing are machine learning and physical modeling. The former is an effective tool for analyzing complex data; the latter is a scientific description of the physical world. The vitality boosted by the effective integration of the two is changing all aspects of scientific research. DeepModeling will ultimately be a set of methodologies and tools that combine machine learning, physical modeling, and cutting-edge computational platforms. People who are attracted by the DeepModeling community are attracted by its open, inclusive environment, as well as its dedication to the cause of advancing scientific computing worldwide.

Why choose open source?

There are different interpretations of the term "open source". The consensus among the DeepModeling community is that open source is a collaborative software development platform based on the spirit of openness and sharing. Open source is a familiar concept for people in the fields of machine learning and computer science, but it is not yet popular in the field of scientific computing. What we advocate is that an algorithm or software should not be judged by the reputation of the journal in which it is published, but by its ability to solve real world problems and its actual contribution to science. The sustainable development of a software requires continuous investment in manpower. It should undergo incremental improvement, and it should be put to the test of solving real-world problems in an open environment. This is often difficult to achieve by individuals or individual groups. The open-source community provides better solutions.

The history of the DeepModeling community

The "DeepModeling Community" started with the initiation of the "deepmd-kit" project. “deepmd-kit" is a software tool that combines machine learning and molecular dynamics, which helps to overcome a long-standing difficulty in the field of molecular dynamics, namely the dilemma of having to choose between efficiency and accuracy. The name "DeepModeling" was proposed by early developers of the deepmd-kit project, with the intention of using deep learning tools to solve the curse of dimensionality problem in multi-scale modeling. DeepModeling has therefore become the name of the GitHub organization (https://github.com/deepmodeling) which manages the original deepmd-kit project. After the development of deepmd-kit, the DeepModeling community has successively initiated projects such as dpdata, dp-gen, and dpdispatcher, and extended the modeling scale to electronic structure level through projects such as deepks-kit and ABACUS. These projects have brought together people from all over the world working on molecular simulations.

The short-term plan and long-term vision of the DeepModeling community

In the short term, developers in the DeepModeling community will focus on atomic-scale simulation methods and tools. This includes solving the many-body Schrödinger equation, electronic structure calculation, molecular dynamics simulation, and coarse-grained molecular dynamics simulation. This also includes tasks such as data generation, model training, high-performance optimization, etc. In addition, it includes different workflows and management tools, as well as computing power scheduling tools for different systems, different scenarios, and different purposes.

It should be pointed out that the combination of physical modeling and machine learning often fundamentally changes the implementation logic of a piece of software. Therefore, the new infrastructure will not be settled once and for all, but will be gradually improved through an iterative process and upgrades from time to time.

In the long run, the DeepModeling community is committed to combining physical models at all scales with machine learning methods, using the most cutting-edge computing platforms to solve the most challenging scientific and technological problems faced by the human society.

How can you contribute?

If you want to contribute to an existing project in the DeepModeling community, please just do so or contact
the corresponding developer directly; if you want to open a new project in the DeepModeling community, or if you want the DeepModeling community to help develop your project, just contact contact@deepmodeling.org.

If you are a programmer who loves science and is attracted by the future scientific computing platform built by the DeepModeling community, you can contribute not only through new algorithms, but also code development specifications, document writing specifications, community databases, task scheduling, workflow management and other tools. In addition, you can contribute to code architecture design and high-performance optimization tasks in the DeepModeling community. People in the field of scientific computing will greatly appreciate your expertise and contribution.

If you are a hardcore developer familiar with topics such as electronic structure calculations, molecular dynamics, and finite element methods, the DeepModeling community will be your place to showcase your talents. The addition of machine learning components requires us to rethink about architecture design, each specific implementation for the tasks mentioned above and high-performance optimization. You will become important bridges that connect other developers, contributors, and users in different areas.

If you have only used some basic scientific software and have worked on some post-processing scripts, the DeepModeling community also needs you. Try to ask questions and communicate on github/gitee and other communication platforms, try to give opinions, and try to fork, commit, pr... Your little by little contribution will make the DeepModeling community better and better, and the DeepModeling community will be very grateful for such contributions.

Even if you are just a bystander, if you support the concept of the DeepModeling community, your recognition and dissemination will also be a great encouragement and support for the DeepModeling community.

Final remarks

Despite the tremendous advances in AI and computing power, the scientific computing community is largely embedded in an old-fashioned culture. Many of the most important tasks rely on legacy codes. The core algorithms used in many commercial software have been outdated. The self-sufficient style of work is similar to that of the agricultural ages
resulting in poor efficiency. It is only in recent years that some promising open-source communities have emerged. However, these communities are often aimed at specific tools for specific scales, and are often maintained by specific academic research groups. They face serious challenges in terms of continuous development and improved user experience.

The DeepModeling project promises to change all that.

The combination of machine learning and physical modeling calls for a new paradigm, the open-source community paradigm. Such a paradigm has long been embraced in the computer and electronics industry, with Linux and Andriod being the very well-known examples. In this sense, what the DeepModeling project does is to borrow these ideas and use them for scientific computing. For people in computational science and engineering, efficient and reusable modeling tools that can be continuously improved will free researchers from the plight of no model or with only ad hoc models. For those who work on machine learning, the world of physical models will provide a relatively new and surely vast playground. Working together as an open-source community will make our work more productive, up to date, reliable, and transparent. The spirit of close collaboration, of respect and building on each other’s work will surely inspire more and more people to join the cause of advancing computing for the benefit of the human society. This is an exciting opportunity. This is the future of scientific computing!

]]><p>The integration of machine learning and physical modeling is changing the paradigm of scientific research. Those who hope to extend the frontier of science and solve challenging practical problems through computational modeling are coming together in new ways never seen before. This calls for a new infrastructure--new platforms for collaboration, new coding<br>frameworks, new data processing schemes, and new ways of using the computing power. It also calls for a new culture—the culture of working together closely for the benefit of all, of free exchange and sharing of knowledge and tools, of respect and appreciation of each other&#39;s work, and of the pursuit of harmony among diversity.</p> +<p>What? Only three steps? Yes, it&#39;s that simple.The DeepModeling Manifestohttps://deepmodeling.com/blog/manifesto/2021-06-09T16:00:00.000Z2021-06-09T16:00:00.000Z

The integration of machine learning and physical modeling is changing the paradigm of scientific research. Those who hope to extend the frontier of science and solve challenging practical problems through computational modeling are coming together in new ways never seen before. This calls for a new infrastructure--new platforms for collaboration, new coding
frameworks, new data processing schemes, and new ways of using the computing power. It also calls for a new culture—the culture of working together closely for the benefit of all, of free exchange and sharing of knowledge and tools, of respect and appreciation of each other's work, and of the pursuit of harmony among diversity.

The DeepModeling community is a community of such a group of people.

What is DeepModeling?

The two most important applications of computing are machine learning and physical modeling. The former is an effective tool for analyzing complex data; the latter is a scientific description of the physical world. The vitality boosted by the effective integration of the two is changing all aspects of scientific research. DeepModeling will ultimately be a set of methodologies and tools that combine machine learning, physical modeling, and cutting-edge computational platforms. People who are attracted by the DeepModeling community are attracted by its open, inclusive environment, as well as its dedication to the cause of advancing scientific computing worldwide.

Why choose open source?

There are different interpretations of the term "open source". The consensus among the DeepModeling community is that open source is a collaborative software development platform based on the spirit of openness and sharing. Open source is a familiar concept for people in the fields of machine learning and computer science, but it is not yet popular in the field of scientific computing. What we advocate is that an algorithm or software should not be judged by the reputation of the journal in which it is published, but by its ability to solve real world problems and its actual contribution to science. The sustainable development of a software requires continuous investment in manpower. It should undergo incremental improvement, and it should be put to the test of solving real-world problems in an open environment. This is often difficult to achieve by individuals or individual groups. The open-source community provides better solutions.

The history of the DeepModeling community

The "DeepModeling Community" started with the initiation of the "deepmd-kit" project. “deepmd-kit" is a software tool that combines machine learning and molecular dynamics, which helps to overcome a long-standing difficulty in the field of molecular dynamics, namely the dilemma of having to choose between efficiency and accuracy. The name "DeepModeling" was proposed by early developers of the deepmd-kit project, with the intention of using deep learning tools to solve the curse of dimensionality problem in multi-scale modeling. DeepModeling has therefore become the name of the GitHub organization (https://github.com/deepmodeling) which manages the original deepmd-kit project. After the development of deepmd-kit, the DeepModeling community has successively initiated projects such as dpdata, dp-gen, and dpdispatcher, and extended the modeling scale to electronic structure level through projects such as deepks-kit and ABACUS. These projects have brought together people from all over the world working on molecular simulations.

The short-term plan and long-term vision of the DeepModeling community

In the short term, developers in the DeepModeling community will focus on atomic-scale simulation methods and tools. This includes solving the many-body Schrödinger equation, electronic structure calculation, molecular dynamics simulation, and coarse-grained molecular dynamics simulation. This also includes tasks such as data generation, model training, high-performance optimization, etc. In addition, it includes different workflows and management tools, as well as computing power scheduling tools for different systems, different scenarios, and different purposes.

It should be pointed out that the combination of physical modeling and machine learning often fundamentally changes the implementation logic of a piece of software. Therefore, the new infrastructure will not be settled once and for all, but will be gradually improved through an iterative process and upgrades from time to time.

In the long run, the DeepModeling community is committed to combining physical models at all scales with machine learning methods, using the most cutting-edge computing platforms to solve the most challenging scientific and technological problems faced by the human society.

How can you contribute?

If you want to contribute to an existing project in the DeepModeling community, please just do so or contact
the corresponding developer directly; if you want to open a new project in the DeepModeling community, or if you want the DeepModeling community to help develop your project, just contact contact@deepmodeling.org.

If you are a programmer who loves science and is attracted by the future scientific computing platform built by the DeepModeling community, you can contribute not only through new algorithms, but also code development specifications, document writing specifications, community databases, task scheduling, workflow management and other tools. In addition, you can contribute to code architecture design and high-performance optimization tasks in the DeepModeling community. People in the field of scientific computing will greatly appreciate your expertise and contribution.

If you are a hardcore developer familiar with topics such as electronic structure calculations, molecular dynamics, and finite element methods, the DeepModeling community will be your place to showcase your talents. The addition of machine learning components requires us to rethink about architecture design, each specific implementation for the tasks mentioned above and high-performance optimization. You will become important bridges that connect other developers, contributors, and users in different areas.

If you have only used some basic scientific software and have worked on some post-processing scripts, the DeepModeling community also needs you. Try to ask questions and communicate on github/gitee and other communication platforms, try to give opinions, and try to fork, commit, pr... Your little by little contribution will make the DeepModeling community better and better, and the DeepModeling community will be very grateful for such contributions.

Even if you are just a bystander, if you support the concept of the DeepModeling community, your recognition and dissemination will also be a great encouragement and support for the DeepModeling community.

Final remarks

Despite the tremendous advances in AI and computing power, the scientific computing community is largely embedded in an old-fashioned culture. Many of the most important tasks rely on legacy codes. The core algorithms used in many commercial software have been outdated. The self-sufficient style of work is similar to that of the agricultural ages
resulting in poor efficiency. It is only in recent years that some promising open-source communities have emerged. However, these communities are often aimed at specific tools for specific scales, and are often maintained by specific academic research groups. They face serious challenges in terms of continuous development and improved user experience.

The DeepModeling project promises to change all that.

The combination of machine learning and physical modeling calls for a new paradigm, the open-source community paradigm. Such a paradigm has long been embraced in the computer and electronics industry, with Linux and Andriod being the very well-known examples. In this sense, what the DeepModeling project does is to borrow these ideas and use them for scientific computing. For people in computational science and engineering, efficient and reusable modeling tools that can be continuously improved will free researchers from the plight of no model or with only ad hoc models. For those who work on machine learning, the world of physical models will provide a relatively new and surely vast playground. Working together as an open-source community will make our work more productive, up to date, reliable, and transparent. The spirit of close collaboration, of respect and building on each other’s work will surely inspire more and more people to join the cause of advancing computing for the benefit of the human society. This is an exciting opportunity. This is the future of scientific computing!

]]><p>The integration of machine learning and physical modeling is changing the paradigm of scientific research. Those who hope to extend the frontier of science and solve challenging practical problems through computational modeling are coming together in new ways never seen before. This calls for a new infrastructure--new platforms for collaboration, new coding<br>frameworks, new data processing schemes, and new ways of using the computing power. It also calls for a new culture—the culture of working together closely for the benefit of all, of free exchange and sharing of knowledge and tools, of respect and appreciation of each other&#39;s work, and of the pursuit of harmony among diversity.</p> <p>The DeepModeling community is a community of such a group of people.</p> \ No newline at end of file diff --git a/categories/index.html b/categories/index.html index 07342f6..ac91457 100644 --- a/categories/index.html +++ b/categories/index.html @@ -1 +1 @@ -Categories | DeepModeling

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\ No newline at end of file diff --git a/index.html b/index.html index 64d6f05..5dbf322 100644 --- a/index.html +++ b/index.html @@ -1,5 +1,5 @@ -DeepModeling - Define the future of scientific computing together

DeepModeling

Define the future of scientific computing together

Posted on Word count in article: 1.3k Reading time ≈ 5 mins.

The integration of machine learning and physical modeling is changing the paradigm of scientific research. Those who hope to extend the frontier of science and solve challenging practical problems through computational modeling are coming together in new ways never seen before. This calls for a new infrastructure--new platforms for collaboration, new coding
frameworks, new data processing schemes, and new ways of using the computing power. It also calls for a new culture—the culture of working together closely for the benefit of all, of free exchange and sharing of knowledge and tools, of respect and appreciation of each other's work, and of the pursuit of harmony among diversity.

The DeepModeling community is a community of such a group of people.

Read more »

Posted on Word count in article: 124 Reading time ≈ 1 mins.

Lecture 1: Deep Potential Method for Molecular Simulation, Roberto Car

Lecture 2: Deep Potential at Scale, Linfeng Zhang

Lecture 3: Towards a Realistic Description of H3O+ and OH- Transport, Robert A. DiStasio Jr.

Lecture 4: Next Generation Quantum and Deep Learning Potentials, Darrin York

Lecture 5: Linear Response Theory of Transport in Condensed Matter, Stefano Baroni

Lecture 6: Deep Modeling with Long-Range Electrostatic Interactions, Chunyi Zhang

Hands-on session 4: Machine learning of Wannier centers and dipoles

Hands-on session 5: Long range electrostatic interactions with DPLR

Hands-on session 6: Concurrent learning with DP-GEN

Posted on In Word count in article: 305 Reading time ≈ 1 mins.

Do you prepare to read a long article before clicking the tutorial? Since we can teach you how to setup a DeePMD-kit training in 5 minutes, we can also teach you how to install DeePMD-kit in 5 minutes. The installation manual will be introduced as follows:

Install with conda

After you install conda, you can install the CPU version with the following command:

1
conda install deepmd-kit=*=*cpu lammps-dp=*=*cpu -c deepmodeling

To install the GPU version containing CUDA 10.1:

1
conda install deepmd-kit=*=*gpu lammps-dp=*=*gpu -c deepmodeling

If you want to use the specific version, just replace * with the version:

1
conda install deepmd-kit=1.3.3=*cpu lammps-dp=1.3.3=*cpu -c deepmodeling

Install with offline packages

Download offline packages in the Releases page, or use wget:

1
wget https://github.com/deepmodeling/deepmd-kit/releases/download/v1.3.3/deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh -O deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh

Take an example of v1.3.3. Execuate the following commands and just follow the prompts.

1
sh deepmd-kit-1.3.1-cuda10.1_gpu-Linux-x86_64.sh

With Docker

To pull the CPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cpu
To pull the GPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cuda10.1_gpu

Tips

dp is the program of DeePMD-kit and lmp is the program of LAMMPS.

1
2
dp -h
lmp -h

GPU version has contained CUDA Toolkit. Note that different CUDA versions support different NVIDIA driver versions. See NVIDIA documents for details.

Don't hurry up and try such a convenient installation process. But I still want to remind everyone that the above installation methods only support the official version released by DeePMD-kit. If you need to use the devel version, you still need to go through a long compilation process. Please refer to the installation manual.

Posted on In Word count in article: 949 Reading time ≈ 3 mins.

DeePMD-kit is a software to implement Deep Potential. There is a lot of information on the Internet, but there are not so many tutorials for the new hand, and the official guide is too long. Today, I'll take you 5 minutes to get started with DeePMD-kit.

Let's take a look at the training process of DeePMD-kit:

+DeepModeling - Define the future of scientific computing together
  
DeepModeling
Define the future of scientific computing together
 
 Posted on    Word count in article: 1.3k  Reading time ≈ 5 mins.
The integration of machine learning and physical modeling is changing the paradigm of scientific research. Those who hope to extend the frontier of science and solve challenging practical problems through computational modeling are coming together in new ways never seen before. This calls for a new infrastructure--new platforms for collaboration, new coding
frameworks, new data processing schemes, and new ways of using the computing power. It also calls for a new culture—the culture of working together closely for the benefit of all, of free exchange and sharing of knowledge and tools, of respect and appreciation of each other's work, and of the pursuit of harmony among diversity.
The DeepModeling community is a community of such a group of people.
Read more »
 Posted on    Word count in article: 625  Reading time ≈ 2 mins.
Peter Thiel once said, "We wanted flying cars, instead we got 140 characters (Twitter)." Over the past decade, we have made great strides at the bit level (internet), but progress at the atomic level (cutting-edge technology) has been relatively slow.
The accumulation of linguistic data has propelled the development of machine learning and ultimately led to the emergence of Large Language Models (LLMs). With the push from AI, progress at the atomic level is also accelerating. Methods like Deep Potential, by learning quantum mechanical data, have increased the space-time scale of microscopic simulations by several orders of magnitude and have made significant progress in fields like drug design, material design, and chemical engineering.
The accumulation of quantum mechanical data is gradually covering the entire periodic table, and the Deep Potential team has also begun the practice of the DPA pre-training model. Analogous to the progress of LLMs, we are on the eve of the emergence of a general Large Atom Model (LAM). At the same time, we believe that open-source and openness will play an increasingly important role in the development of LAM.
Against this backdrop, the core developer team of Deep Potential is launching the OpenLAM Initiative to the community. This plan is still in the draft stage and is set to officially start on January 1, 2024. We warmly and openly welcome opinions and support from all parties.
The slogan for OpenLAM is "Conquer the Periodic Table!" We hope to provide a new infrastructure for microscale scientific research and drive the transformation of microscale industrial design in fields such as materials, energy, and biopharmaceuticals by establishing an open-source ecosystem around large microscale models. Relevant models, data, and workflows will be consolidated around the AIS Square; related software development will take place in the DeepModeling open-source community. At the same time, we welcome open interaction from different communities in model development, data sharing, evaluation, and testing.
OpenLAM's goals for the next three years are: In 2024, to effectively cover the periodic table with first-principles data and achieve a universal property learning capability; in 2025, to combine large-scale experimental characterization data and literature data to achieve a universal cross-modal capability; and in 2026, to realize a target-oriented atomic scale universal generation and planning capability. Ultimately, within 5-10 years, we aim to achieve "Large Atom Embodied Intelligence" for atomic-scale intelligent scientific discovery and synthetic design.
OpenLAM's specific plans for 2024 include:
  • Model Update and Evaluation Report Release:
    • Starting from January 1, 2024, driven by the Deep Potential team, with participation from all LAM developers welcomed.
    • Every three months, a major model version update will take place, with updates that may include model architecture, related data, training strategies, and evaluation test criteria.
  • AIS Cup Competition:
    • Initiated by the Deep Potential team and supported by the Bohrium Cloud Platform, starting in March 2024 and concluding at the end of the year;
    • The goal is to promote the creation of a benchmarking system focused on several application-oriented metrics.
  • Domain Data Contribution:
    • Seeking collaboration with domain developers to establish "LAM-ready" datasets for pre-training and evaluation.
    • Domain datasets for iterative training of the latest models will be updated every three months.
  • Domain Application and Evaluation Workflow Contribution:
    • The domain application and evaluation workflows will be updated and released every three months.
  • Education and Training:
    • Planning a series of educational and training events aimed at LAM developers, domain developers, and users to encourage advancement in the field.
  • How to Contact Us:
    • Direct discussions are encouraged in the DeepModeling community.
    • For more complex inquiries, please contact the project lead, Han Wang (王涵, wang_han@iapcm.ac.cn), Linfeng Zhang (张林峰, zhanglf@aisi.ac.cn), for the new future of Science!
 Posted on    Word count in article: 124  Reading time ≈ 1 mins.
Lecture 1: Deep Potential Method for Molecular Simulation, Roberto Car
Lecture 2: Deep Potential at Scale, Linfeng Zhang
Lecture 3: Towards a Realistic Description of H3O+ and OH- Transport, Robert A. DiStasio Jr.
Lecture 4: Next Generation Quantum and Deep Learning Potentials, Darrin York
Lecture 5: Linear Response Theory of Transport in Condensed Matter, Stefano Baroni
Lecture 6: Deep Modeling with Long-Range Electrostatic Interactions, Chunyi Zhang
Hands-on session 4: Machine learning of Wannier centers and dipoles
Hands-on session 5: Long range electrostatic interactions with DPLR
Hands-on session 6: Concurrent learning with DP-GEN
 Posted on   In    Word count in article: 305  Reading time ≈ 1 mins.
Do you prepare to read a long article before clicking the tutorial? Since we can teach you how to setup a DeePMD-kit training in 5 minutes, we can also teach you how to install DeePMD-kit in 5 minutes. The installation manual will be introduced as follows:
Install with conda
After you install conda, you can install the CPU version with the following command:
1
conda install deepmd-kit=*=*cpu lammps-dp=*=*cpu -c deepmodeling
To install the GPU version containing CUDA 10.1:
1
conda install deepmd-kit=*=*gpu lammps-dp=*=*gpu -c deepmodeling
If you want to use the specific version, just replace * with the version:
1
conda install deepmd-kit=1.3.3=*cpu lammps-dp=1.3.3=*cpu -c deepmodeling
Install with offline packages
Download offline packages in the Releases page, or use wget:
1
wget https://github.com/deepmodeling/deepmd-kit/releases/download/v1.3.3/deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh -O deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh
Take an example of v1.3.3. Execuate the following commands and just follow the prompts.
1
sh deepmd-kit-1.3.1-cuda10.1_gpu-Linux-x86_64.sh
With Docker
To pull the CPU version:
docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cpu
To pull the GPU version:
docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cuda10.1_gpu
Tips
dp is the program of DeePMD-kit and lmp is the program of LAMMPS.
1
2
dp -h
lmp -h
GPU version has contained CUDA Toolkit. Note that different CUDA versions support different NVIDIA driver versions. See NVIDIA documents for details.
Don't hurry up and try such a convenient installation process. But I still want to remind everyone that the above installation methods only support the official version released by DeePMD-kit. If you need to use the devel version, you still need to go through a long compilation process. Please refer to the installation manual.
 Posted on   In    Word count in article: 949  Reading time ≈ 3 mins.
DeePMD-kit is a software to implement Deep Potential. There is a lot of information on the Internet, but there are not so many tutorials for the new hand, and the official guide is too long. Today, I'll take you 5 minutes to get started with DeePMD-kit.
Let's take a look at the training process of DeePMD-kit:
 graph LR
 A[Prepare data] --> B[Training]
 B --> C[Freeze the model]
-
What? Only three steps? Yes, it's that simple.
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What? Only three steps? Yes, it's that simple.

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\ No newline at end of file diff --git a/manifesto/index.html b/manifesto/index.html index 4d80cb1..959f060 100644 --- a/manifesto/index.html +++ b/manifesto/index.html @@ -1 +1 @@ -The DeepModeling Manifesto | DeepModeling

DeepModeling

Define the future of scientific computing together

The DeepModeling Manifesto

Posted on Word count in article: 1.3k Reading time ≈ 5 mins.

The integration of machine learning and physical modeling is changing the paradigm of scientific research. Those who hope to extend the frontier of science and solve challenging practical problems through computational modeling are coming together in new ways never seen before. This calls for a new infrastructure--new platforms for collaboration, new coding
frameworks, new data processing schemes, and new ways of using the computing power. It also calls for a new culture—the culture of working together closely for the benefit of all, of free exchange and sharing of knowledge and tools, of respect and appreciation of each other's work, and of the pursuit of harmony among diversity.

The DeepModeling community is a community of such a group of people.

What is DeepModeling?

The two most important applications of computing are machine learning and physical modeling. The former is an effective tool for analyzing complex data; the latter is a scientific description of the physical world. The vitality boosted by the effective integration of the two is changing all aspects of scientific research. DeepModeling will ultimately be a set of methodologies and tools that combine machine learning, physical modeling, and cutting-edge computational platforms. People who are attracted by the DeepModeling community are attracted by its open, inclusive environment, as well as its dedication to the cause of advancing scientific computing worldwide.

Why choose open source?

There are different interpretations of the term "open source". The consensus among the DeepModeling community is that open source is a collaborative software development platform based on the spirit of openness and sharing. Open source is a familiar concept for people in the fields of machine learning and computer science, but it is not yet popular in the field of scientific computing. What we advocate is that an algorithm or software should not be judged by the reputation of the journal in which it is published, but by its ability to solve real world problems and its actual contribution to science. The sustainable development of a software requires continuous investment in manpower. It should undergo incremental improvement, and it should be put to the test of solving real-world problems in an open environment. This is often difficult to achieve by individuals or individual groups. The open-source community provides better solutions.

The history of the DeepModeling community

The "DeepModeling Community" started with the initiation of the "deepmd-kit" project. “deepmd-kit" is a software tool that combines machine learning and molecular dynamics, which helps to overcome a long-standing difficulty in the field of molecular dynamics, namely the dilemma of having to choose between efficiency and accuracy. The name "DeepModeling" was proposed by early developers of the deepmd-kit project, with the intention of using deep learning tools to solve the curse of dimensionality problem in multi-scale modeling. DeepModeling has therefore become the name of the GitHub organization (https://github.com/deepmodeling) which manages the original deepmd-kit project. After the development of deepmd-kit, the DeepModeling community has successively initiated projects such as dpdata, dp-gen, and dpdispatcher, and extended the modeling scale to electronic structure level through projects such as deepks-kit and ABACUS. These projects have brought together people from all over the world working on molecular simulations.

The short-term plan and long-term vision of the DeepModeling community

In the short term, developers in the DeepModeling community will focus on atomic-scale simulation methods and tools. This includes solving the many-body Schrödinger equation, electronic structure calculation, molecular dynamics simulation, and coarse-grained molecular dynamics simulation. This also includes tasks such as data generation, model training, high-performance optimization, etc. In addition, it includes different workflows and management tools, as well as computing power scheduling tools for different systems, different scenarios, and different purposes.

It should be pointed out that the combination of physical modeling and machine learning often fundamentally changes the implementation logic of a piece of software. Therefore, the new infrastructure will not be settled once and for all, but will be gradually improved through an iterative process and upgrades from time to time.

In the long run, the DeepModeling community is committed to combining physical models at all scales with machine learning methods, using the most cutting-edge computing platforms to solve the most challenging scientific and technological problems faced by the human society.

How can you contribute?

If you want to contribute to an existing project in the DeepModeling community, please just do so or contact
the corresponding developer directly; if you want to open a new project in the DeepModeling community, or if you want the DeepModeling community to help develop your project, just contact contact@deepmodeling.org.

If you are a programmer who loves science and is attracted by the future scientific computing platform built by the DeepModeling community, you can contribute not only through new algorithms, but also code development specifications, document writing specifications, community databases, task scheduling, workflow management and other tools. In addition, you can contribute to code architecture design and high-performance optimization tasks in the DeepModeling community. People in the field of scientific computing will greatly appreciate your expertise and contribution.

If you are a hardcore developer familiar with topics such as electronic structure calculations, molecular dynamics, and finite element methods, the DeepModeling community will be your place to showcase your talents. The addition of machine learning components requires us to rethink about architecture design, each specific implementation for the tasks mentioned above and high-performance optimization. You will become important bridges that connect other developers, contributors, and users in different areas.

If you have only used some basic scientific software and have worked on some post-processing scripts, the DeepModeling community also needs you. Try to ask questions and communicate on github/gitee and other communication platforms, try to give opinions, and try to fork, commit, pr... Your little by little contribution will make the DeepModeling community better and better, and the DeepModeling community will be very grateful for such contributions.

Even if you are just a bystander, if you support the concept of the DeepModeling community, your recognition and dissemination will also be a great encouragement and support for the DeepModeling community.

Final remarks

Despite the tremendous advances in AI and computing power, the scientific computing community is largely embedded in an old-fashioned culture. Many of the most important tasks rely on legacy codes. The core algorithms used in many commercial software have been outdated. The self-sufficient style of work is similar to that of the agricultural ages
resulting in poor efficiency. It is only in recent years that some promising open-source communities have emerged. However, these communities are often aimed at specific tools for specific scales, and are often maintained by specific academic research groups. They face serious challenges in terms of continuous development and improved user experience.

The DeepModeling project promises to change all that.

The combination of machine learning and physical modeling calls for a new paradigm, the open-source community paradigm. Such a paradigm has long been embraced in the computer and electronics industry, with Linux and Andriod being the very well-known examples. In this sense, what the DeepModeling project does is to borrow these ideas and use them for scientific computing. For people in computational science and engineering, efficient and reusable modeling tools that can be continuously improved will free researchers from the plight of no model or with only ad hoc models. For those who work on machine learning, the world of physical models will provide a relatively new and surely vast playground. Working together as an open-source community will make our work more productive, up to date, reliable, and transparent. The spirit of close collaboration, of respect and building on each other’s work will surely inspire more and more people to join the cause of advancing computing for the benefit of the human society. This is an exciting opportunity. This is the future of scientific computing!

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\ No newline at end of file +The DeepModeling Manifesto | DeepModeling

DeepModeling

Define the future of scientific computing together

The DeepModeling Manifesto

Posted on Word count in article: 1.3k Reading time ≈ 5 mins.

The integration of machine learning and physical modeling is changing the paradigm of scientific research. Those who hope to extend the frontier of science and solve challenging practical problems through computational modeling are coming together in new ways never seen before. This calls for a new infrastructure--new platforms for collaboration, new coding
frameworks, new data processing schemes, and new ways of using the computing power. It also calls for a new culture—the culture of working together closely for the benefit of all, of free exchange and sharing of knowledge and tools, of respect and appreciation of each other's work, and of the pursuit of harmony among diversity.

The DeepModeling community is a community of such a group of people.

What is DeepModeling?

The two most important applications of computing are machine learning and physical modeling. The former is an effective tool for analyzing complex data; the latter is a scientific description of the physical world. The vitality boosted by the effective integration of the two is changing all aspects of scientific research. DeepModeling will ultimately be a set of methodologies and tools that combine machine learning, physical modeling, and cutting-edge computational platforms. People who are attracted by the DeepModeling community are attracted by its open, inclusive environment, as well as its dedication to the cause of advancing scientific computing worldwide.

Why choose open source?

There are different interpretations of the term "open source". The consensus among the DeepModeling community is that open source is a collaborative software development platform based on the spirit of openness and sharing. Open source is a familiar concept for people in the fields of machine learning and computer science, but it is not yet popular in the field of scientific computing. What we advocate is that an algorithm or software should not be judged by the reputation of the journal in which it is published, but by its ability to solve real world problems and its actual contribution to science. The sustainable development of a software requires continuous investment in manpower. It should undergo incremental improvement, and it should be put to the test of solving real-world problems in an open environment. This is often difficult to achieve by individuals or individual groups. The open-source community provides better solutions.

The history of the DeepModeling community

The "DeepModeling Community" started with the initiation of the "deepmd-kit" project. “deepmd-kit" is a software tool that combines machine learning and molecular dynamics, which helps to overcome a long-standing difficulty in the field of molecular dynamics, namely the dilemma of having to choose between efficiency and accuracy. The name "DeepModeling" was proposed by early developers of the deepmd-kit project, with the intention of using deep learning tools to solve the curse of dimensionality problem in multi-scale modeling. DeepModeling has therefore become the name of the GitHub organization (https://github.com/deepmodeling) which manages the original deepmd-kit project. After the development of deepmd-kit, the DeepModeling community has successively initiated projects such as dpdata, dp-gen, and dpdispatcher, and extended the modeling scale to electronic structure level through projects such as deepks-kit and ABACUS. These projects have brought together people from all over the world working on molecular simulations.

The short-term plan and long-term vision of the DeepModeling community

In the short term, developers in the DeepModeling community will focus on atomic-scale simulation methods and tools. This includes solving the many-body Schrödinger equation, electronic structure calculation, molecular dynamics simulation, and coarse-grained molecular dynamics simulation. This also includes tasks such as data generation, model training, high-performance optimization, etc. In addition, it includes different workflows and management tools, as well as computing power scheduling tools for different systems, different scenarios, and different purposes.

It should be pointed out that the combination of physical modeling and machine learning often fundamentally changes the implementation logic of a piece of software. Therefore, the new infrastructure will not be settled once and for all, but will be gradually improved through an iterative process and upgrades from time to time.

In the long run, the DeepModeling community is committed to combining physical models at all scales with machine learning methods, using the most cutting-edge computing platforms to solve the most challenging scientific and technological problems faced by the human society.

How can you contribute?

If you want to contribute to an existing project in the DeepModeling community, please just do so or contact
the corresponding developer directly; if you want to open a new project in the DeepModeling community, or if you want the DeepModeling community to help develop your project, just contact contact@deepmodeling.org.

If you are a programmer who loves science and is attracted by the future scientific computing platform built by the DeepModeling community, you can contribute not only through new algorithms, but also code development specifications, document writing specifications, community databases, task scheduling, workflow management and other tools. In addition, you can contribute to code architecture design and high-performance optimization tasks in the DeepModeling community. People in the field of scientific computing will greatly appreciate your expertise and contribution.

If you are a hardcore developer familiar with topics such as electronic structure calculations, molecular dynamics, and finite element methods, the DeepModeling community will be your place to showcase your talents. The addition of machine learning components requires us to rethink about architecture design, each specific implementation for the tasks mentioned above and high-performance optimization. You will become important bridges that connect other developers, contributors, and users in different areas.

If you have only used some basic scientific software and have worked on some post-processing scripts, the DeepModeling community also needs you. Try to ask questions and communicate on github/gitee and other communication platforms, try to give opinions, and try to fork, commit, pr... Your little by little contribution will make the DeepModeling community better and better, and the DeepModeling community will be very grateful for such contributions.

Even if you are just a bystander, if you support the concept of the DeepModeling community, your recognition and dissemination will also be a great encouragement and support for the DeepModeling community.

Final remarks

Despite the tremendous advances in AI and computing power, the scientific computing community is largely embedded in an old-fashioned culture. Many of the most important tasks rely on legacy codes. The core algorithms used in many commercial software have been outdated. The self-sufficient style of work is similar to that of the agricultural ages
resulting in poor efficiency. It is only in recent years that some promising open-source communities have emerged. However, these communities are often aimed at specific tools for specific scales, and are often maintained by specific academic research groups. They face serious challenges in terms of continuous development and improved user experience.

The DeepModeling project promises to change all that.

The combination of machine learning and physical modeling calls for a new paradigm, the open-source community paradigm. Such a paradigm has long been embraced in the computer and electronics industry, with Linux and Andriod being the very well-known examples. In this sense, what the DeepModeling project does is to borrow these ideas and use them for scientific computing. For people in computational science and engineering, efficient and reusable modeling tools that can be continuously improved will free researchers from the plight of no model or with only ad hoc models. For those who work on machine learning, the world of physical models will provide a relatively new and surely vast playground. Working together as an open-source community will make our work more productive, up to date, reliable, and transparent. The spirit of close collaboration, of respect and building on each other’s work will surely inspire more and more people to join the cause of advancing computing for the benefit of the human society. This is an exciting opportunity. This is the future of scientific computing!

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\ No newline at end of file diff --git a/openlam/index.html b/openlam/index.html new file mode 100644 index 0000000..bab4b1d --- /dev/null +++ b/openlam/index.html @@ -0,0 +1 @@ +The OpenLAM Initiative | DeepModeling

DeepModeling

Define the future of scientific computing together

The OpenLAM Initiative

Posted on Word count in article: 625 Reading time ≈ 2 mins.

Peter Thiel once said, "We wanted flying cars, instead we got 140 characters (Twitter)." Over the past decade, we have made great strides at the bit level (internet), but progress at the atomic level (cutting-edge technology) has been relatively slow.

The accumulation of linguistic data has propelled the development of machine learning and ultimately led to the emergence of Large Language Models (LLMs). With the push from AI, progress at the atomic level is also accelerating. Methods like Deep Potential, by learning quantum mechanical data, have increased the space-time scale of microscopic simulations by several orders of magnitude and have made significant progress in fields like drug design, material design, and chemical engineering.

The accumulation of quantum mechanical data is gradually covering the entire periodic table, and the Deep Potential team has also begun the practice of the DPA pre-training model. Analogous to the progress of LLMs, we are on the eve of the emergence of a general Large Atom Model (LAM). At the same time, we believe that open-source and openness will play an increasingly important role in the development of LAM.

Against this backdrop, the core developer team of Deep Potential is launching the OpenLAM Initiative to the community. This plan is still in the draft stage and is set to officially start on January 1, 2024. We warmly and openly welcome opinions and support from all parties.

The slogan for OpenLAM is "Conquer the Periodic Table!" We hope to provide a new infrastructure for microscale scientific research and drive the transformation of microscale industrial design in fields such as materials, energy, and biopharmaceuticals by establishing an open-source ecosystem around large microscale models. Relevant models, data, and workflows will be consolidated around the AIS Square; related software development will take place in the DeepModeling open-source community. At the same time, we welcome open interaction from different communities in model development, data sharing, evaluation, and testing.

OpenLAM's goals for the next three years are: In 2024, to effectively cover the periodic table with first-principles data and achieve a universal property learning capability; in 2025, to combine large-scale experimental characterization data and literature data to achieve a universal cross-modal capability; and in 2026, to realize a target-oriented atomic scale universal generation and planning capability. Ultimately, within 5-10 years, we aim to achieve "Large Atom Embodied Intelligence" for atomic-scale intelligent scientific discovery and synthetic design.

OpenLAM's specific plans for 2024 include:

  • Model Update and Evaluation Report Release:

    • Starting from January 1, 2024, driven by the Deep Potential team, with participation from all LAM developers welcomed.
    • Every three months, a major model version update will take place, with updates that may include model architecture, related data, training strategies, and evaluation test criteria.

  • AIS Cup Competition:

    • Initiated by the Deep Potential team and supported by the Bohrium Cloud Platform, starting in March 2024 and concluding at the end of the year;
    • The goal is to promote the creation of a benchmarking system focused on several application-oriented metrics.

  • Domain Data Contribution:

    • Seeking collaboration with domain developers to establish "LAM-ready" datasets for pre-training and evaluation.
    • Domain datasets for iterative training of the latest models will be updated every three months.

  • Domain Application and Evaluation Workflow Contribution:

    • The domain application and evaluation workflows will be updated and released every three months.

  • Education and Training:

    • Planning a series of educational and training events aimed at LAM developers, domain developers, and users to encourage advancement in the field.

  • How to Contact Us:

    • Direct discussions are encouraged in the DeepModeling community.
    • For more complex inquiries, please contact the project lead, Han Wang (王涵, wang_han@iapcm.ac.cn), Linfeng Zhang (张林峰, zhanglf@aisi.ac.cn), for the new future of Science!
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\ No newline at end of file diff --git a/papers/deepmd-kit/index.html b/papers/deepmd-kit/index.html index 0047172..f74f2eb 100644 --- a/papers/deepmd-kit/index.html +++ b/papers/deepmd-kit/index.html @@ -1 +1 @@ -Publications driven by DeePMD-kit | DeepModeling

DeepModeling

Define the future of scientific computing together

Publications driven by DeePMD-kit

The following publications have used the DeePMD-kit software. Publications that only mentioned the DeePMD-kit will not be included below.

We encourage explicitly mentioning DeePMD-kit with proper citations in your publications, so we can more easily find and list these publications.

Last update date: Nov 28, 2023

2024

Ultrafast switching dynamics of the ferroelectric order in stacking- engineered ferroelectrics

Ri He, Bingwen Zhang, Hua Wang, Lei Li, Ping Tang, Gerrit Bauer, Zhicheng Zhong
Acta Materialia, 2024, 262, 119416.
DOI: 10.1016/j.actamat.2023.119416

Unraveling pyrolysis mechanisms of lignin dimer model compounds: Neural network-based molecular dynamics simulation investigations

Zhe Shang, Hui Li
Fuel, 2024, 357, 129909.
DOI: 10.1016/j.fuel.2023.129909

Mobile dislocation mediated Hall-Petch and inverse Hall-Petch behaviors in nanocrystalline Al-doped boron carbide

Jun Li, Kun Luo, Qi An
Journal of the European Ceramic Society, 2024, 44, 659–667.
DOI: 10.1016/j.jeurceramsoc.2023.09.079

2023

Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric KNbO3 perovskite

Hao-Cheng Thong, XiaoYang Wang, Jian Han, Linfeng Zhang, Bei Li, Ke Wang, Ben Xu
Phys. Rev. B, 2023, 107, 14101.
DOI: 10.1103/PhysRevB.107.014101

Li ion diffusion behavior of Li3OCl solid-state electrolytes with different defect structures: insights from the deep potential model

Zhou Zhang, Zhongyun Ma, Yong Pei
Phys. Chem. Chem. Phys., 2023, 25, 13297–13307.
DOI: 10.1039/d2cp06073f

A deep learning-based potential developed for calcium silicate hydrates with both high accuracy and efficiency

Weihuan Li, Yang Zhou, Li Ding, Pengfei Lv, Yifan Su, Rui Wang, Changwen Miao
Journal of Sustainable Cement-Based Materials, 2023, 12, 1335–1346.
DOI: 10.1080/21650373.2023.2219251

Tuning the lattice thermal conductivity of Sb2Te3 by Cr doping: a deep potential molecular dynamics study

Pan Zhang, Wenkai Liao, Ziyang Zhu, Mi Qin, Zhenhua Zhang, Dan Jin, Yong Liu, Ziyu Wang, Zhihong Lu, Rui Xiong
Phys. Chem. Chem. Phys., 2023, 25, 15422–15432.
DOI: 10.1039/d3cp00999h

A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten

Chang-Jie Ding, Ya-Wei Lei, Xiao-Yang Wang, Xiao-Lin Li, Xiang-Yan Li, Yan-Ge Zhang, Yi-Chun Xu, Chang-Song Liu, Xue-Bang Wu
Tungsten, 2023.
DOI: 10.1007/s42864-023-00230-4

Deep-learning potentials for proton transport in double-sided graphanol

Siddarth K. Achar, Leonardo Bernasconi, Juan J. Alvarez, J. Karl Johnson
Journal of Materials Research, 2023.
DOI: 10.1557/s43578-023-01141-3

A deep potential molecular dynamics study on the ionic structure and transport properties of NaCl-CaCl2 molten salt

Gegentana, Liu Cui, Leping Zhou, Xiaoze Du
Ionics, 2023, 1–11.

A first-principles machine-learning force field for heterogeneous ice nucleation on microcline feldspar

Pablo M Piaggi, Annabella Selloni, Athanassios Z Panagiotopoulos, Roberto Car, Pablo G Debenedetti
Faraday Discuss., 2023.
DOI: 10.1039/d3fd00100h

Accelerating materials discovery using integrated deep machine learning approaches

Weiyi Xia, Ling Tang, Huaijun Sun, Chao Zhang, Kai-Ming Ho, Gayatri Viswanathan, Kirill Kovnir, Cai-Zhuang Wang
J. Mater. Chem. A, 2023.
DOI: 10.1039/d3ta03771a

Speciation of La3+-Cl- Complexes in Hydrothermal Fluids from Deep Potential Molecular Dynamics

Wei Zhang, Li Zhou, Tinggui Yan, Mohan Chen
J. Phys. Chem. B, 2023, 127, 8926–8937.
DOI: 10.1021/acs.jpcb.3c05428

Neural Network Water Model Based on the MB-Pol Many-Body Potential

Maria Carolina Muniz, Roberto Car, Athanassios Z Panagiotopoulos
J. Phys. Chem. B, 2023, 127, 9165–9171.
DOI: 10.1021/acs.jpcb.3c04629

Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials

Nikhil V S Avula, Michael L Klein, Sundaram Balasubramanian
J. Phys. Chem. Lett., 2023, 14, 9500–9507.
DOI: 10.1021/acs.jpclett.3c02112

Insights into the local structure evolution and thermophysical properties of NaCl-KCl-MgCl2\texte ndashLaCl3 melt driven by machine learning

Jia Zhao, Taixi Feng, Guimin Lu, Jianguo Yu
J. Mater. Chem. A, 2023, 11, 23999–24012.
DOI: 10.1039/d3ta03434h

Constrained Hybrid Monte Carlo Sampling Made Simple for Chemical Reaction Simulations

Bin Jin, Taiping Hu, Kuang Yu, Shenzhen Xu
J. Chem. Theory Comput., 2023, 19, 7343–7357.
DOI: 10.1021/acs.jctc.3c00571

Water dissociation at the water-rutile TiO 2 (110) interface from ab~initio-based deep neural network simulations

Bo Wen, Marcos F Calegari Andrade, Li-Min Liu, Annabella Selloni
Proc. Natl. Acad. Sci. U. S. A., 2023, 120, e2212250120.
DOI: 10.1073/pnas.2212250120

Development and Validation of Versatile Deep Atomistic Potentials for Metal Oxides

Pandu Wisesa, Christopher M Andolina, Wissam A Saidi
J. Phys. Chem. Lett., 2023, 14, 468–475.
DOI: 10.1021/acs.jpclett.2c03445

Machine learning assisted investigation of the barocaloric performance in ammonium iodide

Xiong Xu, Fangbiao Li, Chang Niu, Min Li, Hui Wang
2023, 122.
DOI: 10.1063/5.0131696

Accessing the thermal conductivities of Sb2Te3 and Bi2Te3/Sb2Te3 superlattices by molecular dynamics simulations with a deep neural network potential

Pan Zhang, Mi Qin, Zhenhua Zhang, Dan Jin, Yong Liu, Ziyu Wang, Zhihong Lu, Jing Shi, Rui Xiong
Phys. Chem. Chem. Phys., 2023, 25, 6164–6174.
DOI: 10.1039/d2cp05590b

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials

Tyler G Sours, Ambarish R Kulkarni
J. Phys. Chem. C. Nanomater. Interfaces, 2023, 127, 1455–1463.
DOI: 10.1021/acs.jpcc.2c08429

Melting of MgSiO3 determined by machine learning potentials

Jie Deng, Haiyang Niu, Junwei Hu, Mingyi Chen, Lars Stixrude
Phys. Rev. B, 2023, 107, 64103.
DOI: 10.1103/PhysRevB.107.064103

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

Songyuan Yao, Richard Van, Xiaoliang Pan, Ji Hwan Park, Yuezhi Mao, Jingzhi Pu, Ye Mei, Yihan Shao
RSC Adv., 2023, 13, 4565–4577.
DOI: 10.1039/d2ra08180f

Large-scale atomistic simulation of dislocation core structure in face-centered cubic metal with Deep Potential method

Fenglin Deng, Hongyu Wu, Ri He, Peijun Yang, Zhicheng Zhong
Computational Materials Science, 2023, 218, 111941.
DOI: 10.1016/j.commatsci.2022.111941

Melting conditions and entropies of superionic water ice: Free-energy calculations based on hybrid solid/liquid reference systems

Vitor Fidalgo C\^andido, Filipe Matusalem, Maurice de Koning
J. Chem. Phys., 2023, 158, 64502.
DOI: 10.1063/5.0138987

Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water

M'arcio S Gomes-Filho, Alberto Torres, Alexandre Reily Rocha, Luana S Pedroza
J. Phys. Chem. B, 2023, 127, 1422–1428.
DOI: 10.1021/acs.jpcb.2c09059

Thermal transport across copper-water interfaces according to deep potential molecular dynamics

Zhiqiang Li, Xiaoyu Tan, Zhiwei Fu, Linhua Liu, Jia-Yue Yang
Phys. Chem. Chem. Phys., 2023, 25, 6746–6756.
DOI: 10.1039/d2cp05530a

Quasiclassical Trajectory Simulation as a Protocol to Build Locally Accurate Machine Learning Potentials

Jintu Zhang, Haotian Zhang, Zhixin Qin, Yu Kang, Xin Hong, Tingjun Hou
J. Chem. Inf. Model., 2023, 63, 1133–1142.
DOI: 10.1021/acs.jcim.2c01497

QD$\pi$: A Quantum Deep Potential Interaction Model for Drug Discovery

Jinzhe Zeng, Yujun Tao, Timothy J Giese, Darrin M York
J. Chem. Theory Comput., 2023, 19, 1261–1275.
DOI: 10.1021/acs.jctc.2c01172

A deep learning approach to predict thermophysical properties of metastable liquid Ti-Ni-Cr-Al alloy

R. L. Xiao, Q. Wang, J. Y. Qin, J. F. Zhao, Y. Ruan, H. P. Wang, H. Li, B. Wei
2023, 133.
DOI: 10.1063/5.0138001

A \textquotedblleftshort blanket\textquotedblright dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?

Yaoguang Zhai, Alessandro Caruso, Sigbj\orn L\oland Bore, Zhishang Luo, Francesco Paesani
J. Chem. Phys., 2023, 158, 84111.
DOI: 10.1063/5.0142843

Structure and solidification of the (Fe0.75B0.15Si0.1)100-xTax (x=0-2) melts: Experiment and machine learning

I.V. Sterkhova, L.V. Kamaeva, V.I. Lad'yanov, N.M. Chtchelkatchev
Journal of Physics and Chemistry of Solids, 2023, 174, 111143.
DOI: 10.1016/j.jpcs.2022.111143

Unravelling the dissolution dynamics of silicate minerals by deep learning molecular dynamics simulation: A case of dicalcium silicate

Yunjian Li, Hui Pan, Zongjin Li
Cement and Concrete Research, 2023, 165, 107092.
DOI: 10.1016/j.cemconres.2023.107092

Profiling the off-center atomic displacements in CuCl at finite temperatures with a deep-learning potential

Zhi-Hao Wang, Xuan-Yan Chen, Zhen Zhang, Xie Zhang, Su- Huai Wei
Phys. Rev. Materials, 2023, 7, 34601.
DOI: 10.1103/PhysRevMaterials.7.034601

Liquid-Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems

I. A. Balyakin, R. E. Ryltsev, N. M. Chtchelkatchev
Jetp Lett., 2023, 117, 370–376.
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Suppression of Rayleigh Scattering in Silica Glass by Codoping Boron and Fluorine: Molecular Dynamics Simulations with Force-Matching and Neural Network Potentials

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2021

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2D Heterostructure of Amorphous CoFeB Coating Black Phosphorus Nanosheets with Optimal Oxygen Intermediate Absorption for Improved Electrocatalytic Water Oxidation

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Accurate force field of two-dimensional ferroelectrics from deep learning

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Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator

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Energy Fuels, 2021, 35, 762–769.
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Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution

Jinzhe Zeng, Timothy J Giese, Şölen Ekesan, Darrin M York
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Mirza Galib, David T Limmer
Science, 2021, 371, 921–925.
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86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

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Computer Physics Communications, 2021, 259, 107624.
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Insights from Computational Studies on the Anisotropic Volume Change of LixNiO2 at High States of Charge (x < 0.25)

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Thermodynamic and Transport Properties of LiF and FLiBe Molten Salts with Deep Learning Potentials

Alejandro Rodriguez, Stephen Lam, Ming Hu
ACS Appl. Mater. Interfaces, 2021, 13, 55367–55379.
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Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

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Experimental observation of localized interfacial phonon modes

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Artificial intelligence model for efficient simulation of monatomic phase change material antimony

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Molecular dynamics simulation of metallic Al-Ce liquids using a neural network machine learning interatomic potential

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Choosing the right molecular machine learning potential

Max Pinheiro Jr, Fuchun Ge, Nicolas Ferr'e, Pavlo O Dral, Mario Barbatti
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Atomic structure of liquid refractory Nb5Si3 intermetallic compound alloy based upon deep neural network potential

Q. Wang, B. Zhai, H. P. Wang, B. Wei
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Azo(xy) vs Aniline Selectivity in Catalytic Nitroarene Reduction by Intermetallics: Experiments and Simulations

Carena L. Daniels, Da-Jiang Liu, Marquix A. S. Adamson, Megan Knobeloch, Javier Vela
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Resolving the Structural Debate for the Hydrated Excess Proton in Water

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Deep-learning potential method to simulate shear viscosity of liquid aluminum at high temperature and high pressure by molecular dynamics

Yuqing Cheng, Han Wang, Shuaichuang Wang, Xingyu Gao, Qiong Li, Jun Fang, Hongzhou Song, Weidong Chu, Gongmu Zhang, Haifeng Song, Haifeng Liu
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Gold Segregation Improves Electrocatalytic Activity of Icosahedron Au@Pt Nanocluster: Insights from Machine Learning

Dingming Chen, Zhuangzhuang Lai, Jiawei Zhang, Jianfu Chen, Peijun Hu, Haifeng Wang
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Condensed Phase Water Molecular Multipole Moments from Deep Neural Network Models Trained on Ab Initio Simulation Data

Yu Shi, Carrie C Doyle, Thomas L Beck
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Learning intermolecular forces at liquid-vapor interfaces

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Modeling Liquid Water by Climbing up Jacob\textquoterights Ladder in Density Functional Theory Facilitated by Using Deep Neural Network Potentials

Chunyi Zhang, Fujie Tang, Mohan Chen, Jianhang Xu, Linfeng Zhang, Diana Y Qiu, John P Perdew, Michael L Klein, Xifan Wu
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Deep Density: Circumventing the Kohn-Sham equations via symmetry preserving neural networks

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Journal of Computational Physics, 2021, 443, 110523.
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First-principles materials simulation and design for alkali and alkaline metal ion batteries accelerated by machine learning

Lujie Jin, Yujin Ji, Hongshuai Wang, Lifeng Ding, Youyong Li
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Local structure elucidation and properties prediction on KCl-CaCl2 molten salt: A deep potential molecular dynamics study

Min Bu, Wenshuo Liang, Guimin Lu, Jianguo Yu
Solar Energy Materials and Solar Cells, 2021, 232, 111346.
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Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

Alberto Torres, Luana S Pedroza, Marivi Fernandez-Serra, Alexandre R Rocha
J. Phys. Chem. B, 2021, 125, 10772–10778.
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Thermal Conductivity of Silicate Liquid Determined by Machine Learning Potentials

Jie Deng, Lars Stixrude
Geophys Res Lett, 2021, 48, e2021GL093806.
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Ab initio validation on the connection between atomistic and hydrodynamic description to unravel the ion dynamics of warm dense matter

Qiyu Zeng, Xiaoxiang Yu, Yunpeng Yao, Tianyu Gao, Bo Chen, Shen Zhang, Dongdong Kang, Han Wang, Jiayu Dai
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Liquid-Liquid Critical Point in Phosphorus

Manyi Yang, Tarak Karmakar, Michele Parrinello
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Robust, Multi-Length-Scale, Machine Learning Potential for Ag–Au Bimetallic Alloys from Clusters to Bulk Materials

Christopher M. Andolina, Marta Bon, Daniele Passerone, Wissam A. Saidi
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Improved Al-Mg alloy surface segregation predictions with a machine learning atomistic potential

Md Sabbir Akhanda, S Emad Rezaei, Keivan Esfarjani, Sergiy Krylyuk, Albert V Davydov, Mona Zebarjadi
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Anomalous Behavior of Viscosity and Electrical Conductivity of MgSiO 3 Melt at Mantle Conditions

Haiyang Luo, Bijaya B. Karki, Dipta B. Ghosh, Huiming Bao
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Deep neural network potentials for diffusional lithium isotope fractionation in silicate melts

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Geochimica et Cosmochimica Acta, 2021, 303, 38–50.
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Phase Diagram of a Deep Potential Water Model

Linfeng Zhang, Han Wang, Roberto Car, Weinan E
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Theoretical prediction on the local structure and transport properties of molten alkali chlorides by deep potentials

Wenshuo Liang, Guimin Lu, Jianguo Yu
Journal of Materials Science & Technology, 2021, 75, 78-85.
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The thermoelectric performance of new structure SnSe studied by quotient graph and deep learning potential

D. Guo, C. Li, K. Li, B. Shao, D. Chen, Y. Ma, J. Sun, X. Cao, W. Zeng, X. Chang
Materials Today Energy, 2021, 20, 100665.
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Phase Equilibrium of Water with Hexagonal and Cubic Ice Using the SCAN Functional

Pablo M Piaggi, Athanassios Z Panagiotopoulos, Pablo G Debenedetti, Roberto Car
J. Chem. Theory Comput., 2021, 17, 3065–3077.
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Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential

Fu-Zhi Dai, Yinjie Sun, Bo Wen, Huimin Xiang, Yanchun Zhou
Journal of Materials Science & Technology, 2021, 72, 8-15.
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Theoretical prediction on the redox potentials of rare-earth ions by deep potentials

Jia Zhao, Wenshuo Liang, Guimin Lu
Ionics, 2021, 27, 2079–2088.
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Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space*

Wanrun Jiang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Chinese Phys. B, 2021, 30, 50706.
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Anharmonic Raman spectra simulation of crystals from deep neural networks

Honghui Shang, Haidi Wang
AIP Advances, 2021, 11, 35105.
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Thermal transport by electrons and ions in warm dense aluminum: A combined density functional theory and deep potential study

Qianrui Liu, Junyi Li, Mohan Chen
Matter and Radiation at Extremes, 2021, 6 (2), 026902.
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Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential

Chao Zhang, Yang Sun, Hai-Di Wang, Feng Zhang, Tong-Qi Wen, Kai-Ming Ho, Cai-Zhuang Wang
J. Phys. Chem. C, 2021, 125 (5), 3127-3133.
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Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations

Pablo M. Piaggi, Roberto Car
Molecular Physics, 2021, 119.
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Static and Dynamic Correlations in Water: Comparison of Classical Ab Initio Molecular Dynamics at Elevated Temperature with Path Integral Simulations at Ambient Temperature

Chenghan Li, Francesco Paesani, Gregory A Voth
J. Chem. Theory Comput., 2022, 18, 2124–2131.
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Molecular dynamics simulations of lanthanum chloride by deep learning potential

Taixi Feng, Jia Zhao, Wenshuo Liang, Guimin Lu
Computational Materials Science, 2021, 111014.
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Diffusional fractionation of helium isotopes in silicate melts

H. Luo, B.B. Karki, D.B. Ghosh, H. Bao
Geochem. Persp. Let., 2021, 19–22.
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Short- and medium-range orders in Al90Tb10 glass and their relation to the structures of competing crystalline phases

L. Tang, Z.J. Yang, T.Q. Wen, K.M. Ho, M.J. Kramer, C.Z. Wang
Acta Materialia, 2021, 204, 116513.
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A DFT accurate machine learning description of molten ZnCl2 and its mixtures: 2. Potential development and properties prediction of ZnCl2-NaCl-KCl ternary salt for CSP

Gechuanqi Pan, Jing Ding, Yunfei Du, Duu-Jong Lee, Yutong Lu
Computational Materials Science, 2021, 187, 110055.
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Deep learning of accurate force field of ferroelectric HfO2

Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Phys. Rev. B, 2021, 103, 24108.
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Machine-Learning-Driven Simulations on Microstructure and Thermophysical Properties of MgCl2-KCl Eutectic

Wenshuo Liang, Guimin Lu, Jianguo Yu
ACS Appl. Mater. Interfaces, 2021, 13, 4034–4042.
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Theoretical study of Na+ transport in the solid-state electrolyte Na3OBr based on deep potential molecular dynamics

Han-Xiao Li, Xu-Yuan Zhou, Yue-Chao Wang, Hong Jiang
Inorg. Chem. Front., 2021, 8, 425–432.
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When do short-range atomistic machine-learning models fall short?

Shuwen Yue, Maria Carolina Muniz, Marcos F Calegari Andrade, Linfeng Zhang, Roberto Car, Athanassios Z Panagiotopoulos
J. Chem. Phys., 2021, 154, 34111.
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2020

Free energy of proton transfer at the water-TiO2 interface from ab initio deep potential molecular dynamics

Marcos F Calegari Andrade, Hsin-Yu Ko, Linfeng Zhang, Roberto Car, Annabella Selloni
Chem. Sci., 2020, 11, 2335–2341.
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Theoretical prediction on thermal and mechanical properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by deep learning potential

Fu-Zhi Dai, Bo Wen, Yinjie Sun, Huimin Xiang, Yanchun Zhou
Journal of Materials Science & Technology, 2020, 43, 168–174.
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A deep neural network interatomic potential for studying thermal conductivity of $\beta$-Ga2O3

Ruiyang Li, Zeyu Liu, Andrew Rohskopf, Kiarash Gordiz, Asegun Henry, Eungkyu Lee, Tengfei Luo
Appl. Phys. Lett., 2020, 117, 152102.
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Structure and dynamics of warm dense aluminum: a molecular dynamics study with density functional theory and deep potential

Qianrui Liu, Denghui Lu, Mohan Chen
J. Phys. Condens. Matter, 2020, 32, 144002.
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Ab initio phase diagram and nucleation of gallium

Haiyang Niu, Luigi Bonati, Pablo M Piaggi, Michele Parrinello
Nat. Commun., 2020, 11, 2654.
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Raman spectrum and polarizability of liquid water from deep neural networks

Grace M Sommers, Marcos F Calegari Andrade, Linfeng Zhang, Han Wang, Roberto Car
Phys. Chem. Chem. Phys., 2020, 22, 10592–10602.
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A machine learning based deep potential for seeking the low-lying candidates of Al clusters

P Tuo, X B Ye, B C Pan
J. Chem. Phys., 2020, 152, 114105.
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Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved

Shu Wang, Zhan Ma, Wenxiao Pan
Soft Matter, 2020, 16, 8330–8344.
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Complex reaction processes in combustion unraveled by neural network- based molecular dynamics simulation

Jinzhe Zeng, Liqun Cao, Mingyuan Xu, Tong Zhu, John Z H Zhang
Nat. Commun., 2020, 11, 5713.
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Deep neural network for the dielectric response of insulators

Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, Roberto Car
Phys. Rev. B, 2020, 102, 41121.
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DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E
Computer Physics Communications, 2020, 253, 107206.
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Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional

Jianhang Xu, Chunyi Zhang, Linfeng Zhang, Mohan Chen, Biswajit Santra, Xifan Wu
Phys. Rev. B, 2020, 102, 214113.
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Hydrogen Dynamics in Supercritical Water Probed by Neutron Scattering and Computer Simulations

Carla Andreani, Giovanni Romanelli, Alexandra Parmentier, Roberto Senesi, Alexander I Kolesnikov, Hsin-Yu Ko, Marcos F Calegari Andrade, Roberto Car
J. Phys. Chem. Lett., 2020, 11, 9461–9467.
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A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases

R. Li, E. Lee, T. Luo
Materials Today Physics, 2020, 12, 100181.
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Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy

Zhilong Wang, Yanqiang Han, Jinjin Li, Xiao He
J. Phys. Chem. B, 2020, 124, 3027–3035.
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A Deep-Learning Potential for Crystalline and Amorphous Li-Si Alloys

Nan Xu, Yao Shi, Yi He, Qing Shao
J. Phys. Chem. C, 2020, 124, 16278–16288.
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Development of interatomic potential for Al-Tb alloys using a deep neural network learning method

L Tang, Z J Yang, T Q Wen, K M Ho, M J Kramer, C Z Wang
Phys. Chem. Chem. Phys., 2020, 22, 18467–18479.
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Isotope effects in x-ray absorption spectra of liquid water

Chunyi Zhang, Linfeng Zhang, Jianhang Xu, Fujie Tang, Biswajit Santra, Xifan Wu
Phys. Rev. B, 2020, 102, 115155.
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Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics

Yuzhi Zhang, Chang Gao, Qianrui Liu, Linfeng Zhang, Han Wang, Mohan Chen
Physics of Plasmas, 2020, 27, 122704.
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Molecular Dynamics Simulations of Molten Magnesium Chloride Using Machine-Learning-Based Deep Potential

Wenshuo Liang, Guimin Lu, Jianguo Yu
Adv. Theory Simul., 2020, 3, 2000180.
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Grain boundary strengthening in ZrB2 by segregation of W: Atomistic simulations with deep learning potential

Fu-Zhi Dai, Bo Wen, Huimin Xiang, Yanchun Zhou
Journal of the European Ceramic Society, 2020, 40, 5029–5036.
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Crystal Structure Prediction of Binary Alloys via Deep Potential

Haidi Wang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Front. Chem., 2020, 8, 589795.
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Deep machine learning interatomic potential for liquid silica

I A Balyakin, S V Rempel, R E Ryltsev, A A Rempel
Phys. Rev. E, 2020, 102, 52125.
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Structure of disordered TiO2 phases from ab initio based deep neural network simulations

Marcos F. Calegari Andrade, Annabella Selloni
Phys. Rev. Materials, 2020, 4, 113803.
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Signatures of a liquid-liquid transition in an ab initio deep neural network model for water

Thomas E Gartner 3rd, Linfeng Zhang, Pablo M Piaggi, Roberto Car, Athanassios Z Panagiotopoulos, Pablo G Debenedetti
Proc. Natl. Acad. Sci. U. S. A., 2020, 117, 26040–26046.
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2019

Active learning of uniformly accurate interatomic potentials for materials simulation

Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, Weinan E
Phys. Rev. Materials, 2019, 3, 23804.
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Isotope effects in liquid water via deep potential molecular dynamics

Hsin-Yu Ko, Linfeng Zhang, Biswajit Santra, Han Wang, Weinan E, Robert A. DiStasio Jr, Roberto Car
Molecular Physics, 2019, 117, 3269–3281.
DOI: 10.1080/00268976.2019.1652366

Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds

Tongqi Wen, Cai-Zhuang Wang, M. J. Kramer, Yang Sun, Beilin Ye, Haidi Wang, Xueyuan Liu, Chao Zhang, Feng Zhang, Kai-Ming Ho, Nan Wang
Phys. Rev. B, 2019, 100, 174101.
DOI: 10.1103/PhysRevB.100.174101

Deep learning inter-atomic potential model for accurate irradiation damage simulations

Hao Wang, Xun Guo, Linfeng Zhang, Han Wang, Jianming Xue
Appl. Phys. Lett., 2019, 114, 244101.
DOI: 10.1063/1.5098061

2018

Silicon Liquid Structure and Crystal Nucleation from Ab~Initio Deep Metadynamics

Luigi Bonati, Michele Parrinello
Phys. Rev. Lett., 2018, 121, 265701.
DOI: 10.1103/PhysRevLett.121.265701

Deep Learning for Nonadiabatic Excited-State Dynamics

Wen-Kai Chen, Xiang-Yang Liu, Wei-Hai Fang, Pavlo O Dral, Ganglong Cui
J. Phys. Chem. Lett., 2018, 9, 6702–6708.
DOI: 10.1021/acs.jpclett.8b03026

Adaptive coupling of a deep neural network potential to a classical force field

Linfeng Zhang, Han Wang, Weinan E
J. Chem. Phys., 2018, 149, 154107.
DOI: 10.1063/1.5042714

DeePCG: Constructing coarse-grained models via deep neural networks

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E
J. Chem. Phys., 2018, 149, 34101.
DOI: 10.1063/1.5027645

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Han Wang, Linfeng Zhang, Jiequn Han, Weinan E
Computer Physics Communications, 2018, 228, 178–184.
DOI: 10.1016/j.cpc.2018.03.016

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E
Phys. Rev. Lett., 2018, 120, 143001.
DOI: 10.1103/PhysRevLett.120.143001

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Publications driven by DeePMD-kit

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Last update date: Nov 28, 2023

2024

Ultrafast switching dynamics of the ferroelectric order in stacking- engineered ferroelectrics

Ri He, Bingwen Zhang, Hua Wang, Lei Li, Ping Tang, Gerrit Bauer, Zhicheng Zhong
Acta Materialia, 2024, 262, 119416.
DOI: 10.1016/j.actamat.2023.119416

Unraveling pyrolysis mechanisms of lignin dimer model compounds: Neural network-based molecular dynamics simulation investigations

Zhe Shang, Hui Li
Fuel, 2024, 357, 129909.
DOI: 10.1016/j.fuel.2023.129909

Mobile dislocation mediated Hall-Petch and inverse Hall-Petch behaviors in nanocrystalline Al-doped boron carbide

Jun Li, Kun Luo, Qi An
Journal of the European Ceramic Society, 2024, 44, 659–667.
DOI: 10.1016/j.jeurceramsoc.2023.09.079

2023

Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric KNbO3 perovskite

Hao-Cheng Thong, XiaoYang Wang, Jian Han, Linfeng Zhang, Bei Li, Ke Wang, Ben Xu
Phys. Rev. B, 2023, 107, 14101.
DOI: 10.1103/PhysRevB.107.014101

Li ion diffusion behavior of Li3OCl solid-state electrolytes with different defect structures: insights from the deep potential model

Zhou Zhang, Zhongyun Ma, Yong Pei
Phys. Chem. Chem. Phys., 2023, 25, 13297–13307.
DOI: 10.1039/d2cp06073f

A deep learning-based potential developed for calcium silicate hydrates with both high accuracy and efficiency

Weihuan Li, Yang Zhou, Li Ding, Pengfei Lv, Yifan Su, Rui Wang, Changwen Miao
Journal of Sustainable Cement-Based Materials, 2023, 12, 1335–1346.
DOI: 10.1080/21650373.2023.2219251

Tuning the lattice thermal conductivity of Sb2Te3 by Cr doping: a deep potential molecular dynamics study

Pan Zhang, Wenkai Liao, Ziyang Zhu, Mi Qin, Zhenhua Zhang, Dan Jin, Yong Liu, Ziyu Wang, Zhihong Lu, Rui Xiong
Phys. Chem. Chem. Phys., 2023, 25, 15422–15432.
DOI: 10.1039/d3cp00999h

A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten

Chang-Jie Ding, Ya-Wei Lei, Xiao-Yang Wang, Xiao-Lin Li, Xiang-Yan Li, Yan-Ge Zhang, Yi-Chun Xu, Chang-Song Liu, Xue-Bang Wu
Tungsten, 2023.
DOI: 10.1007/s42864-023-00230-4

Deep-learning potentials for proton transport in double-sided graphanol

Siddarth K. Achar, Leonardo Bernasconi, Juan J. Alvarez, J. Karl Johnson
Journal of Materials Research, 2023.
DOI: 10.1557/s43578-023-01141-3

A deep potential molecular dynamics study on the ionic structure and transport properties of NaCl-CaCl2 molten salt

Gegentana, Liu Cui, Leping Zhou, Xiaoze Du
Ionics, 2023, 1–11.

A first-principles machine-learning force field for heterogeneous ice nucleation on microcline feldspar

Pablo M Piaggi, Annabella Selloni, Athanassios Z Panagiotopoulos, Roberto Car, Pablo G Debenedetti
Faraday Discuss., 2023.
DOI: 10.1039/d3fd00100h

Accelerating materials discovery using integrated deep machine learning approaches

Weiyi Xia, Ling Tang, Huaijun Sun, Chao Zhang, Kai-Ming Ho, Gayatri Viswanathan, Kirill Kovnir, Cai-Zhuang Wang
J. Mater. Chem. A, 2023.
DOI: 10.1039/d3ta03771a

Speciation of La3+-Cl- Complexes in Hydrothermal Fluids from Deep Potential Molecular Dynamics

Wei Zhang, Li Zhou, Tinggui Yan, Mohan Chen
J. Phys. Chem. B, 2023, 127, 8926–8937.
DOI: 10.1021/acs.jpcb.3c05428

Neural Network Water Model Based on the MB-Pol Many-Body Potential

Maria Carolina Muniz, Roberto Car, Athanassios Z Panagiotopoulos
J. Phys. Chem. B, 2023, 127, 9165–9171.
DOI: 10.1021/acs.jpcb.3c04629

Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials

Nikhil V S Avula, Michael L Klein, Sundaram Balasubramanian
J. Phys. Chem. Lett., 2023, 14, 9500–9507.
DOI: 10.1021/acs.jpclett.3c02112

Insights into the local structure evolution and thermophysical properties of NaCl-KCl-MgCl2\texte ndashLaCl3 melt driven by machine learning

Jia Zhao, Taixi Feng, Guimin Lu, Jianguo Yu
J. Mater. Chem. A, 2023, 11, 23999–24012.
DOI: 10.1039/d3ta03434h

Constrained Hybrid Monte Carlo Sampling Made Simple for Chemical Reaction Simulations

Bin Jin, Taiping Hu, Kuang Yu, Shenzhen Xu
J. Chem. Theory Comput., 2023, 19, 7343–7357.
DOI: 10.1021/acs.jctc.3c00571

Water dissociation at the water-rutile TiO 2 (110) interface from ab~initio-based deep neural network simulations

Bo Wen, Marcos F Calegari Andrade, Li-Min Liu, Annabella Selloni
Proc. Natl. Acad. Sci. U. S. A., 2023, 120, e2212250120.
DOI: 10.1073/pnas.2212250120

Development and Validation of Versatile Deep Atomistic Potentials for Metal Oxides

Pandu Wisesa, Christopher M Andolina, Wissam A Saidi
J. Phys. Chem. Lett., 2023, 14, 468–475.
DOI: 10.1021/acs.jpclett.2c03445

Machine learning assisted investigation of the barocaloric performance in ammonium iodide

Xiong Xu, Fangbiao Li, Chang Niu, Min Li, Hui Wang
2023, 122.
DOI: 10.1063/5.0131696

Accessing the thermal conductivities of Sb2Te3 and Bi2Te3/Sb2Te3 superlattices by molecular dynamics simulations with a deep neural network potential

Pan Zhang, Mi Qin, Zhenhua Zhang, Dan Jin, Yong Liu, Ziyu Wang, Zhihong Lu, Jing Shi, Rui Xiong
Phys. Chem. Chem. Phys., 2023, 25, 6164–6174.
DOI: 10.1039/d2cp05590b

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials

Tyler G Sours, Ambarish R Kulkarni
J. Phys. Chem. C. Nanomater. Interfaces, 2023, 127, 1455–1463.
DOI: 10.1021/acs.jpcc.2c08429

Melting of MgSiO3 determined by machine learning potentials

Jie Deng, Haiyang Niu, Junwei Hu, Mingyi Chen, Lars Stixrude
Phys. Rev. B, 2023, 107, 64103.
DOI: 10.1103/PhysRevB.107.064103

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

Songyuan Yao, Richard Van, Xiaoliang Pan, Ji Hwan Park, Yuezhi Mao, Jingzhi Pu, Ye Mei, Yihan Shao
RSC Adv., 2023, 13, 4565–4577.
DOI: 10.1039/d2ra08180f

Large-scale atomistic simulation of dislocation core structure in face-centered cubic metal with Deep Potential method

Fenglin Deng, Hongyu Wu, Ri He, Peijun Yang, Zhicheng Zhong
Computational Materials Science, 2023, 218, 111941.
DOI: 10.1016/j.commatsci.2022.111941

Melting conditions and entropies of superionic water ice: Free-energy calculations based on hybrid solid/liquid reference systems

Vitor Fidalgo C\^andido, Filipe Matusalem, Maurice de Koning
J. Chem. Phys., 2023, 158, 64502.
DOI: 10.1063/5.0138987

Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water

M'arcio S Gomes-Filho, Alberto Torres, Alexandre Reily Rocha, Luana S Pedroza
J. Phys. Chem. B, 2023, 127, 1422–1428.
DOI: 10.1021/acs.jpcb.2c09059

Thermal transport across copper-water interfaces according to deep potential molecular dynamics

Zhiqiang Li, Xiaoyu Tan, Zhiwei Fu, Linhua Liu, Jia-Yue Yang
Phys. Chem. Chem. Phys., 2023, 25, 6746–6756.
DOI: 10.1039/d2cp05530a

Quasiclassical Trajectory Simulation as a Protocol to Build Locally Accurate Machine Learning Potentials

Jintu Zhang, Haotian Zhang, Zhixin Qin, Yu Kang, Xin Hong, Tingjun Hou
J. Chem. Inf. Model., 2023, 63, 1133–1142.
DOI: 10.1021/acs.jcim.2c01497

QD$\pi$: A Quantum Deep Potential Interaction Model for Drug Discovery

Jinzhe Zeng, Yujun Tao, Timothy J Giese, Darrin M York
J. Chem. Theory Comput., 2023, 19, 1261–1275.
DOI: 10.1021/acs.jctc.2c01172

A deep learning approach to predict thermophysical properties of metastable liquid Ti-Ni-Cr-Al alloy

R. L. Xiao, Q. Wang, J. Y. Qin, J. F. Zhao, Y. Ruan, H. P. Wang, H. Li, B. Wei
2023, 133.
DOI: 10.1063/5.0138001

A \textquotedblleftshort blanket\textquotedblright dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?

Yaoguang Zhai, Alessandro Caruso, Sigbj\orn L\oland Bore, Zhishang Luo, Francesco Paesani
J. Chem. Phys., 2023, 158, 84111.
DOI: 10.1063/5.0142843

Structure and solidification of the (Fe0.75B0.15Si0.1)100-xTax (x=0-2) melts: Experiment and machine learning

I.V. Sterkhova, L.V. Kamaeva, V.I. Lad'yanov, N.M. Chtchelkatchev
Journal of Physics and Chemistry of Solids, 2023, 174, 111143.
DOI: 10.1016/j.jpcs.2022.111143

Unravelling the dissolution dynamics of silicate minerals by deep learning molecular dynamics simulation: A case of dicalcium silicate

Yunjian Li, Hui Pan, Zongjin Li
Cement and Concrete Research, 2023, 165, 107092.
DOI: 10.1016/j.cemconres.2023.107092

Profiling the off-center atomic displacements in CuCl at finite temperatures with a deep-learning potential

Zhi-Hao Wang, Xuan-Yan Chen, Zhen Zhang, Xie Zhang, Su- Huai Wei
Phys. Rev. Materials, 2023, 7, 34601.
DOI: 10.1103/PhysRevMaterials.7.034601

Liquid-Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems

I. A. Balyakin, R. E. Ryltsev, N. M. Chtchelkatchev
Jetp Lett., 2023, 117, 370–376.
DOI: 10.1134/S0021364023600234

Oxygen Vacancy Diffusion in Rutile TiO2: Insight from Deep Neural Network Potential Simulations

Zhihong Wu, Wen-Jin Yin, Bo Wen, Dongwei Ma, Li-Min Liu
J. Phys. Chem. Lett., 2023, 14, 2208–2214.
DOI: 10.1021/acs.jpclett.2c03827

Nanotwinning-induced pseudoplastic deformation in boron carbide under low temperature

Jun Li, Qi An
International Journal of Mechanical Sciences, 2023, 242, 107998.
DOI: 10.1016/j.ijmecsci.2022.107998

Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2\textquoterights Chemisorption and Diffusion in Mg-MOF-74

Bowen Zheng, Felipe Lopes Oliveira, Rodrigo Neumann Barros Ferreira, Mathias Steiner, Hendrik Hamann, Grace X Gu, Binquan Luan
ACS Nano, 2023, 17, 5579–5587.
DOI: 10.1021/acsnano.2c11102

Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states

Jinzhe Zeng, Yujun Tao, Timothy J Giese, Darrin M York
J. Chem. Phys., 2023, 158, 124110.
DOI: 10.1063/5.0139281

Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments

Matteo Cioni, Daniela Polino, Daniele Rapetti, Luca Pesce, Massimo Delle Piane, Giovanni M Pavan
J. Chem. Phys., 2023, 158, 124701.
DOI: 10.1063/5.0139010

Prediction on local structure and properties of LiCl-KCl-AlCl3 ternary molten salt with deep learning potential

Min Bu, Taixi Feng, Guimin Lu
Journal of Molecular Liquids, 2023, 375, 120689.
DOI: 10.1016/j.molliq.2022.120689

Atomic structure, stability, and dissociation of dislocations in cadmium telluride

Jun Li, Kun Luo, Qi An
International Journal of Plasticity, 2023, 163, 103552.
DOI: 10.1016/j.ijplas.2023.103552

The highest melting point material: Searched by Bayesian global optimization with deep potential molecular dynamics

Yinan Wang, Bo Wen, Xingjian Jiao, Ya Li, Lei Chen, Yujin Wang, Fu-Zhi Dai
2023, 12, 803–814.
DOI: 10.26599/JAC.2023.9220721

High-Accuracy Neural Network Interatomic Potential for Silicon Nitride

Hui Xu, Zeyuan Li, Zhaofu Zhang, Sheng Liu, Shengnan Shen, Yuzheng Guo
Nanomaterials (Basel)., 2023, 13, 1352.
DOI: 10.3390/nano13081352

A New Spinel Chloride Solid Electrolyte with High Ionic Conductivity and Stability for Na-Ion Batteries

Jiahui Liu, Shuo Wang, Yoshiyuki Kawazoe, Qiang Sun
ACS Materials Lett., 2023, 5, 1009–1017.
DOI: 10.1021/acsmaterialslett.3c00119

Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects

Th'eo Jaffrelot Inizan, Thomas Pl'e, Olivier Adjoua, Pengyu Ren, Hatice G"okcan, Olexandr Isayev, Louis Lagard\`ere, Jean-Philip Piquemal
Chem. Sci., 2023, 14, 5438–5452.
DOI: 10.1039/d2sc04815a

Structural and Composition Evolution of Palladium Catalyst for CO Oxidation under Steady-State Reaction Conditions

Jiawei Wu, Dingming Chen, Jianfu Chen, Haifeng Wang
J. Phys. Chem. C, 2023, 127, 6262–6270.
DOI: 10.1021/acs.jpcc.2c07877

Monitoring the melting behavior of boron nanoparticles using a neural network potential

Xiaoya Chang, Qingzhao Chu, Dongping Chen
Phys. Chem. Chem. Phys., 2023, 25, 12841–12853.
DOI: 10.1039/d3cp00571b

Microstructure and Thermophysical Property Prediction for Chloride Composite Phase Change Materials: A Deep Potential Molecular Dynamics Study

C. Y. Zhao, Y. B. Tao, Y. He
J. Phys. Chem. C, 2023, 127, 6852–6860.
DOI: 10.1021/acs.jpcc.2c08589

Cooperative diffusion in body-centered cubic iron in Earth and super- Earths\textquoteright inner core conditions

Maitrayee Ghosh, Shuai Zhang, Lianming Hu, S X Hu
J. Phys. Condens. Matter, 2023, 35, 154002.
DOI: 10.1088/1361-648X/acba71

Pressure-Induced Stability of Methane Hydrate from Machine Learning Force Field Simulations

Kun Luo, Yidi Shen, Jun Li, Qi An
J. Phys. Chem. C, 2023, 127, 7071–7077.
DOI: 10.1021/acs.jpcc.2c09121

Unraveling the Dynamic Correlations between Transition Metal Migration and the Oxygen Dimer Formation in the Highly Delithiated LixCoO2 Cathode

Taiping Hu, Fu-Zhi Dai, Guobing Zhou, Xiaoxu Wang, Shenzhen Xu
J. Phys. Chem. Lett., 2023, 14, 3677–3684.
DOI: 10.1021/acs.jpclett.3c00506

Predicting thermodynamic stability of magnesium alloys in machine learning

Xi He, Jinde Liu, Chen Yang, Gang Jiang
Computational Materials Science, 2023, 223, 112111.
DOI: 10.1016/j.commatsci.2023.112111

Hydrogen distribution between the Earth's inner and outer core

Liang Yuan, Gerd Steinle-Neumann
Earth and Planetary Science Letters, 2023, 609, 118084.
DOI: 10.1016/j.epsl.2023.118084

Lattice Thermal Conductivity of Monolayer InSe Calculated by Machine Learning Potential

Jinsen Han, Qiyu Zeng, Ke Chen, Xiaoxiang Yu, Jiayu Dai
Nanomaterials (Basel)., 2023, 13, 1576.
DOI: 10.3390/nano13091576

Modeling the high-pressure solid and liquid phases of tin from deep potentials with ab initio accuracy

Tao Chen, Fengbo Yuan, Jianchuan Liu, Huayun Geng, Linfeng Zhang, Han Wang, Mohan Chen
Phys. Rev. Materials, 2023, 7, 53603.
DOI: 10.1103/PhysRevMaterials.7.053603

Estimation of frequency factors for the calculation of kinetic isotope effects from classical and path integral free energy simulations

Timothy J Giese, Darrin M York
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0147218

A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water

Ignacio Sanchez-Burgos, Maria Carolina Muniz, Jorge R Espinosa, Athanassios Z Panagiotopoulos
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0144500

First-Principles-Based Machine Learning Models for Phase Behavior and Transport Properties of CO2

Reha Mathur, Maria Carolina Muniz, Shuwen Yue, Roberto Car, Athanassios Z Panagiotopoulos
J. Phys. Chem. B, 2023, 127, 4562–4569.
DOI: 10.1021/acs.jpcb.3c00610

Characterizing Structure-Dependent TiS2/Water Interfaces Using Deep-Neural-Network-Assisted Molecular Dynamics

Lesheng Li, Marcos F. Calegari Andrade, Roberto Car, Annabella Selloni, Emily A. Carter
J. Phys. Chem. C, 2023, 127, 9750–9758.
DOI: 10.1021/acs.jpcc.2c08581

An accurate interatomic potential for the TiAlNb ternary alloy developed by deep neural network learning method

Jiajun Lu, Jinkai Wang, Kaiwei Wan, Ying Chen, Hao Wang, Xinghua Shi
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0147720

In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol

Siddarth K Achar, Leonardo Bernasconi, Ruby I DeMaio, Katlyn R Howard, J Karl Johnson
ACS Appl. Mater. Interfaces, 2023, 15, 25873–25883.
DOI: 10.1021/acsami.3c04022

Machine learning insight into h-BN growth on Pt(111) from atomic states

Kangmo Yeo, Sukmin Jeong
Applied Surface Science, 2023, 621, 156893.
DOI: 10.1016/j.apsusc.2023.156893

Molecular dynamics simulations of CaCl2-NaCl molten salt based on the machine learning potentials

Yun Xie, Min Bu, Guiming Zou, Ye Zhang, Guimin Lu
Solar Energy Materials and Solar Cells, 2023, 254, 112275.
DOI: 10.1016/j.solmat.2023.112275

A Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics

Zhuoying Zhao, Ziling Tan, Pinghui Mo, Xiaonan Wang, Dan Zhao, Xin Zhang, Ming Tao, Jie Liu
IEEE Trans. Circuits Syst. I, 2023, 70, 2439–2449.
DOI: 10.1109/TCSI.2023.3255199

Noble gas (He, Ne, and Ar) solubilities in high-pressure silicate melts calculated based on deep-potential modeling

Kai Wang, Xiancai Lu, Xiandong Liu, Kun Yin
Geochimica et Cosmochimica Acta, 2023, 350, 57–68.
DOI: 10.1016/j.gca.2023.03.032

Reversible densification and cooperative atomic movement induced \textquotedblleftcompaction\textquotedblright in vitreous silica: a new sight from deep neural network interatomic potentials

Yongnian Qi, Xiaoguang Guo, Hao Wang, Shuohua Zhang, Ming Li, Ping Zhou, Dongming Guo
J Mater Sci, 2023, 58, 9515–9532.
DOI: 10.1007/s10853-023-08599-w

Molecular dynamics simulation of Fe-Si alloys using a neural network machine learning potential

Huaijun Sun, Chao Zhang, Ling Tang, Renhai Wang, Weiyi Xia, Cai-Zhuang Wang
Phys. Rev. B, 2023, 107, 224301.
DOI: 10.1103/PhysRevB.107.224301

Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles\textemdashTransferability towards Bulk

Marco Fronzi, Roger D Amos, Rika Kobayashi
Nanomaterials (Basel)., 2023, 13, 1832.
DOI: 10.3390/nano13121832

A genome dependence of metastable phase selection on atomic structure for undercooled liquid Nb90Si10 hypoeutectic alloy

Q. Wang, C. H. Zheng, M. X. Li, L. Hu, H. P. Wang, B. Wei
2023, 122.
DOI: 10.1063/5.0152293

TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra

Cristina Caruso, Annalisa Cardellini, Martina Crippa, Daniele Rapetti, Giovanni M Pavan
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0147025

Deciphering the Anomalous Acidic Tendency of Terminal Water at Rutile(110)-Water Interfaces

Yong-Bin Zhuang, Jun Cheng
J. Phys. Chem. C, 2023, 127, 10532–10540.
DOI: 10.1021/acs.jpcc.3c01870

Temperature-dependent microwave dielectric permittivity of gallium oxide: A deep potential molecular dynamics study

Zhiqiang Li, Xinlei Duan, Linhua Liu, Jia-Yue Yang
2023, 133.
DOI: 10.1063/5.0149447

Investigating the Hydroxyl Reorientation in Hydroxyapatite Using Machine Learning Potentials

Jing Wang, Xin Wang, Hua Zhu, Dingguo Xu
J. Phys. Chem. C, 2023, 127, 11369–11377.
DOI: 10.1021/acs.jpcc.3c02426

Probing Confinement Effects on the Infrared Spectra of Water with Deep Potential Molecular Dynamics Simulations

Marcos F Calegari Andrade, Tuan Anh Pham
J. Phys. Chem. Lett., 2023, 14, 5560–5566.
DOI: 10.1021/acs.jpclett.3c01054

A Deep Neural Network Potential to Study the Thermal Conductivity of MnBi2Te4 and Bi2Te3/MnBi2Te4 Superlattice

Ruijin Qu, Yawei Lv, Zhihong Lu
J. Electron. Mater., 2023, 52, 4475–4483.
DOI: 10.1007/s11664-023-10403-z

Molecular insight into the GaP(110)-water interface using machine learning accelerated molecular dynamics

Xue-Ting Fan, Xiao-Jian Wen, Yong-Bin Zhuang, Jun Cheng
Journal of Energy Chemistry, 2023, 82, 239–247.
DOI: 10.1016/j.jechem.2023.03.013

Modelling of dislocations, twins and crack-tips in HCP and BCC Ti

Tongqi Wen, Anwen Liu, Rui Wang, Linfeng Zhang, Jian Han, Han Wang, David J. Srolovitz, Zhaoxuan Wu
International Journal of Plasticity, 2023, 166, 103644.
DOI: 10.1016/j.ijplas.2023.103644

Halide Vacancies Create No Charge Traps on Lead Halide Perovskite Surfaces but Can Generate Deep Traps in the Bulk

Jingyi Ran, Bipeng Wang, Yifan Wu, Dongyu Liu, Carlos Mora Perez, Andrey S Vasenko, Oleg V Prezhdo
J. Phys. Chem. Lett., 2023, 14, 6028–6036.
DOI: 10.1021/acs.jpclett.3c01231

High-Throughput Condensed-Phase Hybrid Density Functional Theory for Large-Scale Finite-Gap Systems: The SeA Approach

Hsin-Yu Ko, Marcos F Calegari Andrade, Zachary M Sparrow, Ju-An Zhang, Robert A DiStasio Jr
J. Chem. Theory Comput., 2023, 19, 4182–4201.
DOI: 10.1021/acs.jctc.2c00827

Microscopic Mechanism of Proton Transfer in Pure Water under Ambient Conditions

Jun Huo, Jianghao Chen, Pei Liu, Benkun Hong, Jian Zhang, Hao Dong, Shuhua Li
J. Chem. Theory Comput., 2023, 19, 4243–4254.
DOI: 10.1021/acs.jctc.3c00244

Revealing Carbon Vacancy Distribution on $\alpha$-MoC1-x Surfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach

Jun-Zhong Xie, Hong Jiang
J. Phys. Chem. C, 2023, 127, 13228–13237.
DOI: 10.1021/acs.jpcc.3c01941

Modeling the aqueous interface of amorphous TiO2 using deep potential molecular dynamics

Zhutian Ding, Annabella Selloni
J. Chem. Phys., 2023, 159.
DOI: 10.1063/5.0157188

Transferability evaluation of the deep potential model for simulating water-graphene confined system

Dongfei Liu, Jianzhong Wu, Diannan Lu
J. Chem. Phys., 2023, 159.
DOI: 10.1063/5.0153196

Structural transformations in single-crystalline AgPd nanoalloys from multiscale deep potential molecular dynamics

Longfei Guo, Tao Jin, Shuang Shan, Quan Tang, Zhen Li, Chongyang Wang, Junpeng Wang, Bowei Pan, Qiao Wang, Fuyi Chen
J. Chem. Phys., 2023, 159.
DOI: 10.1063/5.0158918

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

Martina Crippa, Annalisa Cardellini, Cristina Caruso, Giovanni M Pavan
Proc. Natl. Acad. Sci. U. S. A., 2023, 120, e2300565120.
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Anisotropic Collective Variables with Machine Learning Potential for Ab Initio Crystallization of Complex Ceramics

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Deep neural network potentials for diffusional lithium isotope fractionation in silicate melts

Haiyang Luo, Bijaya B. Karki, Dipta B. Ghosh, Huiming Bao
Geochimica et Cosmochimica Acta, 2021, 303, 38–50.
DOI: 10.1016/j.gca.2021.03.031

Phase Diagram of a Deep Potential Water Model

Linfeng Zhang, Han Wang, Roberto Car, Weinan E
Phys. Rev. Lett., 2021, 126, 236001.
DOI: 10.1103/PhysRevLett.126.236001

Theoretical prediction on the local structure and transport properties of molten alkali chlorides by deep potentials

Wenshuo Liang, Guimin Lu, Jianguo Yu
Journal of Materials Science & Technology, 2021, 75, 78-85.
DOI: 10.1016/j.jmst.2020.09.040

The thermoelectric performance of new structure SnSe studied by quotient graph and deep learning potential

D. Guo, C. Li, K. Li, B. Shao, D. Chen, Y. Ma, J. Sun, X. Cao, W. Zeng, X. Chang
Materials Today Energy, 2021, 20, 100665.
DOI: 10.1016/j.mtener.2021.100665

Phase Equilibrium of Water with Hexagonal and Cubic Ice Using the SCAN Functional

Pablo M Piaggi, Athanassios Z Panagiotopoulos, Pablo G Debenedetti, Roberto Car
J. Chem. Theory Comput., 2021, 17, 3065–3077.
DOI: 10.1021/acs.jctc.1c00041

Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential

Fu-Zhi Dai, Yinjie Sun, Bo Wen, Huimin Xiang, Yanchun Zhou
Journal of Materials Science & Technology, 2021, 72, 8-15.
DOI: 10.1016/j.jmst.2020.07.014

Theoretical prediction on the redox potentials of rare-earth ions by deep potentials

Jia Zhao, Wenshuo Liang, Guimin Lu
Ionics, 2021, 27, 2079–2088.
DOI: 10.1007/s11581-021-03988-0

Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space*

Wanrun Jiang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Chinese Phys. B, 2021, 30, 50706.
DOI: 10.1088/1674-1056/abf134

Anharmonic Raman spectra simulation of crystals from deep neural networks

Honghui Shang, Haidi Wang
AIP Advances, 2021, 11, 35105.
DOI: 10.1063/5.0040190

Thermal transport by electrons and ions in warm dense aluminum: A combined density functional theory and deep potential study

Qianrui Liu, Junyi Li, Mohan Chen
Matter and Radiation at Extremes, 2021, 6 (2), 026902.
DOI: 10.1063/5.0030123

Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential

Chao Zhang, Yang Sun, Hai-Di Wang, Feng Zhang, Tong-Qi Wen, Kai-Ming Ho, Cai-Zhuang Wang
J. Phys. Chem. C, 2021, 125 (5), 3127-3133.
DOI: 10.1021/acs.jpcc.0c08873

Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations

Pablo M. Piaggi, Roberto Car
Molecular Physics, 2021, 119.
DOI: 10.1080/00268976.2021.1916634

Static and Dynamic Correlations in Water: Comparison of Classical Ab Initio Molecular Dynamics at Elevated Temperature with Path Integral Simulations at Ambient Temperature

Chenghan Li, Francesco Paesani, Gregory A Voth
J. Chem. Theory Comput., 2022, 18, 2124–2131.
DOI: 10.1021/acs.jctc.1c01223

Molecular dynamics simulations of lanthanum chloride by deep learning potential

Taixi Feng, Jia Zhao, Wenshuo Liang, Guimin Lu
Computational Materials Science, 2021, 111014.
DOI: 10.1016/j.commatsci.2021.111014

Diffusional fractionation of helium isotopes in silicate melts

H. Luo, B.B. Karki, D.B. Ghosh, H. Bao
Geochem. Persp. Let., 2021, 19–22.
DOI: 10.7185/geochemlet.2128

Short- and medium-range orders in Al90Tb10 glass and their relation to the structures of competing crystalline phases

L. Tang, Z.J. Yang, T.Q. Wen, K.M. Ho, M.J. Kramer, C.Z. Wang
Acta Materialia, 2021, 204, 116513.
DOI: 10.1016/j.actamat.2020.116513

A DFT accurate machine learning description of molten ZnCl2 and its mixtures: 2. Potential development and properties prediction of ZnCl2-NaCl-KCl ternary salt for CSP

Gechuanqi Pan, Jing Ding, Yunfei Du, Duu-Jong Lee, Yutong Lu
Computational Materials Science, 2021, 187, 110055.
DOI: 10.1016/j.commatsci.2020.110055

Deep learning of accurate force field of ferroelectric HfO2

Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Phys. Rev. B, 2021, 103, 24108.
DOI: 10.1103/PhysRevB.103.024108

Machine-Learning-Driven Simulations on Microstructure and Thermophysical Properties of MgCl2-KCl Eutectic

Wenshuo Liang, Guimin Lu, Jianguo Yu
ACS Appl. Mater. Interfaces, 2021, 13, 4034–4042.
DOI: 10.1021/acsami.0c20665

Theoretical study of Na+ transport in the solid-state electrolyte Na3OBr based on deep potential molecular dynamics

Han-Xiao Li, Xu-Yuan Zhou, Yue-Chao Wang, Hong Jiang
Inorg. Chem. Front., 2021, 8, 425–432.
DOI: 10.1039/D0QI00921K

When do short-range atomistic machine-learning models fall short?

Shuwen Yue, Maria Carolina Muniz, Marcos F Calegari Andrade, Linfeng Zhang, Roberto Car, Athanassios Z Panagiotopoulos
J. Chem. Phys., 2021, 154, 34111.
DOI: 10.1063/5.0031215

2020

Free energy of proton transfer at the water-TiO2 interface from ab initio deep potential molecular dynamics

Marcos F Calegari Andrade, Hsin-Yu Ko, Linfeng Zhang, Roberto Car, Annabella Selloni
Chem. Sci., 2020, 11, 2335–2341.
DOI: 10.1039/C9SC05116C

Theoretical prediction on thermal and mechanical properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by deep learning potential

Fu-Zhi Dai, Bo Wen, Yinjie Sun, Huimin Xiang, Yanchun Zhou
Journal of Materials Science & Technology, 2020, 43, 168–174.
DOI: 10.1016/j.jmst.2020.01.005

A deep neural network interatomic potential for studying thermal conductivity of $\beta$-Ga2O3

Ruiyang Li, Zeyu Liu, Andrew Rohskopf, Kiarash Gordiz, Asegun Henry, Eungkyu Lee, Tengfei Luo
Appl. Phys. Lett., 2020, 117, 152102.
DOI: 10.1063/5.0025051

Structure and dynamics of warm dense aluminum: a molecular dynamics study with density functional theory and deep potential

Qianrui Liu, Denghui Lu, Mohan Chen
J. Phys. Condens. Matter, 2020, 32, 144002.
DOI: 10.1088/1361-648X/ab5890

Ab initio phase diagram and nucleation of gallium

Haiyang Niu, Luigi Bonati, Pablo M Piaggi, Michele Parrinello
Nat. Commun., 2020, 11, 2654.
DOI: 10.1038/s41467-020-16372-9

Raman spectrum and polarizability of liquid water from deep neural networks

Grace M Sommers, Marcos F Calegari Andrade, Linfeng Zhang, Han Wang, Roberto Car
Phys. Chem. Chem. Phys., 2020, 22, 10592–10602.
DOI: 10.1039/D0CP01893G

A machine learning based deep potential for seeking the low-lying candidates of Al clusters

P Tuo, X B Ye, B C Pan
J. Chem. Phys., 2020, 152, 114105.
DOI: 10.1063/5.0001491

Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved

Shu Wang, Zhan Ma, Wenxiao Pan
Soft Matter, 2020, 16, 8330–8344.
DOI: 10.1039/D0SM01019G

Complex reaction processes in combustion unraveled by neural network- based molecular dynamics simulation

Jinzhe Zeng, Liqun Cao, Mingyuan Xu, Tong Zhu, John Z H Zhang
Nat. Commun., 2020, 11, 5713.
DOI: 10.1038/s41467-020-19497-z

Deep neural network for the dielectric response of insulators

Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, Roberto Car
Phys. Rev. B, 2020, 102, 41121.
DOI: 10.1103/PhysRevB.102.041121

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E
Computer Physics Communications, 2020, 253, 107206.
DOI: 10.1016/j.cpc.2020.107206

Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional

Jianhang Xu, Chunyi Zhang, Linfeng Zhang, Mohan Chen, Biswajit Santra, Xifan Wu
Phys. Rev. B, 2020, 102, 214113.
DOI: 10.1103/PhysRevB.102.214113

Hydrogen Dynamics in Supercritical Water Probed by Neutron Scattering and Computer Simulations

Carla Andreani, Giovanni Romanelli, Alexandra Parmentier, Roberto Senesi, Alexander I Kolesnikov, Hsin-Yu Ko, Marcos F Calegari Andrade, Roberto Car
J. Phys. Chem. Lett., 2020, 11, 9461–9467.
DOI: 10.1021/acs.jpclett.0c02547

A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases

R. Li, E. Lee, T. Luo
Materials Today Physics, 2020, 12, 100181.
DOI: 10.1016/j.mtphys.2020.100181

Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy

Zhilong Wang, Yanqiang Han, Jinjin Li, Xiao He
J. Phys. Chem. B, 2020, 124, 3027–3035.
DOI: 10.1021/acs.jpcb.0c01370

A Deep-Learning Potential for Crystalline and Amorphous Li-Si Alloys

Nan Xu, Yao Shi, Yi He, Qing Shao
J. Phys. Chem. C, 2020, 124, 16278–16288.
DOI: 10.1021/acs.jpcc.0c03333

Development of interatomic potential for Al-Tb alloys using a deep neural network learning method

L Tang, Z J Yang, T Q Wen, K M Ho, M J Kramer, C Z Wang
Phys. Chem. Chem. Phys., 2020, 22, 18467–18479.
DOI: 10.1039/D0CP01689F

Isotope effects in x-ray absorption spectra of liquid water

Chunyi Zhang, Linfeng Zhang, Jianhang Xu, Fujie Tang, Biswajit Santra, Xifan Wu
Phys. Rev. B, 2020, 102, 115155.
DOI: 10.1103/PhysRevB.102.115155

Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics

Yuzhi Zhang, Chang Gao, Qianrui Liu, Linfeng Zhang, Han Wang, Mohan Chen
Physics of Plasmas, 2020, 27, 122704.
DOI: 10.1063/5.0023265

Molecular Dynamics Simulations of Molten Magnesium Chloride Using Machine-Learning-Based Deep Potential

Wenshuo Liang, Guimin Lu, Jianguo Yu
Adv. Theory Simul., 2020, 3, 2000180.
DOI: 10.1002/adts.202000180

Grain boundary strengthening in ZrB2 by segregation of W: Atomistic simulations with deep learning potential

Fu-Zhi Dai, Bo Wen, Huimin Xiang, Yanchun Zhou
Journal of the European Ceramic Society, 2020, 40, 5029–5036.
DOI: 10.1016/j.jeurceramsoc.2020.06.007

Crystal Structure Prediction of Binary Alloys via Deep Potential

Haidi Wang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Front. Chem., 2020, 8, 589795.
DOI: 10.3389/fchem.2020.589795

Deep machine learning interatomic potential for liquid silica

I A Balyakin, S V Rempel, R E Ryltsev, A A Rempel
Phys. Rev. E, 2020, 102, 52125.
DOI: 10.1103/PhysRevE.102.052125

Structure of disordered TiO2 phases from ab initio based deep neural network simulations

Marcos F. Calegari Andrade, Annabella Selloni
Phys. Rev. Materials, 2020, 4, 113803.
DOI: 10.1103/PhysRevMaterials.4.113803

Signatures of a liquid-liquid transition in an ab initio deep neural network model for water

Thomas E Gartner 3rd, Linfeng Zhang, Pablo M Piaggi, Roberto Car, Athanassios Z Panagiotopoulos, Pablo G Debenedetti
Proc. Natl. Acad. Sci. U. S. A., 2020, 117, 26040–26046.
DOI: 10.1073/pnas.2015440117

2019

Active learning of uniformly accurate interatomic potentials for materials simulation

Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, Weinan E
Phys. Rev. Materials, 2019, 3, 23804.
DOI: 10.1103/PhysRevMaterials.3.023804

Isotope effects in liquid water via deep potential molecular dynamics

Hsin-Yu Ko, Linfeng Zhang, Biswajit Santra, Han Wang, Weinan E, Robert A. DiStasio Jr, Roberto Car
Molecular Physics, 2019, 117, 3269–3281.
DOI: 10.1080/00268976.2019.1652366

Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds

Tongqi Wen, Cai-Zhuang Wang, M. J. Kramer, Yang Sun, Beilin Ye, Haidi Wang, Xueyuan Liu, Chao Zhang, Feng Zhang, Kai-Ming Ho, Nan Wang
Phys. Rev. B, 2019, 100, 174101.
DOI: 10.1103/PhysRevB.100.174101

Deep learning inter-atomic potential model for accurate irradiation damage simulations

Hao Wang, Xun Guo, Linfeng Zhang, Han Wang, Jianming Xue
Appl. Phys. Lett., 2019, 114, 244101.
DOI: 10.1063/1.5098061

2018

Silicon Liquid Structure and Crystal Nucleation from Ab~Initio Deep Metadynamics

Luigi Bonati, Michele Parrinello
Phys. Rev. Lett., 2018, 121, 265701.
DOI: 10.1103/PhysRevLett.121.265701

Deep Learning for Nonadiabatic Excited-State Dynamics

Wen-Kai Chen, Xiang-Yang Liu, Wei-Hai Fang, Pavlo O Dral, Ganglong Cui
J. Phys. Chem. Lett., 2018, 9, 6702–6708.
DOI: 10.1021/acs.jpclett.8b03026

Adaptive coupling of a deep neural network potential to a classical force field

Linfeng Zhang, Han Wang, Weinan E
J. Chem. Phys., 2018, 149, 154107.
DOI: 10.1063/1.5042714

DeePCG: Constructing coarse-grained models via deep neural networks

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E
J. Chem. Phys., 2018, 149, 34101.
DOI: 10.1063/1.5027645

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Han Wang, Linfeng Zhang, Jiequn Han, Weinan E
Computer Physics Communications, 2018, 228, 178–184.
DOI: 10.1016/j.cpc.2018.03.016

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E
Phys. Rev. Lett., 2018, 120, 143001.
DOI: 10.1103/PhysRevLett.120.143001

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Publications driven by DP-GEN

The following publications have used the DP-GEN software. Publications that only mentioned the DP-GEN will not be included below.

We encourage explicitly mentioning DP-GEN with proper citations in your publications, so we can more easily find and list these publications.

Last update date: Nov 28, 2023

2024

Ultrafast switching dynamics of the ferroelectric order in stacking- engineered ferroelectrics

Ri He, Bingwen Zhang, Hua Wang, Lei Li, Ping Tang, Gerrit Bauer, Zhicheng Zhong
Acta Materialia, 2024, 262, 119416.
DOI: 10.1016/j.actamat.2023.119416

Unraveling pyrolysis mechanisms of lignin dimer model compounds: Neural network-based molecular dynamics simulation investigations

Zhe Shang, Hui Li
Fuel, 2024, 357, 129909.
DOI: 10.1016/j.fuel.2023.129909

2023

Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric KNbO3 perovskite

Hao-Cheng Thong, XiaoYang Wang, Jian Han, Linfeng Zhang, Bei Li, Ke Wang, Ben Xu
Phys. Rev. B, 2023, 107, 14101.
DOI: 10.1103/PhysRevB.107.014101

Li ion diffusion behavior of Li3OCl solid-state electrolytes with different defect structures: insights from the deep potential model

Zhou Zhang, Zhongyun Ma, Yong Pei
Phys. Chem. Chem. Phys., 2023, 25, 13297–13307.
DOI: 10.1039/d2cp06073f

A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten

Chang-Jie Ding, Ya-Wei Lei, Xiao-Yang Wang, Xiao-Lin Li, Xiang-Yan Li, Yan-Ge Zhang, Yi-Chun Xu, Chang-Song Liu, Xue-Bang Wu
Tungsten, 2023.
DOI: 10.1007/s42864-023-00230-4

Deep-learning potentials for proton transport in double-sided graphanol

Siddarth K. Achar, Leonardo Bernasconi, Juan J. Alvarez, J. Karl Johnson
Journal of Materials Research, 2023.
DOI: 10.1557/s43578-023-01141-3

Speciation of La3+-Cl- Complexes in Hydrothermal Fluids from Deep Potential Molecular Dynamics

Wei Zhang, Li Zhou, Tinggui Yan, Mohan Chen
J. Phys. Chem. B, 2023, 127, 8926–8937.
DOI: 10.1021/acs.jpcb.3c05428

Neural Network Water Model Based on the MB-Pol Many-Body Potential

Maria Carolina Muniz, Roberto Car, Athanassios Z Panagiotopoulos
J. Phys. Chem. B, 2023, 127, 9165–9171.
DOI: 10.1021/acs.jpcb.3c04629

Insights into the local structure evolution and thermophysical properties of NaCl-KCl-MgCl2\texte ndashLaCl3 melt driven by machine learning

Jia Zhao, Taixi Feng, Guimin Lu, Jianguo Yu
J. Mater. Chem. A, 2023, 11, 23999–24012.
DOI: 10.1039/d3ta03434h

Constrained Hybrid Monte Carlo Sampling Made Simple for Chemical Reaction Simulations

Bin Jin, Taiping Hu, Kuang Yu, Shenzhen Xu
J. Chem. Theory Comput., 2023, 19, 7343–7357.
DOI: 10.1021/acs.jctc.3c00571

Machine learning assisted investigation of the barocaloric performance in ammonium iodide

Xiong Xu, Fangbiao Li, Chang Niu, Min Li, Hui Wang
2023, 122.
DOI: 10.1063/5.0131696

Thermal transport across copper-water interfaces according to deep potential molecular dynamics

Zhiqiang Li, Xiaoyu Tan, Zhiwei Fu, Linhua Liu, Jia-Yue Yang
Phys. Chem. Chem. Phys., 2023, 25, 6746–6756.
DOI: 10.1039/d2cp05530a

Structure and solidification of the (Fe0.75B0.15Si0.1)100-xTax (x=0-2) melts: Experiment and machine learning

I.V. Sterkhova, L.V. Kamaeva, V.I. Lad'yanov, N.M. Chtchelkatchev
Journal of Physics and Chemistry of Solids, 2023, 174, 111143.
DOI: 10.1016/j.jpcs.2022.111143

Unravelling the dissolution dynamics of silicate minerals by deep learning molecular dynamics simulation: A case of dicalcium silicate

Yunjian Li, Hui Pan, Zongjin Li
Cement and Concrete Research, 2023, 165, 107092.
DOI: 10.1016/j.cemconres.2023.107092

Profiling the off-center atomic displacements in CuCl at finite temperatures with a deep-learning potential

Zhi-Hao Wang, Xuan-Yan Chen, Zhen Zhang, Xie Zhang, Su- Huai Wei
Phys. Rev. Materials, 2023, 7, 34601.
DOI: 10.1103/PhysRevMaterials.7.034601

Liquid-Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems

I. A. Balyakin, R. E. Ryltsev, N. M. Chtchelkatchev
Jetp Lett., 2023, 117, 370–376.
DOI: 10.1134/S0021364023600234

Atomic structure, stability, and dissociation of dislocations in cadmium telluride

Jun Li, Kun Luo, Qi An
International Journal of Plasticity, 2023, 163, 103552.
DOI: 10.1016/j.ijplas.2023.103552

The highest melting point material: Searched by Bayesian global optimization with deep potential molecular dynamics

Yinan Wang, Bo Wen, Xingjian Jiao, Ya Li, Lei Chen, Yujin Wang, Fu-Zhi Dai
2023, 12, 803–814.
DOI: 10.26599/JAC.2023.9220721

Structural and Composition Evolution of Palladium Catalyst for CO Oxidation under Steady-State Reaction Conditions

Jiawei Wu, Dingming Chen, Jianfu Chen, Haifeng Wang
J. Phys. Chem. C, 2023, 127, 6262–6270.
DOI: 10.1021/acs.jpcc.2c07877

Monitoring the melting behavior of boron nanoparticles using a neural network potential

Xiaoya Chang, Qingzhao Chu, Dongping Chen
Phys. Chem. Chem. Phys., 2023, 25, 12841–12853.
DOI: 10.1039/d3cp00571b

Unraveling the Dynamic Correlations between Transition Metal Migration and the Oxygen Dimer Formation in the Highly Delithiated LixCoO2 Cathode

Taiping Hu, Fu-Zhi Dai, Guobing Zhou, Xiaoxu Wang, Shenzhen Xu
J. Phys. Chem. Lett., 2023, 14, 3677–3684.
DOI: 10.1021/acs.jpclett.3c00506

Hydrogen distribution between the Earth's inner and outer core

Liang Yuan, Gerd Steinle-Neumann
Earth and Planetary Science Letters, 2023, 609, 118084.
DOI: 10.1016/j.epsl.2023.118084

Lattice Thermal Conductivity of Monolayer InSe Calculated by Machine Learning Potential

Jinsen Han, Qiyu Zeng, Ke Chen, Xiaoxiang Yu, Jiayu Dai
Nanomaterials (Basel)., 2023, 13, 1576.
DOI: 10.3390/nano13091576

Modeling the high-pressure solid and liquid phases of tin from deep potentials with ab initio accuracy

Tao Chen, Fengbo Yuan, Jianchuan Liu, Huayun Geng, Linfeng Zhang, Han Wang, Mohan Chen
Phys. Rev. Materials, 2023, 7, 53603.
DOI: 10.1103/PhysRevMaterials.7.053603

A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water

Ignacio Sanchez-Burgos, Maria Carolina Muniz, Jorge R Espinosa, Athanassios Z Panagiotopoulos
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0144500

First-Principles-Based Machine Learning Models for Phase Behavior and Transport Properties of CO2

Reha Mathur, Maria Carolina Muniz, Shuwen Yue, Roberto Car, Athanassios Z Panagiotopoulos
J. Phys. Chem. B, 2023, 127, 4562–4569.
DOI: 10.1021/acs.jpcb.3c00610

An accurate interatomic potential for the TiAlNb ternary alloy developed by deep neural network learning method

Jiajun Lu, Jinkai Wang, Kaiwei Wan, Ying Chen, Hao Wang, Xinghua Shi
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0147720

In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol

Siddarth K Achar, Leonardo Bernasconi, Ruby I DeMaio, Katlyn R Howard, J Karl Johnson
ACS Appl. Mater. Interfaces, 2023, 15, 25873–25883.
DOI: 10.1021/acsami.3c04022

Noble gas (He, Ne, and Ar) solubilities in high-pressure silicate melts calculated based on deep-potential modeling

Kai Wang, Xiancai Lu, Xiandong Liu, Kun Yin
Geochimica et Cosmochimica Acta, 2023, 350, 57–68.
DOI: 10.1016/j.gca.2023.03.032

Deciphering the Anomalous Acidic Tendency of Terminal Water at Rutile(110)-Water Interfaces

Yong-Bin Zhuang, Jun Cheng
J. Phys. Chem. C, 2023, 127, 10532–10540.
DOI: 10.1021/acs.jpcc.3c01870

Investigating the Hydroxyl Reorientation in Hydroxyapatite Using Machine Learning Potentials

Jing Wang, Xin Wang, Hua Zhu, Dingguo Xu
J. Phys. Chem. C, 2023, 127, 11369–11377.
DOI: 10.1021/acs.jpcc.3c02426

Molecular insight into the GaP(110)-water interface using machine learning accelerated molecular dynamics

Xue-Ting Fan, Xiao-Jian Wen, Yong-Bin Zhuang, Jun Cheng
Journal of Energy Chemistry, 2023, 82, 239–247.
DOI: 10.1016/j.jechem.2023.03.013

Revealing Carbon Vacancy Distribution on $\alpha$-MoC1-x Surfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach

Jun-Zhong Xie, Hong Jiang
J. Phys. Chem. C, 2023, 127, 13228–13237.
DOI: 10.1021/acs.jpcc.3c01941

Fluorine spillover for ceria- vs silica-supported palladium nanoparticles: A MD study using machine learning potentials

Da-Jiang Liu, James W Evans
J. Chem. Phys., 2023, 159.
DOI: 10.1063/5.0147132

Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab~initio simulations

N M Chtchelkatchev, R E Ryltsev, M V Magnitskaya, S M Gorbunov, K A Cherednichenko, V L Solozhenko, V V Brazhkin
J. Chem. Phys., 2023, 159.
DOI: 10.1063/5.0165948

Deep Potential Molecular Dynamics Study of Chapman-Jouguet Detonation Events of Energetic Materials

Jidong Zhang, Wei Guo, Yugui Yao
J. Phys. Chem. Lett., 2023, 14, 7141–7148.
DOI: 10.1021/acs.jpclett.3c01392

Deep neural network potential for simulating hydrogen blistering in tungsten

Xiao-Yang Wang, Yi-Nan Wang, Ke Xu, Fu-Zhi Dai, Hai-Feng Liu, Guang-Hong Lu, Han Wang
Phys. Rev. Materials, 2023, 7, 93601.
DOI: 10.1103/PhysRevMaterials.7.093601

Unraveling the Atomic-scale Mechanism of Phase Transformations and Structural Evolutions during (de)Lithiation in Si Anodes

Fangjia Fu, Xiaoxu Wang, Linfeng Zhang, Yifang Yang, Jianhui Chen, Bo Xu, Chuying Ouyang, Shenzhen Xu, Fu-Zhi Dai, Weinan E
Adv Funct Materials, 2023, 33.
DOI: 10.1002/adfm.202303936

Collective motion in hcp-Fe at Earth\textquoterights inner core conditions

Youjun Zhang, Yong Wang, Yuqian Huang, Junjie Wang, Zhixin Liang, Long Hao, Zhipeng Gao, Jun Li, Qiang Wu, Hong Zhang, Yun Liu, Jian Sun, Jung-Fu Lin
Proc. Natl. Acad. Sci. U. S. A., 2023, 120, e2309952120.
DOI: 10.1073/pnas.2309952120

Machine learning potential for Ab Initio phase transitions of zirconia

Yuanpeng Deng, Chong Wang, Xiang Xu, Hui Li
Theoretical and Applied Mechanics Letters, 2023, 13, 100481.
DOI: 10.1016/j.taml.2023.100481

Modelling electrified microporous carbon/electrolyte electrochemical interface and unraveling charge storage mechanism by machine learning accelerated molecular dynamics

Yifeng Zhang, Hui Huang, Jie Tian, Chengwei Li, Yuchen Jiang, Zeng Fan, Lujun Pan
Energy Storage Materials, 2023, 63, 103069.
DOI: 10.1016/j.ensm.2023.103069

Data-driven prediction of complex crystal structures of dense lithium

Xiaoyang Wang, Zhenyu Wang, Pengyue Gao, Chengqian Zhang, Jian Lv, Han Wang, Haifeng Liu, Yanchao Wang, Yanming Ma
Nat. Commun., 2023, 14, 2924.
DOI: 10.1038/s41467-023-38650-y

Realizing long-cycling all-solid-state Li-In||TiS2 batteries using Li6+xMxAs1-xS5I (M=Si, Sn) sulfide solid electrolytes

Pushun Lu, Yu Xia, Guochen Sun, Dengxu Wu, Siyuan Wu, Wenlin Yan, Xiang Zhu, Jiaze Lu, Quanhai Niu, Shaochen Shi, Zhengju Sha, Liquan Chen, Hong Li, Fan Wu
Nat. Commun., 2023, 14, 4077.
DOI: 10.1038/s41467-023-39686-w

Dislocation-mediated migration of the $\alpha$/$\beta$ interfaces in titanium

Jin-Yu Zhang, Zhi-Peng Sun, Dong Qiu, Fu-Zhi Dai, Yang- Sheng Zhang, Dongsheng Xu, Wen-Zheng Zhang
Acta Materialia, 2023, 261, 119364.
DOI: 10.1016/j.actamat.2023.119364

Interfacial heat and mass transfer at silica/binary molten salt interface from deep potential molecular dynamics

Fei Liang, Jing Ding, Xiaolan Wei, Gechuanqi Pan, Shule Liu
International Journal of Heat and Mass Transfer, 2023, 217, 124705.
DOI: 10.1016/j.ijheatmasstransfer.2023.124705

Revisiting the structure, interaction, and dynamical property of ionic liquid from the deep learning force field

Yulong Ling, Kun Li, Mi Wang, Junfeng Lu, Chenlu Wang, Yanlei Wang, Hongyan He
Journal of Power Sources, 2023, 555, 232350.
DOI: 10.1016/j.jpowsour.2022.232350

Solvation structures of calcium and magnesium ions in water with the presence of hydroxide: a study by deep potential molecular dynamics

Jianchuan Liu, Renxi Liu, Yu Cao, Mohan Chen
Phys. Chem. Chem. Phys., 2023.
DOI: 10.1039/d2cp04105g

Accurate interatomic potential for the nucleation in liquid Ti-Al binary alloy developed by deep neural network learning method

B. Zhai, H.P. Wang
Computational Materials Science, 2023, 216, 111843.
DOI: 10.1016/j.commatsci.2022.111843

2022

Convergence acceleration in machine learning potentials for atomistic simulations

Dylan Bayerl, Christopher M. Andolina, Shyam Dwaraknath, Wissam A. Saidi
Digital Discovery, 2022, 1, 61–69.
DOI: 10.1039/d1dd00005e

Towards fully ab initio simulation of atmospheric aerosol nucleation

Shuai Jiang, Yi-Rong Liu, Teng Huang, Ya-Juan Feng, Chun- Yu Wang, Zhong-Quan Wang, Bin-Jing Ge, Quan-Sheng Liu, Wei-Ran Guang, Wei Huang
Nat. Commun., 2022, 13, 6067.
DOI: 10.1038/s41467-022-33783-y

Modeling Chemical Reactions in Alkali Carbonate-Hydroxide Electrolytes with Deep Learning Potentials

Anirban Mondal, Dina Kussainova, Shuwen Yue, Athanassios Z Panagiotopoulos
J. Chem. Theory Comput., 2022.
DOI: 10.1021/acs.jctc.2c00816

Lattice Thermal Conductivity of MgSiO3 Perovskite and Post- Perovskite under Lower Mantle Conditions Calculated by Deep Potential Molecular Dynamics

Fenghu Yang, Qiyu Zeng, Bo Chen, Dongdong Kang, Shen Zhang, Jianhua Wu, Xiaoxiang Yu, Jiayu Dai
Chinese Phys. Lett., 2022, 39, 116301.
DOI: 10.1088/0256-307X/39/11/116301

Resolving the odd-even oscillation of water dissociation at rutile TiO2(110)-water interface by machine learning accelerated molecular dynamics

Yong-Bin Zhuang, Rui-Hao Bi, Jun Cheng
J. Chem. Phys., 2022, 157, 164701.
DOI: 10.1063/5.0126333

Origin of negative thermal expansion and pressure-induced amorphization in zirconium tungstate from a machine-learning potential

Ri He, Hongyu Wu, Yi Lu, Zhicheng Zhong
Phys. Rev. B, 2022, 106, 174101.
DOI: 10.1103/PhysRevB.106.174101

Classical and machine learning interatomic potentials for BCC vanadium

Rui Wang, Xiaoxiao Ma, Linfeng Zhang, Han Wang, David J. Srolovitz, Tongqi Wen, Zhaoxuan Wu
Phys. Rev. Materials, 2022, 6, 113603.
DOI: 10.1103/PhysRevMaterials.6.113603

Metal Affinity of Support Dictates Sintering of Gold Catalysts

Jin-Cheng Liu, Langli Luo, Hai Xiao, Junfa Zhu, Yang He, Jun Li
J. Am. Chem. Soc., 2022, 144, 20601–20609.
DOI: 10.1021/jacs.2c06785

Multireference Generalization of the Weighted Thermodynamic Perturbation Method

Timothy J Giese, Jinzhe Zeng, Darrin M York
J. Phys. Chem. A, 2022, 126, 8519–8533.
DOI: 10.1021/acs.jpca.2c06201

Thermal Conductivity of Hydrous Wadsleyite Determined by Non-Equilibrium Molecular Dynamics Based on Machine Learning

Dong Wang, Zhongqing Wu, Xin Deng
Geophysical Research Letters, 2022, 49.
DOI: 10.1029/2022GL100337

Deep potential for a face-centered cubic Cu system at finite temperatures

Yunzhen Du, Zhaocang Meng, Qiang Yan, Canglong Wang, Yuan Tian, Wenshan Duan, Sheng Zhang, Ping Lin
Phys. Chem. Chem. Phys., 2022, 24, 18361–18369.
DOI: 10.1039/D2CP02758E

Structural and electrocatalytic properties of copper clusters: A study via deep learning and first principles

Xiaoning Wang, Haidi Wang, Qiquan Luo, Jinlong Yang
J. Chem. Phys., 2022, 157, 74304.
DOI: 10.1063/5.0100505

High accuracy neural network interatomic potential for NiTi shape memory alloy

Hao Tang, Yin Zhang, Qing-Jie Li, Haowei Xu, Yuchi Wang, Yunzhi Wang, Ju Li
Acta Materialia, 2022, 238, 118217.
DOI: 10.1016/j.actamat.2022.118217

A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment

Xiaoyang Wang, Yinan Wang, Linfeng Zhang, Fuzhi Dai, Han Wang
Nucl. Fusion, 2022, 62, 126013.
DOI: 10.1088/1741-4326/ac888b

Molecular dynamics simulations of LiCl ion pairs in high temperature aqueous solutions by deep learning potential

Wei Zhang, Li Zhou, Bin Yang, Tinggui Yan
Journal of Molecular Liquids, 2022, 367, 120500.
DOI: 10.1016/j.molliq.2022.120500

Combined QM/MM, Machine Learning Path Integral Approach to Compute Free Energy Profiles and Kinetic Isotope Effects in RNA Cleavage Reactions

Timothy J Giese, Jinzhe Zeng, Şölen Ekesan, Darrin M York
J. Chem. Theory Comput., 2022, 18, 4304–4317.
DOI: 10.1021/acs.jctc.2c00151

Machine Learning Accelerates Molten Salt Simulations: Thermal Conductivity of MgCl 2 -NaCl Eutectic

Wenshuo Liang, Guimin Lu, Jianguo Yu
Advcd Theory and Sims, 2022, 2200206.
DOI: 10.1002/adts.202200206

Machine Learning Force Field Aided Cluster Expansion Approach to Configurationally Disordered Materials: Critical Assessment of Training Set Selection and Size Convergence

Jun-Zhong Xie, Xu-Yuan Zhou, Dong Luan, Hong Jiang
J. Chem. Theory Comput., 2022, 18, 3795–3804.
DOI: 10.1021/acs.jctc.2c00017

Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66

Siddarth K Achar, Jacob J Wardzala, Leonardo Bernasconi, Linfeng Zhang, J Karl Johnson
J. Chem. Theory Comput., 2022, 18, 3593–3606.
DOI: 10.1021/acs.jctc.2c00010

Towards large-scale and spatiotemporally resolved diagnosis of electronic density of states by deep learning

Qiyu Zeng, Bo Chen, Xiaoxiang Yu, Shen Zhang, Dongdong Kang, Han Wang, Jiayu Dai
Phys. Rev. B, 2022, 105, 174109.
DOI: 10.1103/PhysRevB.105.174109

Exploring Complex Reaction Networks Using Neural Network-Based Molecular Dynamics Simulation

Qingzhao Chu, Kai H Luo, Dongping Chen
J. Phys. Chem. Lett., 2022, 13, 4052–4057.
DOI: 10.1021/acs.jpclett.2c00647

Acids at the Edge: Why Nitric and Formic Acid Dissociations at Air-Water Interfaces Depend on Depth and on Interface Specific Area

Miguel de la Puente, Rolf David, Axel Gomez, Damien Laage
J. Am. Chem. Soc., 2022, 144, 10524–10529.
DOI: 10.1021/jacs.2c03099

Dissolving salt is not equivalent to applying a pressure on water

Chunyi Zhang, Shuwen Yue, Athanassios Z Panagiotopoulos, Michael L Klein, Xifan Wu
Nat. Commun., 2022, 13, 822.
DOI: 10.1038/s41467-022-28538-8

Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

Paolo Pegolo, Stefano Baroni, Federico Grasselli
npj Comput Mater, 2022, 8, 24.
DOI: 10.1038/s41524-021-00693-4

The chemical origin of temperature-dependent lithium-ion concerted diffusion in sulfide solid electrolyte Li10GeP2S12

Zhong-Heng Fu, Xiang Chen, Nan Yao, Xin Shen, Xia-Xia Ma, Shuai Feng, Shuhao Wang, Rui Zhang, Linfeng Zhang, Qiang Zhang
Journal of Energy Chemistry, 2022, 70, 59–66.
DOI: 10.1016/j.jechem.2022.01.018

Efficient and accurate atomistic modeling of dopant migration using deep neural network

Xi Ding, Ming Tao, Junhua Li, Mingyuan Li, Mengchao Shi, Jiashu Chen, Zhen Tang, Francis Benistant, Jie Liu
Materials Science in Semiconductor Processing, 2022, 143, 106513.
DOI: 10.1016/j.mssp.2022.106513

Exploring the Effects of Ionic Defects on the Stability of CsPbI 3 with a Deep Learning Potential

Weijie Yang, Jiajia Li, Xuelu Chen, Yajun Feng, Chongchong Wu, Ian D Gates, Zhengyang Gao, Xunlei Ding, Jianxi Yao, Hao Li
Chemphyschem, 2022, 23, e202100841.
DOI: 10.1002/cphc.202100841

Self-Healing Mechanism of Lithium in Lithium Metal

Junyu Jiao, Genming Lai, Liang Zhao, Jiaze Lu, Qidong Li, Xianqi Xu, Yao Jiang, Yan-Bing He, Chuying Ouyang, Feng Pan, Hong Li, Jiaxin Zheng
Adv. Sci. (Weinh)., 2022, 9, e2105574.
DOI: 10.1002/advs.202105574

A deep potential model with long-range electrostatic interactions

Linfeng Zhang, Han Wang, Maria Carolina Muniz, Athanassios Z Panagiotopoulos, Roberto Car, Weinan E
J. Chem. Phys., 2022, 156, 124107.
DOI: 10.1063/5.0083669

A generalizable machine learning potential of Ag-Au nanoalloys and its application to surface reconstruction, segregation and diffusion

YiNan Wang, LinFeng Zhang, Ben Xu, XiaoYang Wang, Han Wang
Modelling Simul. Mater. Sci. Eng., 2022, 30, 25003.
DOI: 10.1088/1361-651X/ac4002

Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water

Manyi Yang, Luigi Bonati, Daniela Polino, Michele Parrinello
Catalysis Today, 2022, 387, 143–149.
DOI: 10.1016/j.cattod.2021.03.018

Molecular dynamics simulation of molten strontium chloride based on deep potential

Di Guo, Jia Zhao, Wenshuo Liang, Guimin Lu
Journal of Molecular Liquids, 2022, 348, 118380.
DOI: 10.1016/j.molliq.2021.118380

Structural phase transitions in $\mathrmSrTi\mathrmO_3$ from deep potential molecular dynamics

Ri He, Hongyu Wu, Linfeng Zhang, Xiaoxu Wang, Fangjia Fu, Shi Liu, Zhicheng Zhong
Phys. Rev. B, 2022, 105, 064104.
DOI: 10.1103/PhysRevB.105.064104

A deep learning potential applied in tobermorite phases and extended to calcium silicate hydrates

Yang Zhou, Haojie Zheng, Weihuan Li, Tao Ma, Changwen Miao
Cement and Concrete Research, 2022, 152, 106685.
DOI: 10.1016/j.cemconres.2021.106685

2021

Insights from Computational Studies on the Anisotropic Volume Change of LixNiO2 at High States of Charge (x < 0.25)

Juan C. Garcia, Joshua Gabriel, Noah H. Paulson, John Low, Marius Stan, Hakim Iddir
J. Phys. Chem. C, 2021, 125 (49), 27130-27139.
DOI: 10.1021/acs.jpcc.1c08022

Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

Tongqi Wen, Rui Wang, Lingyu Zhu, Linfeng Zhang, Han Wang, David J. Srolovitz, Zhaoxuan Wu
npj Comput Mater, 2021, 7, 206.
DOI: 10.1038/s41524-021-00661-y

Artificial intelligence model for efficient simulation of monatomic phase change material antimony

Mengchao Shi, Junhua Li, Ming Tao, Xin Zhang, Jie Liu
Materials Science in Semiconductor Processing, 2021, 136, 106146.
DOI: 10.1016/j.mssp.2021.106146

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution

Jinzhe Zeng, Timothy J Giese, Şölen Ekesan, Darrin M York
J. Chem. Theory Comput., 2021, 17, 6993–7009.
DOI: 10.1021/acs.jctc.1c00201

Accurate force field of two-dimensional ferroelectrics from deep learning

Jing Wu, Liyi Bai, Jiawei Huang, Liyang Ma, Jian Liu, Shi Liu
Phys. Rev. B, 2021, 104, 174107.
DOI: 10.1103/PhysRevB.104.174107

Liquid-Liquid Critical Point in Phosphorus

Manyi Yang, Tarak Karmakar, Michele Parrinello
Phys. Rev. Lett., 2021, 127, 80603.
DOI: 10.1103/PhysRevLett.127.080603

Anomalous Behavior of Viscosity and Electrical Conductivity of MgSiO 3 Melt at Mantle Conditions

Haiyang Luo, Bijaya B. Karki, Dipta B. Ghosh, Huiming Bao
Geophys Res Lett, 2021, 48.
DOI: 10.1029/2021GL093573

Phase Diagram of a Deep Potential Water Model

Linfeng Zhang, Han Wang, Roberto Car, Weinan E
Phys. Rev. Lett., 2021, 126, 236001.
DOI: 10.1103/PhysRevLett.126.236001

The thermoelectric performance of new structure SnSe studied by quotient graph and deep learning potential

D. Guo, C. Li, K. Li, B. Shao, D. Chen, Y. Ma, J. Sun, X. Cao, W. Zeng, X. Chang
Materials Today Energy, 2021, 20, 100665.
DOI: 10.1016/j.mtener.2021.100665

Phase Equilibrium of Water with Hexagonal and Cubic Ice Using the SCAN Functional

Pablo M Piaggi, Athanassios Z Panagiotopoulos, Pablo G Debenedetti, Roberto Car
J. Chem. Theory Comput., 2021, 17, 3065–3077.
DOI: 10.1021/acs.jctc.1c00041

Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space*

Wanrun Jiang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Chinese Phys. B, 2021, 30, 50706.
DOI: 10.1088/1674-1056/abf134

Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors

Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng, Weinan E
J. Chem. Phys., 2021, 154, 94703.
DOI: 10.1063/5.0041849

Anharmonic Raman spectra simulation of crystals from deep neural networks

Honghui Shang, Haidi Wang
AIP Advances, 2021, 11, 35105.
DOI: 10.1063/5.0040190

Deep learning of accurate force field of ferroelectric HfO2

Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Phys. Rev. B, 2021, 103, 24108.
DOI: 10.1103/PhysRevB.103.024108

Theoretical study of Na+ transport in the solid-state electrolyte Na3OBr based on deep potential molecular dynamics

Han-Xiao Li, Xu-Yuan Zhou, Yue-Chao Wang, Hong Jiang
Inorg. Chem. Front., 2021, 8, 425–432.
DOI: 10.1039/D0QI00921K

Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator

Jinzhe Zeng, Linfeng Zhang, Han Wang, Tong Zhu
Energy Fuels, 2021, 35, 762–769.
DOI: 10.1021/acs.energyfuels.0c03211

Diffusional fractionation of helium isotopes in silicate melts

H. Luo, B.B. Karki, D.B. Ghosh, H. Bao
Geochem. Persp. Let., 2021, 19–22.
DOI: 10.7185/geochemlet.2128

Deep-learning potential method to simulate shear viscosity of liquid aluminum at high temperature and high pressure by molecular dynamics

Yuqing Cheng, Han Wang, Shuaichuang Wang, Xingyu Gao, Qiong Li, Jun Fang, Hongzhou Song, Weidong Chu, Gongmu Zhang, Haifeng Song, Haifeng Liu
AIP Advances, 2021, 11, 15043.
DOI: 10.1063/5.0036298

2020

Crystal Structure Prediction of Binary Alloys via Deep Potential

Haidi Wang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Front. Chem., 2020, 8, 589795.
DOI: 10.3389/fchem.2020.589795

Signatures of a liquid-liquid transition in an ab initio deep neural network model for water

Thomas E Gartner 3rd, Linfeng Zhang, Pablo M Piaggi, Roberto Car, Athanassios Z Panagiotopoulos, Pablo G Debenedetti
Proc. Natl. Acad. Sci. U. S. A., 2020, 117, 26040–26046.
DOI: 10.1073/pnas.2015440117

A Deep-Learning Potential for Crystalline and Amorphous Li-Si Alloys

Nan Xu, Yao Shi, Yi He, Qing Shao
J. Phys. Chem. C, 2020, 124, 16278–16288.
DOI: 10.1021/acs.jpcc.0c03333

Deep neural network for the dielectric response of insulators

Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, Roberto Car
Phys. Rev. B, 2020, 102, 41121.
DOI: 10.1103/PhysRevB.102.041121

Raman spectrum and polarizability of liquid water from deep neural networks

Grace M Sommers, Marcos F Calegari Andrade, Linfeng Zhang, Han Wang, Roberto Car
Phys. Chem. Chem. Phys., 2020, 22, 10592–10602.
DOI: 10.1039/D0CP01893G

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E
Computer Physics Communications, 2020, 253, 107206.
DOI: 10.1016/j.cpc.2020.107206

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Last update date: Nov 28, 2023

2024

Ultrafast switching dynamics of the ferroelectric order in stacking- engineered ferroelectrics

Ri He, Bingwen Zhang, Hua Wang, Lei Li, Ping Tang, Gerrit Bauer, Zhicheng Zhong
Acta Materialia, 2024, 262, 119416.
DOI: 10.1016/j.actamat.2023.119416

Unraveling pyrolysis mechanisms of lignin dimer model compounds: Neural network-based molecular dynamics simulation investigations

Zhe Shang, Hui Li
Fuel, 2024, 357, 129909.
DOI: 10.1016/j.fuel.2023.129909

2023

Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric KNbO3 perovskite

Hao-Cheng Thong, XiaoYang Wang, Jian Han, Linfeng Zhang, Bei Li, Ke Wang, Ben Xu
Phys. Rev. B, 2023, 107, 14101.
DOI: 10.1103/PhysRevB.107.014101

Li ion diffusion behavior of Li3OCl solid-state electrolytes with different defect structures: insights from the deep potential model

Zhou Zhang, Zhongyun Ma, Yong Pei
Phys. Chem. Chem. Phys., 2023, 25, 13297–13307.
DOI: 10.1039/d2cp06073f

A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten

Chang-Jie Ding, Ya-Wei Lei, Xiao-Yang Wang, Xiao-Lin Li, Xiang-Yan Li, Yan-Ge Zhang, Yi-Chun Xu, Chang-Song Liu, Xue-Bang Wu
Tungsten, 2023.
DOI: 10.1007/s42864-023-00230-4

Deep-learning potentials for proton transport in double-sided graphanol

Siddarth K. Achar, Leonardo Bernasconi, Juan J. Alvarez, J. Karl Johnson
Journal of Materials Research, 2023.
DOI: 10.1557/s43578-023-01141-3

Speciation of La3+-Cl- Complexes in Hydrothermal Fluids from Deep Potential Molecular Dynamics

Wei Zhang, Li Zhou, Tinggui Yan, Mohan Chen
J. Phys. Chem. B, 2023, 127, 8926–8937.
DOI: 10.1021/acs.jpcb.3c05428

Neural Network Water Model Based on the MB-Pol Many-Body Potential

Maria Carolina Muniz, Roberto Car, Athanassios Z Panagiotopoulos
J. Phys. Chem. B, 2023, 127, 9165–9171.
DOI: 10.1021/acs.jpcb.3c04629

Insights into the local structure evolution and thermophysical properties of NaCl-KCl-MgCl2\texte ndashLaCl3 melt driven by machine learning

Jia Zhao, Taixi Feng, Guimin Lu, Jianguo Yu
J. Mater. Chem. A, 2023, 11, 23999–24012.
DOI: 10.1039/d3ta03434h

Constrained Hybrid Monte Carlo Sampling Made Simple for Chemical Reaction Simulations

Bin Jin, Taiping Hu, Kuang Yu, Shenzhen Xu
J. Chem. Theory Comput., 2023, 19, 7343–7357.
DOI: 10.1021/acs.jctc.3c00571

Machine learning assisted investigation of the barocaloric performance in ammonium iodide

Xiong Xu, Fangbiao Li, Chang Niu, Min Li, Hui Wang
2023, 122.
DOI: 10.1063/5.0131696

Thermal transport across copper-water interfaces according to deep potential molecular dynamics

Zhiqiang Li, Xiaoyu Tan, Zhiwei Fu, Linhua Liu, Jia-Yue Yang
Phys. Chem. Chem. Phys., 2023, 25, 6746–6756.
DOI: 10.1039/d2cp05530a

Structure and solidification of the (Fe0.75B0.15Si0.1)100-xTax (x=0-2) melts: Experiment and machine learning

I.V. Sterkhova, L.V. Kamaeva, V.I. Lad'yanov, N.M. Chtchelkatchev
Journal of Physics and Chemistry of Solids, 2023, 174, 111143.
DOI: 10.1016/j.jpcs.2022.111143

Unravelling the dissolution dynamics of silicate minerals by deep learning molecular dynamics simulation: A case of dicalcium silicate

Yunjian Li, Hui Pan, Zongjin Li
Cement and Concrete Research, 2023, 165, 107092.
DOI: 10.1016/j.cemconres.2023.107092

Profiling the off-center atomic displacements in CuCl at finite temperatures with a deep-learning potential

Zhi-Hao Wang, Xuan-Yan Chen, Zhen Zhang, Xie Zhang, Su- Huai Wei
Phys. Rev. Materials, 2023, 7, 34601.
DOI: 10.1103/PhysRevMaterials.7.034601

Liquid-Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems

I. A. Balyakin, R. E. Ryltsev, N. M. Chtchelkatchev
Jetp Lett., 2023, 117, 370–376.
DOI: 10.1134/S0021364023600234

Atomic structure, stability, and dissociation of dislocations in cadmium telluride

Jun Li, Kun Luo, Qi An
International Journal of Plasticity, 2023, 163, 103552.
DOI: 10.1016/j.ijplas.2023.103552

The highest melting point material: Searched by Bayesian global optimization with deep potential molecular dynamics

Yinan Wang, Bo Wen, Xingjian Jiao, Ya Li, Lei Chen, Yujin Wang, Fu-Zhi Dai
2023, 12, 803–814.
DOI: 10.26599/JAC.2023.9220721

Structural and Composition Evolution of Palladium Catalyst for CO Oxidation under Steady-State Reaction Conditions

Jiawei Wu, Dingming Chen, Jianfu Chen, Haifeng Wang
J. Phys. Chem. C, 2023, 127, 6262–6270.
DOI: 10.1021/acs.jpcc.2c07877

Monitoring the melting behavior of boron nanoparticles using a neural network potential

Xiaoya Chang, Qingzhao Chu, Dongping Chen
Phys. Chem. Chem. Phys., 2023, 25, 12841–12853.
DOI: 10.1039/d3cp00571b

Unraveling the Dynamic Correlations between Transition Metal Migration and the Oxygen Dimer Formation in the Highly Delithiated LixCoO2 Cathode

Taiping Hu, Fu-Zhi Dai, Guobing Zhou, Xiaoxu Wang, Shenzhen Xu
J. Phys. Chem. Lett., 2023, 14, 3677–3684.
DOI: 10.1021/acs.jpclett.3c00506

Hydrogen distribution between the Earth's inner and outer core

Liang Yuan, Gerd Steinle-Neumann
Earth and Planetary Science Letters, 2023, 609, 118084.
DOI: 10.1016/j.epsl.2023.118084

Lattice Thermal Conductivity of Monolayer InSe Calculated by Machine Learning Potential

Jinsen Han, Qiyu Zeng, Ke Chen, Xiaoxiang Yu, Jiayu Dai
Nanomaterials (Basel)., 2023, 13, 1576.
DOI: 10.3390/nano13091576

Modeling the high-pressure solid and liquid phases of tin from deep potentials with ab initio accuracy

Tao Chen, Fengbo Yuan, Jianchuan Liu, Huayun Geng, Linfeng Zhang, Han Wang, Mohan Chen
Phys. Rev. Materials, 2023, 7, 53603.
DOI: 10.1103/PhysRevMaterials.7.053603

A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water

Ignacio Sanchez-Burgos, Maria Carolina Muniz, Jorge R Espinosa, Athanassios Z Panagiotopoulos
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0144500

First-Principles-Based Machine Learning Models for Phase Behavior and Transport Properties of CO2

Reha Mathur, Maria Carolina Muniz, Shuwen Yue, Roberto Car, Athanassios Z Panagiotopoulos
J. Phys. Chem. B, 2023, 127, 4562–4569.
DOI: 10.1021/acs.jpcb.3c00610

An accurate interatomic potential for the TiAlNb ternary alloy developed by deep neural network learning method

Jiajun Lu, Jinkai Wang, Kaiwei Wan, Ying Chen, Hao Wang, Xinghua Shi
J. Chem. Phys., 2023, 158.
DOI: 10.1063/5.0147720

In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol

Siddarth K Achar, Leonardo Bernasconi, Ruby I DeMaio, Katlyn R Howard, J Karl Johnson
ACS Appl. Mater. Interfaces, 2023, 15, 25873–25883.
DOI: 10.1021/acsami.3c04022

Noble gas (He, Ne, and Ar) solubilities in high-pressure silicate melts calculated based on deep-potential modeling

Kai Wang, Xiancai Lu, Xiandong Liu, Kun Yin
Geochimica et Cosmochimica Acta, 2023, 350, 57–68.
DOI: 10.1016/j.gca.2023.03.032

Deciphering the Anomalous Acidic Tendency of Terminal Water at Rutile(110)-Water Interfaces

Yong-Bin Zhuang, Jun Cheng
J. Phys. Chem. C, 2023, 127, 10532–10540.
DOI: 10.1021/acs.jpcc.3c01870

Investigating the Hydroxyl Reorientation in Hydroxyapatite Using Machine Learning Potentials

Jing Wang, Xin Wang, Hua Zhu, Dingguo Xu
J. Phys. Chem. C, 2023, 127, 11369–11377.
DOI: 10.1021/acs.jpcc.3c02426

Molecular insight into the GaP(110)-water interface using machine learning accelerated molecular dynamics

Xue-Ting Fan, Xiao-Jian Wen, Yong-Bin Zhuang, Jun Cheng
Journal of Energy Chemistry, 2023, 82, 239–247.
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Revealing Carbon Vacancy Distribution on $\alpha$-MoC1-x Surfaces by Machine-Learning Force-Field-Aided Cluster Expansion Approach

Jun-Zhong Xie, Hong Jiang
J. Phys. Chem. C, 2023, 127, 13228–13237.
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Fluorine spillover for ceria- vs silica-supported palladium nanoparticles: A MD study using machine learning potentials

Da-Jiang Liu, James W Evans
J. Chem. Phys., 2023, 159.
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Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab~initio simulations

N M Chtchelkatchev, R E Ryltsev, M V Magnitskaya, S M Gorbunov, K A Cherednichenko, V L Solozhenko, V V Brazhkin
J. Chem. Phys., 2023, 159.
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Deep Potential Molecular Dynamics Study of Chapman-Jouguet Detonation Events of Energetic Materials

Jidong Zhang, Wei Guo, Yugui Yao
J. Phys. Chem. Lett., 2023, 14, 7141–7148.
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Deep neural network potential for simulating hydrogen blistering in tungsten

Xiao-Yang Wang, Yi-Nan Wang, Ke Xu, Fu-Zhi Dai, Hai-Feng Liu, Guang-Hong Lu, Han Wang
Phys. Rev. Materials, 2023, 7, 93601.
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Unraveling the Atomic-scale Mechanism of Phase Transformations and Structural Evolutions during (de)Lithiation in Si Anodes

Fangjia Fu, Xiaoxu Wang, Linfeng Zhang, Yifang Yang, Jianhui Chen, Bo Xu, Chuying Ouyang, Shenzhen Xu, Fu-Zhi Dai, Weinan E
Adv Funct Materials, 2023, 33.
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Collective motion in hcp-Fe at Earth\textquoterights inner core conditions

Youjun Zhang, Yong Wang, Yuqian Huang, Junjie Wang, Zhixin Liang, Long Hao, Zhipeng Gao, Jun Li, Qiang Wu, Hong Zhang, Yun Liu, Jian Sun, Jung-Fu Lin
Proc. Natl. Acad. Sci. U. S. A., 2023, 120, e2309952120.
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Machine learning potential for Ab Initio phase transitions of zirconia

Yuanpeng Deng, Chong Wang, Xiang Xu, Hui Li
Theoretical and Applied Mechanics Letters, 2023, 13, 100481.
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Modelling electrified microporous carbon/electrolyte electrochemical interface and unraveling charge storage mechanism by machine learning accelerated molecular dynamics

Yifeng Zhang, Hui Huang, Jie Tian, Chengwei Li, Yuchen Jiang, Zeng Fan, Lujun Pan
Energy Storage Materials, 2023, 63, 103069.
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Data-driven prediction of complex crystal structures of dense lithium

Xiaoyang Wang, Zhenyu Wang, Pengyue Gao, Chengqian Zhang, Jian Lv, Han Wang, Haifeng Liu, Yanchao Wang, Yanming Ma
Nat. Commun., 2023, 14, 2924.
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Realizing long-cycling all-solid-state Li-In||TiS2 batteries using Li6+xMxAs1-xS5I (M=Si, Sn) sulfide solid electrolytes

Pushun Lu, Yu Xia, Guochen Sun, Dengxu Wu, Siyuan Wu, Wenlin Yan, Xiang Zhu, Jiaze Lu, Quanhai Niu, Shaochen Shi, Zhengju Sha, Liquan Chen, Hong Li, Fan Wu
Nat. Commun., 2023, 14, 4077.
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Dislocation-mediated migration of the $\alpha$/$\beta$ interfaces in titanium

Jin-Yu Zhang, Zhi-Peng Sun, Dong Qiu, Fu-Zhi Dai, Yang- Sheng Zhang, Dongsheng Xu, Wen-Zheng Zhang
Acta Materialia, 2023, 261, 119364.
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Interfacial heat and mass transfer at silica/binary molten salt interface from deep potential molecular dynamics

Fei Liang, Jing Ding, Xiaolan Wei, Gechuanqi Pan, Shule Liu
International Journal of Heat and Mass Transfer, 2023, 217, 124705.
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Revisiting the structure, interaction, and dynamical property of ionic liquid from the deep learning force field

Yulong Ling, Kun Li, Mi Wang, Junfeng Lu, Chenlu Wang, Yanlei Wang, Hongyan He
Journal of Power Sources, 2023, 555, 232350.
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Solvation structures of calcium and magnesium ions in water with the presence of hydroxide: a study by deep potential molecular dynamics

Jianchuan Liu, Renxi Liu, Yu Cao, Mohan Chen
Phys. Chem. Chem. Phys., 2023.
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Accurate interatomic potential for the nucleation in liquid Ti-Al binary alloy developed by deep neural network learning method

B. Zhai, H.P. Wang
Computational Materials Science, 2023, 216, 111843.
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2022

Convergence acceleration in machine learning potentials for atomistic simulations

Dylan Bayerl, Christopher M. Andolina, Shyam Dwaraknath, Wissam A. Saidi
Digital Discovery, 2022, 1, 61–69.
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Towards fully ab initio simulation of atmospheric aerosol nucleation

Shuai Jiang, Yi-Rong Liu, Teng Huang, Ya-Juan Feng, Chun- Yu Wang, Zhong-Quan Wang, Bin-Jing Ge, Quan-Sheng Liu, Wei-Ran Guang, Wei Huang
Nat. Commun., 2022, 13, 6067.
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Modeling Chemical Reactions in Alkali Carbonate-Hydroxide Electrolytes with Deep Learning Potentials

Anirban Mondal, Dina Kussainova, Shuwen Yue, Athanassios Z Panagiotopoulos
J. Chem. Theory Comput., 2022.
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Lattice Thermal Conductivity of MgSiO3 Perovskite and Post- Perovskite under Lower Mantle Conditions Calculated by Deep Potential Molecular Dynamics

Fenghu Yang, Qiyu Zeng, Bo Chen, Dongdong Kang, Shen Zhang, Jianhua Wu, Xiaoxiang Yu, Jiayu Dai
Chinese Phys. Lett., 2022, 39, 116301.
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Resolving the odd-even oscillation of water dissociation at rutile TiO2(110)-water interface by machine learning accelerated molecular dynamics

Yong-Bin Zhuang, Rui-Hao Bi, Jun Cheng
J. Chem. Phys., 2022, 157, 164701.
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Origin of negative thermal expansion and pressure-induced amorphization in zirconium tungstate from a machine-learning potential

Ri He, Hongyu Wu, Yi Lu, Zhicheng Zhong
Phys. Rev. B, 2022, 106, 174101.
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Classical and machine learning interatomic potentials for BCC vanadium

Rui Wang, Xiaoxiao Ma, Linfeng Zhang, Han Wang, David J. Srolovitz, Tongqi Wen, Zhaoxuan Wu
Phys. Rev. Materials, 2022, 6, 113603.
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Metal Affinity of Support Dictates Sintering of Gold Catalysts

Jin-Cheng Liu, Langli Luo, Hai Xiao, Junfa Zhu, Yang He, Jun Li
J. Am. Chem. Soc., 2022, 144, 20601–20609.
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Multireference Generalization of the Weighted Thermodynamic Perturbation Method

Timothy J Giese, Jinzhe Zeng, Darrin M York
J. Phys. Chem. A, 2022, 126, 8519–8533.
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Thermal Conductivity of Hydrous Wadsleyite Determined by Non-Equilibrium Molecular Dynamics Based on Machine Learning

Dong Wang, Zhongqing Wu, Xin Deng
Geophysical Research Letters, 2022, 49.
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Deep potential for a face-centered cubic Cu system at finite temperatures

Yunzhen Du, Zhaocang Meng, Qiang Yan, Canglong Wang, Yuan Tian, Wenshan Duan, Sheng Zhang, Ping Lin
Phys. Chem. Chem. Phys., 2022, 24, 18361–18369.
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Structural and electrocatalytic properties of copper clusters: A study via deep learning and first principles

Xiaoning Wang, Haidi Wang, Qiquan Luo, Jinlong Yang
J. Chem. Phys., 2022, 157, 74304.
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High accuracy neural network interatomic potential for NiTi shape memory alloy

Hao Tang, Yin Zhang, Qing-Jie Li, Haowei Xu, Yuchi Wang, Yunzhi Wang, Ju Li
Acta Materialia, 2022, 238, 118217.
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A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment

Xiaoyang Wang, Yinan Wang, Linfeng Zhang, Fuzhi Dai, Han Wang
Nucl. Fusion, 2022, 62, 126013.
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Molecular dynamics simulations of LiCl ion pairs in high temperature aqueous solutions by deep learning potential

Wei Zhang, Li Zhou, Bin Yang, Tinggui Yan
Journal of Molecular Liquids, 2022, 367, 120500.
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Combined QM/MM, Machine Learning Path Integral Approach to Compute Free Energy Profiles and Kinetic Isotope Effects in RNA Cleavage Reactions

Timothy J Giese, Jinzhe Zeng, Şölen Ekesan, Darrin M York
J. Chem. Theory Comput., 2022, 18, 4304–4317.
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Machine Learning Accelerates Molten Salt Simulations: Thermal Conductivity of MgCl 2 -NaCl Eutectic

Wenshuo Liang, Guimin Lu, Jianguo Yu
Advcd Theory and Sims, 2022, 2200206.
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Machine Learning Force Field Aided Cluster Expansion Approach to Configurationally Disordered Materials: Critical Assessment of Training Set Selection and Size Convergence

Jun-Zhong Xie, Xu-Yuan Zhou, Dong Luan, Hong Jiang
J. Chem. Theory Comput., 2022, 18, 3795–3804.
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Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66

Siddarth K Achar, Jacob J Wardzala, Leonardo Bernasconi, Linfeng Zhang, J Karl Johnson
J. Chem. Theory Comput., 2022, 18, 3593–3606.
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Towards large-scale and spatiotemporally resolved diagnosis of electronic density of states by deep learning

Qiyu Zeng, Bo Chen, Xiaoxiang Yu, Shen Zhang, Dongdong Kang, Han Wang, Jiayu Dai
Phys. Rev. B, 2022, 105, 174109.
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Exploring Complex Reaction Networks Using Neural Network-Based Molecular Dynamics Simulation

Qingzhao Chu, Kai H Luo, Dongping Chen
J. Phys. Chem. Lett., 2022, 13, 4052–4057.
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Acids at the Edge: Why Nitric and Formic Acid Dissociations at Air-Water Interfaces Depend on Depth and on Interface Specific Area

Miguel de la Puente, Rolf David, Axel Gomez, Damien Laage
J. Am. Chem. Soc., 2022, 144, 10524–10529.
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Dissolving salt is not equivalent to applying a pressure on water

Chunyi Zhang, Shuwen Yue, Athanassios Z Panagiotopoulos, Michael L Klein, Xifan Wu
Nat. Commun., 2022, 13, 822.
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Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

Paolo Pegolo, Stefano Baroni, Federico Grasselli
npj Comput Mater, 2022, 8, 24.
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The chemical origin of temperature-dependent lithium-ion concerted diffusion in sulfide solid electrolyte Li10GeP2S12

Zhong-Heng Fu, Xiang Chen, Nan Yao, Xin Shen, Xia-Xia Ma, Shuai Feng, Shuhao Wang, Rui Zhang, Linfeng Zhang, Qiang Zhang
Journal of Energy Chemistry, 2022, 70, 59–66.
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Efficient and accurate atomistic modeling of dopant migration using deep neural network

Xi Ding, Ming Tao, Junhua Li, Mingyuan Li, Mengchao Shi, Jiashu Chen, Zhen Tang, Francis Benistant, Jie Liu
Materials Science in Semiconductor Processing, 2022, 143, 106513.
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Exploring the Effects of Ionic Defects on the Stability of CsPbI 3 with a Deep Learning Potential

Weijie Yang, Jiajia Li, Xuelu Chen, Yajun Feng, Chongchong Wu, Ian D Gates, Zhengyang Gao, Xunlei Ding, Jianxi Yao, Hao Li
Chemphyschem, 2022, 23, e202100841.
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Self-Healing Mechanism of Lithium in Lithium Metal

Junyu Jiao, Genming Lai, Liang Zhao, Jiaze Lu, Qidong Li, Xianqi Xu, Yao Jiang, Yan-Bing He, Chuying Ouyang, Feng Pan, Hong Li, Jiaxin Zheng
Adv. Sci. (Weinh)., 2022, 9, e2105574.
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A deep potential model with long-range electrostatic interactions

Linfeng Zhang, Han Wang, Maria Carolina Muniz, Athanassios Z Panagiotopoulos, Roberto Car, Weinan E
J. Chem. Phys., 2022, 156, 124107.
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A generalizable machine learning potential of Ag-Au nanoalloys and its application to surface reconstruction, segregation and diffusion

YiNan Wang, LinFeng Zhang, Ben Xu, XiaoYang Wang, Han Wang
Modelling Simul. Mater. Sci. Eng., 2022, 30, 25003.
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Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water

Manyi Yang, Luigi Bonati, Daniela Polino, Michele Parrinello
Catalysis Today, 2022, 387, 143–149.
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Molecular dynamics simulation of molten strontium chloride based on deep potential

Di Guo, Jia Zhao, Wenshuo Liang, Guimin Lu
Journal of Molecular Liquids, 2022, 348, 118380.
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Structural phase transitions in $\mathrmSrTi\mathrmO_3$ from deep potential molecular dynamics

Ri He, Hongyu Wu, Linfeng Zhang, Xiaoxu Wang, Fangjia Fu, Shi Liu, Zhicheng Zhong
Phys. Rev. B, 2022, 105, 064104.
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A deep learning potential applied in tobermorite phases and extended to calcium silicate hydrates

Yang Zhou, Haojie Zheng, Weihuan Li, Tao Ma, Changwen Miao
Cement and Concrete Research, 2022, 152, 106685.
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2021

Insights from Computational Studies on the Anisotropic Volume Change of LixNiO2 at High States of Charge (x < 0.25)

Juan C. Garcia, Joshua Gabriel, Noah H. Paulson, John Low, Marius Stan, Hakim Iddir
J. Phys. Chem. C, 2021, 125 (49), 27130-27139.
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Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

Tongqi Wen, Rui Wang, Lingyu Zhu, Linfeng Zhang, Han Wang, David J. Srolovitz, Zhaoxuan Wu
npj Comput Mater, 2021, 7, 206.
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Artificial intelligence model for efficient simulation of monatomic phase change material antimony

Mengchao Shi, Junhua Li, Ming Tao, Xin Zhang, Jie Liu
Materials Science in Semiconductor Processing, 2021, 136, 106146.
DOI: 10.1016/j.mssp.2021.106146

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution

Jinzhe Zeng, Timothy J Giese, Şölen Ekesan, Darrin M York
J. Chem. Theory Comput., 2021, 17, 6993–7009.
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Accurate force field of two-dimensional ferroelectrics from deep learning

Jing Wu, Liyi Bai, Jiawei Huang, Liyang Ma, Jian Liu, Shi Liu
Phys. Rev. B, 2021, 104, 174107.
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Liquid-Liquid Critical Point in Phosphorus

Manyi Yang, Tarak Karmakar, Michele Parrinello
Phys. Rev. Lett., 2021, 127, 80603.
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Anomalous Behavior of Viscosity and Electrical Conductivity of MgSiO 3 Melt at Mantle Conditions

Haiyang Luo, Bijaya B. Karki, Dipta B. Ghosh, Huiming Bao
Geophys Res Lett, 2021, 48.
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Phase Diagram of a Deep Potential Water Model

Linfeng Zhang, Han Wang, Roberto Car, Weinan E
Phys. Rev. Lett., 2021, 126, 236001.
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The thermoelectric performance of new structure SnSe studied by quotient graph and deep learning potential

D. Guo, C. Li, K. Li, B. Shao, D. Chen, Y. Ma, J. Sun, X. Cao, W. Zeng, X. Chang
Materials Today Energy, 2021, 20, 100665.
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Phase Equilibrium of Water with Hexagonal and Cubic Ice Using the SCAN Functional

Pablo M Piaggi, Athanassios Z Panagiotopoulos, Pablo G Debenedetti, Roberto Car
J. Chem. Theory Comput., 2021, 17, 3065–3077.
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Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space*

Wanrun Jiang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Chinese Phys. B, 2021, 30, 50706.
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Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors

Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng, Weinan E
J. Chem. Phys., 2021, 154, 94703.
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Anharmonic Raman spectra simulation of crystals from deep neural networks

Honghui Shang, Haidi Wang
AIP Advances, 2021, 11, 35105.
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Deep learning of accurate force field of ferroelectric HfO2

Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Phys. Rev. B, 2021, 103, 24108.
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Theoretical study of Na+ transport in the solid-state electrolyte Na3OBr based on deep potential molecular dynamics

Han-Xiao Li, Xu-Yuan Zhou, Yue-Chao Wang, Hong Jiang
Inorg. Chem. Front., 2021, 8, 425–432.
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Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator

Jinzhe Zeng, Linfeng Zhang, Han Wang, Tong Zhu
Energy Fuels, 2021, 35, 762–769.
DOI: 10.1021/acs.energyfuels.0c03211

Diffusional fractionation of helium isotopes in silicate melts

H. Luo, B.B. Karki, D.B. Ghosh, H. Bao
Geochem. Persp. Let., 2021, 19–22.
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Deep-learning potential method to simulate shear viscosity of liquid aluminum at high temperature and high pressure by molecular dynamics

Yuqing Cheng, Han Wang, Shuaichuang Wang, Xingyu Gao, Qiong Li, Jun Fang, Hongzhou Song, Weidong Chu, Gongmu Zhang, Haifeng Song, Haifeng Liu
AIP Advances, 2021, 11, 15043.
DOI: 10.1063/5.0036298

2020

Crystal Structure Prediction of Binary Alloys via Deep Potential

Haidi Wang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Front. Chem., 2020, 8, 589795.
DOI: 10.3389/fchem.2020.589795

Signatures of a liquid-liquid transition in an ab initio deep neural network model for water

Thomas E Gartner 3rd, Linfeng Zhang, Pablo M Piaggi, Roberto Car, Athanassios Z Panagiotopoulos, Pablo G Debenedetti
Proc. Natl. Acad. Sci. U. S. A., 2020, 117, 26040–26046.
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A Deep-Learning Potential for Crystalline and Amorphous Li-Si Alloys

Nan Xu, Yao Shi, Yi He, Qing Shao
J. Phys. Chem. C, 2020, 124, 16278–16288.
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Deep neural network for the dielectric response of insulators

Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, Roberto Car
Phys. Rev. B, 2020, 102, 41121.
DOI: 10.1103/PhysRevB.102.041121

Raman spectrum and polarizability of liquid water from deep neural networks

Grace M Sommers, Marcos F Calegari Andrade, Linfeng Zhang, Han Wang, Roberto Car
Phys. Chem. Chem. Phys., 2020, 22, 10592–10602.
DOI: 10.1039/D0CP01893G

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E
Computer Physics Communications, 2020, 253, 107206.
DOI: 10.1016/j.cpc.2020.107206

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Efficiently Trained Deep Learning Potential for Graphane

Siddarth K. Achar, Linfeng Zhang, J. Karl Johnson
The Journal of Physical Chemistry C, 2021, 125 (27), 14874–14882.
DOI: 10/gmfwwb

Cormorant: Covariant Molecular Neural Networks

Brandon Anderson, Truong-Son Hy, Risi Kondor
Advances in Neural Information Processing Systems 32 (Nips 2019), 2019, 32.

Optimization and Validation of a Deep Learning CuZr Atomistic Potential: Robust Applications for Crystalline and Amorphous Phases with near-DFT Accuracy

Christopher M. Andolina, Philip Williamson, Wissam A. Saidi
Journal of Chemical Physics, 2020, 152 (15).
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Robust, Multi-Length-Scale, Machine Learning Potential for Ag–Au Bimetallic Alloys from Clusters to Bulk Materials

Christopher M. Andolina, Marta Bon, Daniele Passerone, Wissam A. Saidi
The Journal of Physical Chemistry C, 2021.
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Free Energy of Proton Transfer at the Water-TiO2 Interface from Ab Initio Deep Potential Molecular Dynamics

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Chemical Science, 2020, 11 (9), 2335–2341.
DOI: 10.1039/c9sc05116c

Hydrogen Dynamics in Supercritical Water Probed by Neutron Scattering and Computer Simulations

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Journal of Physical Chemistry Letters, 2020, 11 (21), 9461–9467.
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Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

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2020.

Active Learning Accelerates Ab Initio Molecular Dynamics on Reactive Energy Surfaces

Shi Jun Ang, Wujie Wang, Daniel Schwalbe-Koda, Simon Axelrod, Rafael Gómez-Bombarelli
Chem, 2021, 7 (3), 738–751.
DOI: 10/gmgdj2

Embedding Quantum Statistical Excitations in a Classical Force Field

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DOI: 10.1021/acs.jpca.1c00164

Deep Machine Learning Interatomic Potential for Liquid Silica

I. A. Balyakin, S. Rempel, R. E. Ryltsev, A. A. Rempel
Physical Review E, 2020, 102 (5), 052125.
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Machine-Learning-Based Interatomic Potential for Phonon Transport in Perfect Crystalline Si and Crystalline Si with Vacancies

Hasan Banaei, Ruiqiang Guo, Amirreza Hashemi, Sangyeop Lee
Physical Review Materials, 2019, 3 (7), 074603.
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Structure Motif-Centric Learning Framework for Inorganic Crystalline Systems

Huta R. Banjade, Sandro Hauri, Shanshan Zhang, Francesco Ricci, Weiyi Gong, Geoffroy Hautier, Slobodan Vucetic, Qimin Yan
Science Advances, 2021, 7 (17), eabf1754.
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Voxelized Atomic Structure Potentials: Predicting Atomic Forces with the Accuracy of Quantum Mechanics Using Convolutional Neural Networks

Matthew C. Barry, Kristopher E. Wise, Surya R. Kalidindi, Satish Kumar
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Machine Learning a General-Purpose Interatomic Potential for Silicon

Albert P. Bartók, James Kermode, Noam Bernstein, Gábor Csányi
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Machine Learning for Multi-Fidelity Scale Bridging and Dynamical Simulations of Materials

R Batra, S Sankaranarayanan - Journal of Physics: Materials, undefined 2020
iopscience.iop.org, 2020, 3, 31002.
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SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, Boris Kozinsky
2021.

De Novo Exploration and Self-Guided Learning of Potential-Energy Surfaces

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Npj Computational Materials, 2019, 5, 99.
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Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Physical Review B, 2021, 103 (2), 024108.
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Deep Learning of Accurate Force Field of Ferroelectric HfO2

Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Physical Review B, 2021, 103 (2), 024108.
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Modeling of Metal Nanoparticles: Development of Neural-Network Interatomic Potential Inspired by Features of the Modified Embedded-Atom Method

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Physical Review B, 2020, 102 (14), 144107.
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High-Throughput Study of Lattice Thermal Conductivity in Binary Rocksalt and Zinc Blende Compounds Including Higher-Order Anharmonicity

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Physical Review X, 2020, 10 (4), 041029.
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Ab-Initio Study of Interacting Fermions at Finite Temperature with Neural Canonical Transformation

Hao Xie, Linfeng Zhang, Lei Wang
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Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene

Yu Xie, Jonathan Vandermause, Lixin Sun, Andrea Cepellotti, Boris Kozinsky
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Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials

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Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method

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Automatically Constructed Neural Network Potentials for Molecular Dynamics Simulation of Zinc Proteins

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A Deep-Learning Potential for Crystalline and Amorphous Li-Si Alloys

Nan Xu, Yao Shi, Yi He, Qing Shao
Journal of Physical Chemistry C, 2020, 124 (30), 16278–16288.
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Ab Initio Molecular Dynamics Simulation of Zinc Metalloproteins with Enhanced Self-Organizing Incremental High Dimensional Neural Network

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Isotope Effects in Molecular Structures and Electronic Properties of Liquid Water via Deep Potential Molecular Dynamics Based on the SCAN Functional

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Physical Review B, 2020, 102 (21), 214113.
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Isotope Effects in Molecular Structures and Electronic Properties of Liquid Water via Deep Potential Molecular Dynamics Based on the SCAN Functional

Jianhang Xu, Chunyi Zhang, Linfeng Zhang, Mohan Chen, Biswajit Santra, Xifan Wu
Physical Review B, 2020, 102 (21), 214113.
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Molecular Dynamics Simulation of Zinc Ion in Water with an Ab Initio Based Neural Network Potential

Mingyuan Xu, Tong Zhu, John Z. H. Zhang
Journal of Physical Chemistry A, 2019, 123 (30), 6587–6595.
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De Novo Molecule Design through Molecular Generative Model Conditioned by 3D Information of Protein Binding Sites

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Optimizing Training Data Set for the Machine Learning Potential of Li-Si Alloys via Structural Similarity-Based Screening

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Perspective on Computational Reaction Prediction Using Machine Learning Methods in Heterogeneous Catalysis

Jiayan Xu, Xiao-Ming Cao, P. Hu
Physical Chemistry Chemical Physics, 2021, 23 (19), 11155–11179.
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Using Metadynamics to Build Neural Network Potentials for Reactive Events: The Case of Urea Decomposition in Water

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Construction of a Neural Network Energy Function for Protein Physics

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Role of Water in the Reaction Mechanism and Endo/Exo Selectivity of 1,3-Dipolar Cycloadditions Elucidated by Quantum Chemistry and Machine Learning

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Chemistry-a European Journal, 2019, 25 (35), 8289–8303.
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Active Learning Algorithm for Computational Physics

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Nuclear Quantum Effect and Its Temperature Dependence in Liquid Water from Random Phase Approximation via Artificial Neural Network

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The Journal of Physical Chemistry Letters, 2021, 12 (27), 6354–6362.
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Atomic Energy Mapping of Neural Network Potential

Dongsun Yoo, Kyuhyun Lee, Wonseok Jeong, Dongheon Lee, Satoshi Watanabe, Seungwu Han
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A Transferable Active-Learning Strategy for Reactive Molecular Force Fields

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When Do Short-Range Atomistic Machine-Learning Models Fall Short?

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Explore the Chemical Space of Linear Alkanes Pyrolysis via Deep Potential Generator

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Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution

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Complex Reaction Processes in Combustion Unraveled by Neural Network-Based Molecular Dynamics Simulation

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Complex Reaction Processes in Combustion Unraveled by Neural Network-Based Molecular Dynamics Simulation

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Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator

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Neural Network Based in Silico Simulation of Combustion Reactions

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Deep Density: Circumventing the Kohn-Sham Equations via Symmetry Preserving Neural Networks

Leonardo Zepeda-Núñez, Yixiao Chen, Jiefu Zhang, Weile Jia, Linfeng Zhang, Lin Lin
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Active Learning of Many-Body Configuration Space: Application to the Cs+-Water MB-Nrg Potential Energy Function as a Case Study

Yaoguang Zhai, Alessandro Caruso, Sicun Gao, Francesco Paesani
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BubbleNet: Inferring Micro-Bubble Dynamics with Semi-Physics-Informed Deep Learning

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Machine Learning for Multi-Scale Molecular Modeling: Theories, Algorithms, and Applications

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Accelerating Atomistic Simulations with Piecewise Machine-Learned Ab Initio Potentials at a Classical Force Field-like Cost

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Active Learning of Uniformly Accurate Interatomic Potentials for Materials Simulation

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Adaptive Coupling of a Deep Neural Network Potential to a Classical Force Field

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Anomalous Phase Separation and Hidden Coarsening of Super-Clusters in the Falicov-Kimball Model

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Arrested Phase Separation in Double-Exchange Models: Machine-Learning Enabled Large-Scale Simulation

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Bridging the Gap between Direct Dynamics and Globally Accurate Reactive Potential Energy Surfaces Using Neural Networks

Yaolong Zhang, Xueyao Zhou, Bin Jiang
Journal of Physical Chemistry Letters, 2019, 10 (6), 1185–1191.
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Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential

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DeePCG: Constructing Coarse-Grained Models via Deep Neural Networks

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Deep Neural Network for the Dielectric Response of Insulators

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Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

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Physical Review Letters, 2018, 120 (14), 143001.
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DP-GEN: A Concurrent Learning Platform for the Generation of Reliable Deep Learning Based Potential Energy Models

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Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties

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The Journal of Physical Chemistry B, 2020, 124 (33), 7284–7290.
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Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation

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Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation

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Global Optimization of Chemical Cluster Structures: Methods, Applications, and Challenges

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Isotope Effects in X-Ray Absorption Spectra of Liquid Water

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A Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks

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Machine Learning Dynamics of Phase Separation in Correlated Electron Magnets

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Molecular CT: Unifying Geometry and Representation Learning for Molecules at Different Scales

Jun Zhang, Yaqiang Zhou, Yao-Kun Lei, Yi Isaac Yang, Yi Qin Gao
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A Perspective on Deep Learning for Molecular Modeling and Simulations

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Reinforced Dynamics for Enhanced Sampling in Large Atomic and Molecular Systems

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Warm Dense Matter Simulation via Electron Temperature Dependent Deep Potential Molecular Dynamics

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Physics of Plasmas, 2020, 27 (12), 122704.
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Learning the Physics of Pattern Formation from Images

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Theoretical Prediction on the Redox Potentials of Rare-Earth Ions by Deep Potentials

Jia Zhao, Wenshuo Liang, Guimin Lu
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Retention and Recycling of Deuterium in Liquid Lithium-Tin Slab Studied by First-Principles Molecular Dynamics

Daye Zheng, Zhen-Xiong Shen, Mohan Chen, Xinguo Ren, Lixin He
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Atomic-State-Dependent Screening Model for Hot and Warm Dense Plasmas

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Frame-Independent Vector-Cloud Neural Network for Nonlocal Constitutive Modelling on Arbitrary Grids

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Structure and Dynamics of Supercooled Liquid Ge \textsubscript2 Sb \textsubscript2 Te \textsubscript5 from Machine‐Learning‐Driven Simulations

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Discriminating High-Pressure Water Phases Using Rare-Event Determined Ionic Dynamical Properties*

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Chinese Physics Letters, 2020, 37 (4), 043101.
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Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence

Tetiana Zubatiuk, Olexandr Isayev
Accounts of Chemical Research, 2021, 54 (7), 1575–1585.
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Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks

Tetiana Zubatiuk, Benjamin Nebgen, Nicholas Lubbers, Justin S. Smith, Roman Zubatyuk, Guoqing Zhou, Christopher Koh, Kipton Barros, Olexandr Isayev, Sergei Tretiak
The Journal of Chemical Physics, 2021, 154 (24), 244108.
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Performance and Cost Assessment of Machine Learning Interatomic Potentials

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Modified Embedded-Atom Method Potentials for the Plasticity and Fracture Behaviors of Unary Fcc Metals

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Machine Learning on Neutron and X-Ray Scattering and Spectroscopies

Zhantao Chen, Nina Andrejevic, Nathan C. Drucker, Thanh Nguyen, R. Patrick Xian, Tess Smidt, Yao Wang, Ralph Ernstorfer, D. Alan Tennant, Maria Chan, Mingda Li
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Deep Learning for Nonadiabatic Excited-State Dynamics

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Building Machine Learning Force Fields of Proteins with Fragment-Based Approach and Transfer Learning

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The Study of the Optical Phonon Frequency of 3C-SiC by Molecular Dynamics Simulations with Deep Neural Network Potential

Wei Chen, Liang-Sheng Li
Journal of Applied Physics, 2021, 129 (24), 244104.
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On the Role of Gradients for Machine Learning of Molecular Energies and Forces

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Long-Lived Hot Electron in a Metallic Particle for Plasmonics and Catalysis: Ab Initio Nonadiabatic Molecular Dynamics with Machine Learning

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Implementing a Neural Network Interatomic Model with Performance Portability for Emerging Exascale Architectures

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Nonadiabatic Excited-State Dynamics with Machine Learning

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Machine Learning and Computational Mathematics

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The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety

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Heat and Charge Transport in H 2 O at Ice-Giant Conditions from Ab Initio Molecular Dynamics Simulations

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High-Throughput Production of Force-Fields for Solid-State Electrolyte Materials

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Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory Based \$ Ab \$\$ Initio \$ Molecular Dynamics II: Extensions to the Isobaric-Isoenthalpic and Isobaric-Isothermal Ensembles

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Molecular Dynamics Simulations of Molten Magnesium Chloride Using Machine‐Learning‐Based Deep Potential

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A Deep Neural Network Interatomic Potential for Studying Thermal Conductivity of β-Ga2O3

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Effects of Density and Composition on the Properties of Amorphous Alumina: A High-Dimensional Neural Network Potential Study

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Automatically Growing Global Reactive Neural Network Potential Energy Surfaces: A Trajectory-Free Active Learning Strategy

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A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions

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Understanding Simple Liquids through Statistical and Deep Learning Approaches

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Automated Calculation of Thermal Rate Coefficients Using Ring Polymer Molecular Dynamics and Machine-Learning Interatomic Potentials with Active Learning

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Modeling H 2 O/Rutile-TiO 2 (110) Potential Energy Surfaces with Deep Networks

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Deep Learning Interatomic Potential for Simulation of Radiation Damage in Vanadium-Rich V-Cr-Ti Ternary Alloys

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Deep Learning for UV Absorption Spectra with SchNarc: First Steps toward Transferability in Chemical Compound Space

Julia Westermayr, Philipp Marquetand
The Journal of Chemical Physics, 2020, 153 (15), 154112.
DOI: 10.1063/5.0021915

Machine Learning for Nonadiabatic Molecular Dynamics

Julia Westermayr, Philipp Marquetand
Machine Learning in Chemistry, 2020, 17, 76.
DOI: 10.1039/9781839160233-00076

A Data-Driven Construction of the Periodic Table of the Elements

Michael J. Willatt, Félix Musil, Michele Ceriotti
2018.

Theory and Practice of Atom-Density Representations for Machine Learning

Michael J. Willatt, Félix Musil, Michele Ceriotti
arXiv preprint, 2018.

Modeling and Predicting Responses of Magnetoelectric Materials

Ben Xu, Ce-Wen Nan
MRS Bulletin, 2018, 43 (11), 829–833.
DOI: 10.1557/mrs.2018.259

Theoretical Investigation of Halide Perovskites for Solar Cell and Optoelectronic Applications

Jingxiu Yang, Peng Zhang, Jianping Wang, Su Huai Wei
Chinese Physics B, 2020, 29 (10).
DOI: 10.1088/1674-1056/abb3f6

OnsagerNet: Learning Stable and Interpretable Dynamics Using a Generalized Onsager Principle

Haijun Yu, Xinyuan Tian, Q Li - arXiv preprint ArXiv:2009.02327, undefined 2020, Weinan E, Qianxiao Li
arxiv.org, 2020.

Exploration of Transferable and Uniformly Accurate Neural Network Interatomic Potentials Using Optimal Experimental Design

V Zaverkin, J Kästner
iopscience.iop.org, 2021.

Discovery and Design of Soft Polymeric Bio-Inspired Materials with Multiscale Simulations and Artificial Intelligence

Chenxi Zhai, Tianjiao Li, Haoyuan Shi, Jingjie Yeo
Journal of Materials Chemistry B, 2020, 8 (31), 6562–6587.
DOI: 10.1039/D0TB00896F

Inferring Micro-Bubble Dynamics with Physics-Informed Deep Learning

Hanfeng Zhai, Guohui Hu
2021.

Arrested Phase Separation in Double-Exchange Models: Machine-Learning Enabled Large-Scale Simulation

Puhan Zhang, Gia-Wei Chern
2021.

Physically Inspired Atom-Centered Symmetry Functions for the Construction of High Dimensional Neural Network Potential Energy Surfaces

Kangyu Zhang, Lichang Yin, Gang Liu
Computational Materials Science, 2021, 186, 110071.
DOI: 10.1016/j.commatsci.2020.110071

Adaptive Genetic Algorithm for Structure Prediction and Application to Magnetic Materials

Xin Zhao, Shunqing Wu, Manh Cuong Nguyen, Kai-Ming Ho, Cai-Zhuang Wang
Handbook of Materials Modeling: Applications: Current and Emerging Materials, 2020, 2757–2776.
DOI: 10.1007/978-3-319-44680-6_73

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\ No newline at end of file diff --git a/papers/reviews.html b/papers/reviews.html index 55c4d07..787fd36 100644 --- a/papers/reviews.html +++ b/papers/reviews.html @@ -1 +1 @@ -Reviews | DeepModeling

DeepModeling

Define the future of scientific computing together

Reviews

Discovery and Implementation of Fast, Accurate and Transferable Many-Body Interatomic Potentials

Adarsh Balasubramanian
2019.

Four Generations of High-Dimensional Neural Network Potentials

Jörg Behler
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.0c00868

Dynamical Processes in the Condensed Phase: Methods and Models

Matthew Ralph Carbone
2021.

Machine Learning and the Physical Sciences

Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, Lenka Zdeborova
Reviews of Modern Physics, 2019, 91 (4), 045002.
DOI: 10.1103/RevModPhys.91.045002

Autonomous Discovery in the Chemical Sciences Part I: Progress

Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Angewandte Chemie-International Edition, 2020, 59 (51), 22858–22893.
DOI: 10.1002/anie.201909987

Designing Models Using Machine Learning: One-Body Reduced Density Matrices and Spectra

Andrea Costamagna
2020.

Interfacial Potentials in Ion Solvation

Carrie Conor Doyle
2020.

Molecular Excited States through a Machine Learning Lens

Pavlo O. Dral, Mario Barbatti
Nature Reviews Chemistry, 2021, 5 (6), 388–405.
DOI: 10.1038/s41570-021-00278-1

Characterizing Magnetic Skyrmions at Their Fundamental Length and Time Scales

Peter Fischer, Sujoy Roy
Magnetic Skyrmions and Their Applications, 2021, 55–97.

Unsupervised Learning Methods for Molecular Simulation Data

Aldo Glielmo, Brooke E. Husic, Alex Rodriguez, Cecilia Clementi, Frank Noé, Alessandro Laio
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.0c01195

The Structure and Dynamics of Materials Using Machine Learning

Mário Rui Gonçalves Marques
2020.

Machine-Learning-Assisted Modeling

Sarah Greenstreet
Physics Today, 2021, 74 (7), 42–47.
DOI: 10.1063/PT.3.4794

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

Andrea Grisafi, David M. Wilkins, Michael J. Willatt, Michele Ceriotti
Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions, 2019, 1–21.

Adaptive Iron-Based Magnetic Nanomaterials of High Performance for Biomedical Applications

Ning Gu, Zuoheng Zhang, Yan Li
Nano Research, 2021.
DOI: 10.1007/s12274-021-3546-1

Deep Learning for Large-Scale Molecular Dynamics and High-Dimensional Partial Differential Equations

Jiequn Han
2018.

Machine Learning for Alloys

Gus L. W. Hart, Tim Mueller, Cormac Toher, Stefano Curtarolo
Nature Reviews Materials, 2021.
DOI: 10.1038/s41578-021-00340-w

Characterizing Performance Improvement of GPUs

Dodi Heryadi, Scott Hampton
Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning), 2019, 1–5.

Physics-Informed Machine Learning

George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, Liu Yang
Nature Reviews Physics, 2021, 3 (6), 422–440.
DOI: 10.1038/s42254-021-00314-5

Neural Network Potentials: A Concise Overview of Methods

Emir Kocer, TW Tsz Wai Ko, Jörg Behler, J Behler
arxiv.org, 2021.

First-Principles Study on the Structural and Thermal Properties of Molecular Crystals and Liquids

Hsin-Yu Ko
2019.

FMO Interfaced with Molecular Dynamics Simulation

Yuto Komeiji, Takeshi Ishikawa
Recent Advances of the Fragment Molecular Orbital Method, 2021, 373–389.

Classical Molecular Dynamics Using Neural Network Representations of Potential Energy Surfaces

Andreas Godø Lefdalsnes
2019.

Modeling Electrified Metal/Water Interfaces from Ab Initio Molecular Dynamics: Structure and Helmholtz Capacitance

Jia-Bo Le, Jun Cheng
Current Opinion in Electrochemistry, 2021, 27, 100693.
DOI: 10/ghtqnk

Molecular Dynamics Study of Charged Nanomaterials: Electrostatics and Self-Assembly

Yaohua Li
2021.

Discovering and Understanding Materials through Computation

Steven G. Louie, Yang-Hao Chan, Felipe H. da Jornada, Zhenglu Li, Diana Y. Qiu
Nature Materials, 2021, 20 (6), 728–735.
DOI: 10.1038/s41563-021-01015-1

Future Directions of Chemical Theory and Computation

Yuyuan Lu, Geng Deng, Zhigang Shuai
Pure and Applied Chemistry, 2021.
DOI: 10.1515/pac-2020-1006

Integrating Machine Learning into Protein-Ligand Scoring Function Development

Jianing Lu
2020.

Development of a Machine Learning Potential for Nucleotides in Water

Riccardo Martina
, 57.

Machine Learning for Chemical Reactions

Markus Meuwly
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.1c00033

Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations

April M. Miksch, Tobias Morawietz, Johannes Kaestner, Alexander Urban, Nongnuch Artrith
Machine Learning-Science and Technology, 2021, 2 (3), 031001.
DOI: 10.1088/2632-2153/abfd96

Membrane Models for Molecular Simulations of Peripheral Membrane Proteins

Mahmoud Moqadam, Thibault Tubiana, Emmanuel E. Moutoussamy, Nathalie Reuter
Advances in Physics-X, 2021, 6 (1), 1932589.
DOI: 10.1080/23746149.2021.1932589

Machine Learning-Accelerated Quantum Mechanics-Based Atomistic Simulations for Industrial Applications

Tobias Morawietz, Nongnuch Artrith
Journal of Computer-Aided Molecular Design, 2021, 35 (4), 557–586.
DOI: 10.1007/s10822-020-00346-6

Physics-Inspired Structural Representations for Molecules and Materials

Felix Musil, Andrea Grisafi, Albert P. Bartók, Christoph Ortner, Gábor Csányi, Michele Ceriotti
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.1c00021

Computational Predictions of the Thermal Conductivity of Solids and Liquids

Marcello Puligheddu
2020.

RANDOM PHASE APPROXIMATION AND BEYOND: FROM THEORY TO REALISTIC MATERIALS

Dario Rocca
2020.

Theoretical Insights into the Surface Physics and Chemistry of Redox-Active Oxides

Roger Rousseau, Vassiliki-Alexandra Glezakou, Annabella Selloni
Nature Reviews Materials, 2020, 5 (6), 460–475.
DOI: 10.1038/s41578-020-0198-9

Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights

Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko
Machine Learning Meets Quantum Physics, 2020, 277–307.

Interatomic Potential for Li-C Systems from Cluster Expansion to Artificial Neural Network Techniques

Yusuf Shaidu
2020.

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

Yunqi Shao, Lisanne Knijff, Florian M. Dietrich, Kersti Hermansson, Chao Zhang
Batteries \& Supercaps, 2021, 4 (4), 585–595.
DOI: 10.1002/batt.202000262

Neural Network for the Prediction of Force Differences between an Amino Acid in Solution and Vacuum

Gopal Narayan Srivastava
2020.

Machine Learning Force Fields

Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.0c01111

Challenges for Machine Learning Force Fields in Reproducing Potential Energy Surfaces of Flexible Molecules

Valentin Vassilev-Galindo, Gregory Fonseca, Igor Poltavsky, Alexandre Tkatchenko
Journal of Chemical Physics, 2021, 154 (9), 094119.
DOI: 10.1063/5.0038516

Force Field Development and Nanoreactor Chemistry

Lee-Ping Wang
Computational Approaches for Chemistry Under Extreme Conditions, 2019, 127–159.

Investigations of Water/Oxide Interfaces by Molecular Dynamics Simulations

Ruiyu Wang, Michael L. Klein, Vincenzo Carnevale, Eric Borguet
Wiley Interdisciplinary Reviews-Computational Molecular Science, 2021, e1537.
DOI: 10.1002/wcms.1537

Physics-Guided Deep Learning for Dynamical Systems: A Survey

Rui Wang
2021.

Integrating Machine Learning with Physics-Based Modeling

E Weinan, Jiequn Han, Zhang Linfeng
2020.

Machine Learning and Computational Mathematics

E. Weinan
Communications in Computational Physics, 2020, 28 (5), 1639–1670.
DOI: 10.4208/cicp.OA-2020-0185

Machine Learning for Electronically Excited States of Molecules

Julia Westermayr, Philipp Marquetand
Chemical Reviews, 2020.
DOI: 10.1021/acs.chemrev.0c00749

Integrating Physics-Based Modeling with Machine Learning: A Survey

J Willard, X Jia, S Xu, M Steinbach, V Kumar
arxiv.org, 2021.

Deep Learning Methods for the Design and Understanding of Solid Materials

Tian Xie
2020.

Perspective on Computational Reaction Prediction Using Machine Learning Methods in Heterogeneous Catalysis

Jiayan Xu, Xiao-Ming Cao, P. Hu
Physical Chemistry Chemical Physics, 2021, 23 (19), 11155–11179.
DOI: 10.1039/d1cp01349a

Recent Progress on Multiscale Modeling of Electrochemistry

Xiao‐Hui Yang, Yong‐Bin Zhuang, Jia‐Xin Zhu, Jia‐Bo Le, Jun Cheng
WIREs Computational Molecular Science, 2021.
DOI: 10.1002/wcms.1559

Machine Learning of Coarse-Grained Models for Organic Molecules and Polymers: Progress, Opportunities, and Challenges

Huilin Ye, Weikang Xian, Ying Li
ACS omega, 2021, 6 (3), 1758–1772.
DOI: 10.1021/acsomega.0c05321

Machine Learning for Multi-Scale Molecular Modeling: Theories, Algorithms, and Applications

L Zhang
2020.

Global Optimization of Chemical Cluster Structures: Methods, Applications, and Challenges

Jun Zhang, Vassiliki-Alexandra Glezakou
International Journal of Quantum Chemistry, 2021, 121 (7), e26553.
DOI: 10.1002/qua.26553

Non-Contact Ultrasound

Xiang Zhang
2019.

京ICP备20010051号-8
© 2021 – 2023 DeepModeling
3k 10 mins.
Powered by Hexo & NexT.Gemini
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\ No newline at end of file +Reviews | DeepModeling

DeepModeling

Define the future of scientific computing together

Reviews

Discovery and Implementation of Fast, Accurate and Transferable Many-Body Interatomic Potentials

Adarsh Balasubramanian
2019.

Four Generations of High-Dimensional Neural Network Potentials

Jörg Behler
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.0c00868

Dynamical Processes in the Condensed Phase: Methods and Models

Matthew Ralph Carbone
2021.

Machine Learning and the Physical Sciences

Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, Lenka Zdeborova
Reviews of Modern Physics, 2019, 91 (4), 045002.
DOI: 10.1103/RevModPhys.91.045002

Autonomous Discovery in the Chemical Sciences Part I: Progress

Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Angewandte Chemie-International Edition, 2020, 59 (51), 22858–22893.
DOI: 10.1002/anie.201909987

Designing Models Using Machine Learning: One-Body Reduced Density Matrices and Spectra

Andrea Costamagna
2020.

Interfacial Potentials in Ion Solvation

Carrie Conor Doyle
2020.

Molecular Excited States through a Machine Learning Lens

Pavlo O. Dral, Mario Barbatti
Nature Reviews Chemistry, 2021, 5 (6), 388–405.
DOI: 10.1038/s41570-021-00278-1

Characterizing Magnetic Skyrmions at Their Fundamental Length and Time Scales

Peter Fischer, Sujoy Roy
Magnetic Skyrmions and Their Applications, 2021, 55–97.

Unsupervised Learning Methods for Molecular Simulation Data

Aldo Glielmo, Brooke E. Husic, Alex Rodriguez, Cecilia Clementi, Frank Noé, Alessandro Laio
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.0c01195

The Structure and Dynamics of Materials Using Machine Learning

Mário Rui Gonçalves Marques
2020.

Machine-Learning-Assisted Modeling

Sarah Greenstreet
Physics Today, 2021, 74 (7), 42–47.
DOI: 10.1063/PT.3.4794

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

Andrea Grisafi, David M. Wilkins, Michael J. Willatt, Michele Ceriotti
Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions, 2019, 1–21.

Adaptive Iron-Based Magnetic Nanomaterials of High Performance for Biomedical Applications

Ning Gu, Zuoheng Zhang, Yan Li
Nano Research, 2021.
DOI: 10.1007/s12274-021-3546-1

Deep Learning for Large-Scale Molecular Dynamics and High-Dimensional Partial Differential Equations

Jiequn Han
2018.

Machine Learning for Alloys

Gus L. W. Hart, Tim Mueller, Cormac Toher, Stefano Curtarolo
Nature Reviews Materials, 2021.
DOI: 10.1038/s41578-021-00340-w

Characterizing Performance Improvement of GPUs

Dodi Heryadi, Scott Hampton
Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning), 2019, 1–5.

Physics-Informed Machine Learning

George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, Liu Yang
Nature Reviews Physics, 2021, 3 (6), 422–440.
DOI: 10.1038/s42254-021-00314-5

Neural Network Potentials: A Concise Overview of Methods

Emir Kocer, TW Tsz Wai Ko, Jörg Behler, J Behler
arxiv.org, 2021.

First-Principles Study on the Structural and Thermal Properties of Molecular Crystals and Liquids

Hsin-Yu Ko
2019.

FMO Interfaced with Molecular Dynamics Simulation

Yuto Komeiji, Takeshi Ishikawa
Recent Advances of the Fragment Molecular Orbital Method, 2021, 373–389.

Classical Molecular Dynamics Using Neural Network Representations of Potential Energy Surfaces

Andreas Godø Lefdalsnes
2019.

Modeling Electrified Metal/Water Interfaces from Ab Initio Molecular Dynamics: Structure and Helmholtz Capacitance

Jia-Bo Le, Jun Cheng
Current Opinion in Electrochemistry, 2021, 27, 100693.
DOI: 10/ghtqnk

Molecular Dynamics Study of Charged Nanomaterials: Electrostatics and Self-Assembly

Yaohua Li
2021.

Discovering and Understanding Materials through Computation

Steven G. Louie, Yang-Hao Chan, Felipe H. da Jornada, Zhenglu Li, Diana Y. Qiu
Nature Materials, 2021, 20 (6), 728–735.
DOI: 10.1038/s41563-021-01015-1

Future Directions of Chemical Theory and Computation

Yuyuan Lu, Geng Deng, Zhigang Shuai
Pure and Applied Chemistry, 2021.
DOI: 10.1515/pac-2020-1006

Integrating Machine Learning into Protein-Ligand Scoring Function Development

Jianing Lu
2020.

Development of a Machine Learning Potential for Nucleotides in Water

Riccardo Martina
, 57.

Machine Learning for Chemical Reactions

Markus Meuwly
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.1c00033

Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations

April M. Miksch, Tobias Morawietz, Johannes Kaestner, Alexander Urban, Nongnuch Artrith
Machine Learning-Science and Technology, 2021, 2 (3), 031001.
DOI: 10.1088/2632-2153/abfd96

Membrane Models for Molecular Simulations of Peripheral Membrane Proteins

Mahmoud Moqadam, Thibault Tubiana, Emmanuel E. Moutoussamy, Nathalie Reuter
Advances in Physics-X, 2021, 6 (1), 1932589.
DOI: 10.1080/23746149.2021.1932589

Machine Learning-Accelerated Quantum Mechanics-Based Atomistic Simulations for Industrial Applications

Tobias Morawietz, Nongnuch Artrith
Journal of Computer-Aided Molecular Design, 2021, 35 (4), 557–586.
DOI: 10.1007/s10822-020-00346-6

Physics-Inspired Structural Representations for Molecules and Materials

Felix Musil, Andrea Grisafi, Albert P. Bartók, Christoph Ortner, Gábor Csányi, Michele Ceriotti
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.1c00021

Computational Predictions of the Thermal Conductivity of Solids and Liquids

Marcello Puligheddu
2020.

RANDOM PHASE APPROXIMATION AND BEYOND: FROM THEORY TO REALISTIC MATERIALS

Dario Rocca
2020.

Theoretical Insights into the Surface Physics and Chemistry of Redox-Active Oxides

Roger Rousseau, Vassiliki-Alexandra Glezakou, Annabella Selloni
Nature Reviews Materials, 2020, 5 (6), 460–475.
DOI: 10.1038/s41578-020-0198-9

Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights

Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko
Machine Learning Meets Quantum Physics, 2020, 277–307.

Interatomic Potential for Li-C Systems from Cluster Expansion to Artificial Neural Network Techniques

Yusuf Shaidu
2020.

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

Yunqi Shao, Lisanne Knijff, Florian M. Dietrich, Kersti Hermansson, Chao Zhang
Batteries \& Supercaps, 2021, 4 (4), 585–595.
DOI: 10.1002/batt.202000262

Neural Network for the Prediction of Force Differences between an Amino Acid in Solution and Vacuum

Gopal Narayan Srivastava
2020.

Machine Learning Force Fields

Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.0c01111

Challenges for Machine Learning Force Fields in Reproducing Potential Energy Surfaces of Flexible Molecules

Valentin Vassilev-Galindo, Gregory Fonseca, Igor Poltavsky, Alexandre Tkatchenko
Journal of Chemical Physics, 2021, 154 (9), 094119.
DOI: 10.1063/5.0038516

Force Field Development and Nanoreactor Chemistry

Lee-Ping Wang
Computational Approaches for Chemistry Under Extreme Conditions, 2019, 127–159.

Investigations of Water/Oxide Interfaces by Molecular Dynamics Simulations

Ruiyu Wang, Michael L. Klein, Vincenzo Carnevale, Eric Borguet
Wiley Interdisciplinary Reviews-Computational Molecular Science, 2021, e1537.
DOI: 10.1002/wcms.1537

Physics-Guided Deep Learning for Dynamical Systems: A Survey

Rui Wang
2021.

Integrating Machine Learning with Physics-Based Modeling

E Weinan, Jiequn Han, Zhang Linfeng
2020.

Machine Learning and Computational Mathematics

E. Weinan
Communications in Computational Physics, 2020, 28 (5), 1639–1670.
DOI: 10.4208/cicp.OA-2020-0185

Machine Learning for Electronically Excited States of Molecules

Julia Westermayr, Philipp Marquetand
Chemical Reviews, 2020.
DOI: 10.1021/acs.chemrev.0c00749

Integrating Physics-Based Modeling with Machine Learning: A Survey

J Willard, X Jia, S Xu, M Steinbach, V Kumar
arxiv.org, 2021.

Deep Learning Methods for the Design and Understanding of Solid Materials

Tian Xie
2020.

Perspective on Computational Reaction Prediction Using Machine Learning Methods in Heterogeneous Catalysis

Jiayan Xu, Xiao-Ming Cao, P. Hu
Physical Chemistry Chemical Physics, 2021, 23 (19), 11155–11179.
DOI: 10.1039/d1cp01349a

Recent Progress on Multiscale Modeling of Electrochemistry

Xiao‐Hui Yang, Yong‐Bin Zhuang, Jia‐Xin Zhu, Jia‐Bo Le, Jun Cheng
WIREs Computational Molecular Science, 2021.
DOI: 10.1002/wcms.1559

Machine Learning of Coarse-Grained Models for Organic Molecules and Polymers: Progress, Opportunities, and Challenges

Huilin Ye, Weikang Xian, Ying Li
ACS omega, 2021, 6 (3), 1758–1772.
DOI: 10.1021/acsomega.0c05321

Machine Learning for Multi-Scale Molecular Modeling: Theories, Algorithms, and Applications

L Zhang
2020.

Global Optimization of Chemical Cluster Structures: Methods, Applications, and Challenges

Jun Zhang, Vassiliki-Alexandra Glezakou
International Journal of Quantum Chemistry, 2021, 121 (7), e26553.
DOI: 10.1002/qua.26553

Non-Contact Ultrasound

Xiang Zhang
2019.

京ICP备20010051号-8
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\ No newline at end of file diff --git a/search.xml b/search.xml index 994882d..d2eff82 100644 --- a/search.xml +++ b/search.xml @@ -25,7 +25,7 @@ The history of the DeepModeling communityThe "DeepModeling Community" The short-term plan and long-term vision of the DeepModeling communityIn the short term, developers in the DeepModeling community will focus on atomic-scale simulation methods and tools. This includes solving the many-body Schrödinger equation, electronic structure calculation, molecular dynamics simulation, and coarse-grained molecular dynamics simulation. This also includes tasks such as data generation, model training, high-performance optimization, etc. In addition, it includes different workflows and management tools, as well as computing power scheduling tools for different systems, different scenarios, and different purposes. It should be pointed out that the combination of physical modeling and machine learning often fundamentally changes the implementation logic of a piece of software. Therefore, the new infrastructure will not be settled once and for all, but will be gradually improved through an iterative process and upgrades from time to time. In the long run, the DeepModeling community is committed to combining physical models at all scales with machine learning methods, using the most cutting-edge computing platforms to solve the most challenging scientific and technological problems faced by the human society. -How can you contribute? If you want to contribute to an existing project in the DeepModeling community, please just do so or contactthe corresponding developer directly; if you want to open a new project in the DeepModeling community, or if you want the DeepModeling community to help develop your project, just contact contact@deepmodeling.org. +How can you contribute? If you want to contribute to an existing project in the DeepModeling community, please just do so or contactthe corresponding developer directly; if you want to open a new project in the DeepModeling community, or if you want the DeepModeling community to help develop your project, just contact contact@deepmodeling.org. If you are a programmer who loves science and is attracted by the future scientific computing platform built by the DeepModeling community, you can contribute not only through new algorithms, but also code development specifications, document writing specifications, community databases, task scheduling, workflow management and other tools. In addition, you can contribute to code architecture design and high-performance optimization tasks in the DeepModeling community. People in the field of scientific computing will greatly appreciate your expertise and contribution. If you are a hardcore developer familiar with topics such as electronic structure calculations, molecular dynamics, and finite element methods, the DeepModeling community will be your place to showcase your talents. The addition of machine learning components requires us to rethink about architecture design, each specific implementation for the tasks mentioned above and high-performance optimization. You will become important bridges that connect other developers, contributors, and users in different areas. If you have only used some basic scientific software and have worked on some post-processing scripts, the DeepModeling community also needs you. Try to ask questions and communicate on github/gitee and other communication platforms, try to give opinions, and try to fork, commit, pr... Your little by little contribution will make the DeepModeling community better and better, and the DeepModeling community will be very grateful for such contributions. @@ -33,6 +33,49 @@ Even if you are just a bystander, if you support the concept of the DeepModeling Final remarksDespite the tremendous advances in AI and computing power, the scientific computing community is largely embedded in an old-fashioned culture. Many of the most important tasks rely on legacy codes. The core algorithms used in many commercial software have been outdated. The self-sufficient style of work is similar to that of the agricultural agesresulting in poor efficiency. It is only in recent years that some promising open-source communities have emerged. However, these communities are often aimed at specific tools for specific scales, and are often maintained by specific academic research groups. They face serious challenges in terms of continuous development and improved user experience. The DeepModeling project promises to change all that. The combination of machine learning and physical modeling calls for a new paradigm, the open-source community paradigm. Such a paradigm has long been embraced in the computer and electronics industry, with Linux and Andriod being the very well-known examples. In this sense, what the DeepModeling project does is to borrow these ideas and use them for scientific computing. For people in computational science and engineering, efficient and reusable modeling tools that can be continuously improved will free researchers from the plight of no model or with only ad hoc models. For those who work on machine learning, the world of physical models will provide a relatively new and surely vast playground. Working together as an open-source community will make our work more productive, up to date, reliable, and transparent. The spirit of close collaboration, of respect and building on each other’s work will surely inspire more and more people to join the cause of advancing computing for the benefit of the human society. This is an exciting opportunity. This is the future of scientific computing! +]]>The OpenLAM Initiative/blog/openlam/DP Tutorial 1: How to Setup a DeePMD-kit Training within 5 Minutes?/blog/tutorial1/https://deepmodeling.com/blog/papers/index.html2023-11-28monthly0.6https://deepmodeling.com/blog/2022_csi_workshop/2022-07-07monthly0.6https://deepmodeling.com/blog/papers/dpgen/index.html2022-05-10monthly0.6https://deepmodeling.com/blog/papers/deepmd-kit/index.html2022-04-30monthly0.6https://deepmodeling.com/blog/papers/others.html2021-08-08monthly0.6https://deepmodeling.com/blog/papers/reviews.html2021-08-08monthly0.6https://deepmodeling.com/blog/tutorial2/2021-07-05monthly0.6https://deepmodeling.com/blog/categories/index.html2021-06-14monthly0.6https://deepmodeling.com/blog/tutorial1/2021-06-11monthly0.6https://deepmodeling.com/blog/manifesto/2021-06-09monthly0.6https://deepmodeling.com/blog2023-11-28daily1.0https://deepmodeling.com/blog/tags/DeePMD-kit/2023-11-28weekly0.2https://deepmodeling.com/blog/categories/tutorial/2023-11-28weekly0.2 \ No newline at end of file +https://deepmodeling.com/blog/papers/index.html2023-12-02monthly0.6https://deepmodeling.com/blog/openlam/2023-11-30monthly0.6https://deepmodeling.com/blog/2022_csi_workshop/2022-07-07monthly0.6https://deepmodeling.com/blog/papers/dpgen/index.html2022-05-10monthly0.6https://deepmodeling.com/blog/papers/deepmd-kit/index.html2022-04-30monthly0.6https://deepmodeling.com/blog/papers/others.html2021-08-08monthly0.6https://deepmodeling.com/blog/papers/reviews.html2021-08-08monthly0.6https://deepmodeling.com/blog/tutorial2/2021-07-05monthly0.6https://deepmodeling.com/blog/categories/index.html2021-06-14monthly0.6https://deepmodeling.com/blog/tutorial1/2021-06-11monthly0.6https://deepmodeling.com/blog/manifesto/2021-06-09monthly0.6https://deepmodeling.com/blog2023-12-02daily1.0https://deepmodeling.com/blog/tags/DeePMD-kit/2023-12-02weekly0.2https://deepmodeling.com/blog/categories/tutorial/2023-12-02weekly0.2 \ No newline at end of file diff --git a/tags/DeePMD-kit/index.html b/tags/DeePMD-kit/index.html index b2da086..c464464 100644 --- a/tags/DeePMD-kit/index.html +++ b/tags/DeePMD-kit/index.html @@ -1 +1 @@ -Tag: DeePMD-kit | DeepModeling

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\ No newline at end of file diff --git a/tutorial1/index.html b/tutorial1/index.html index 4d0b5d0..3e930a9 100644 --- a/tutorial1/index.html +++ b/tutorial1/index.html @@ -1,5 +1,5 @@ -DP Tutorial 1: How to Setup a DeePMD-kit Training within 5 Minutes? | DeepModeling

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DP Tutorial 1: How to Setup a DeePMD-kit Training within 5 Minutes?

Posted on In Word count in article: 949 Reading time ≈ 3 mins.

DeePMD-kit is a software to implement Deep Potential. There is a lot of information on the Internet, but there are not so many tutorials for the new hand, and the official guide is too long. Today, I'll take you 5 minutes to get started with DeePMD-kit.

Let's take a look at the training process of DeePMD-kit:

+DP Tutorial 1: How to Setup a DeePMD-kit Training within 5 Minutes? | DeepModeling
  
DeepModeling
Define the future of scientific computing together
DP Tutorial 1: How to Setup a DeePMD-kit Training within 5 Minutes?
 Posted on   In    Word count in article: 949  Reading time ≈ 3 mins.
DeePMD-kit is a software to implement Deep Potential. There is a lot of information on the Internet, but there are not so many tutorials for the new hand, and the official guide is too long. Today, I'll take you 5 minutes to get started with DeePMD-kit.
Let's take a look at the training process of DeePMD-kit:
 graph LR
 A[Prepare data] --> B[Training]
 B --> C[Freeze the model]
-
What? Only three steps? Yes, it's that simple.
  1. Preparing data is converting the computational results of DFT to data that can be recognized by the DeePMD-kit.
  2. Training is train a Deep Potential model using the DeePMD-kit with data prepared in the previous step.
  3. Finally, what we need to do is to freeze the restart file in the training process into a model, in other words is to extract the neural network parameters into a file for subsequent use. I believe you can't wait to get started. Let's go!
1. Preparing Data
The data format of the DeePMD-kit is introduced in the official document but seems complex. Don't worry, I'd like to introduce a data processing tool: dpdata! You can use only one line Python scripts to process data. So easy!
1
2
import dpdata
dpdata.LabeledSystem('OUTCAR').to('deepmd/npy', 'data', set_size=200)
In this example, we converted the computational results of the VASP in the OUTCAR to the data format of the DeePMD-kit and saved in to a directory named data, where npy is the compressed format of the numpy, which is required by the DeePMD-kit training. We assume OUTCAR stores 1000 frames of molecular dynamics trajectory, then where will be 1000 points after converting. set_size=200 means these 1000 points will be divided into 5 subsets, which is named as data/set.000~data/set.004, respectively. The size of each set is 200. In these 5 sets, data/set.000~data/set.003 will be considered as the training set by the DeePMD-kit, and data/set.004 will be considered as the test set. The last set will be considered as the test set by the DeePMD-kit by default. If there is only one set, the set will be both the training set and the test set. (Of course, such test set is meaningless.)
2. Training
It's required to prepare an input script to start the DeePMD-kit training. Are you still out of the fear of being dominated by INCAR script? Don't worry, it's much easier to configure the DeePMD-kit than configuring the VASP. First, let's download an example and save to input.json:
1
wget https://raw.githubusercontent.com/deepmodeling/deepmd-kit/v1.3.3/examples/water/train/water_se_a.json -O input.json
The strength of the DeePMD-kit is that the same training parameters are suitable for different systems, so we only need to slightly modify input.json to start training. Here is the first parameter to modify:
1
"type_map":     ["O", "H"],
In the DeePMD-kit data, each atom type is numbered as an integer starting from 0. The parameter gives an element name to each atom in the numbering system. Here, we can copy from the content of data/type_map.raw. For example,
1
"type_map":    ["A", "B","C"],
Next, we are going to modify the neighbour searching parameter:
1
"sel":       [46, 92],
Each number in this list gives the maximum number of atoms of each type among neighbor atoms of an atom. For example, 46 means there are at most 46 O (type 0) neighbours. Here, our elements were modified to A, B, and C, so this parameters is also required to modify. What to do if you don't know the maximum number of neighbors? You can be roughly estimate one by the density of the system, or try a number blindly. If it is not big enough, the DeePMD-kit will shoot WARNINGS. Below we changed it to
1
"sel":       [64, 64, 64]
In addtion, we need to modify
1
"systems":     ["../data/"],
to
1
"systems":     ["./data/"],
It is because that the directory to write to is ./data/ in the current directory. Here I'd like to introduce the definition of the data system. The DeePMD-kit considers that data with corresponding element types and atomic numbers form a system. Our data is generated from a molecular dynamics simulation and meets this condition, so we can put them into one system. Dpdata works the same way. If data cannot be put into a system, multiple systems is required to be set as a list here:
1
2
"training": {
       "systems": ["system1", "system2"]
Finnally, we are likely to modify another two parameters:
1
2
"stop_batch":   1000000,
"batch_size":   1,
stop_batch is the numebr of training step using the SGD method of deep learning, and batch_size is the mini-batch size of data in each step.
If we want to reduce stop_batch and use batch_size that the DeePMD-kit recommends, we can use
1
2
"stop_batch":   500000,
"batch_size":   "auto",
Now we have succesfully set a input file! To start training, we execuate
1
dp train input.json
and wait for results. During the training process, we can see lcurve.out to observe the error reduction.Among them, Column 4 and 5 are the test and training errors of energy (normalized by the number of atoms), and Column 6 and 7 are the test and training errors of the force.
3. Freeze the Model
After training, we can use the following script to freeze the model:
1
dp freeze
The default filename of the output model is frozen_model.pb. As so, we have got a good or bad DP model. As for the reliability of this model and how to use it, I will give you a detailed tutorial in the next post.
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+

What? Only three steps? Yes, it's that simple.

  1. Preparing data is converting the computational results of DFT to data that can be recognized by the DeePMD-kit.
  2. Training is train a Deep Potential model using the DeePMD-kit with data prepared in the previous step.
  3. Finally, what we need to do is to freeze the restart file in the training process into a model, in other words is to extract the neural network parameters into a file for subsequent use. I believe you can't wait to get started. Let's go!

1. Preparing Data

The data format of the DeePMD-kit is introduced in the official document but seems complex. Don't worry, I'd like to introduce a data processing tool: dpdata! You can use only one line Python scripts to process data. So easy!

1
2
import dpdata
dpdata.LabeledSystem('OUTCAR').to('deepmd/npy', 'data', set_size=200)

In this example, we converted the computational results of the VASP in the OUTCAR to the data format of the DeePMD-kit and saved in to a directory named data, where npy is the compressed format of the numpy, which is required by the DeePMD-kit training. We assume OUTCAR stores 1000 frames of molecular dynamics trajectory, then where will be 1000 points after converting. set_size=200 means these 1000 points will be divided into 5 subsets, which is named as data/set.000~data/set.004, respectively. The size of each set is 200. In these 5 sets, data/set.000~data/set.003 will be considered as the training set by the DeePMD-kit, and data/set.004 will be considered as the test set. The last set will be considered as the test set by the DeePMD-kit by default. If there is only one set, the set will be both the training set and the test set. (Of course, such test set is meaningless.)

2. Training

It's required to prepare an input script to start the DeePMD-kit training. Are you still out of the fear of being dominated by INCAR script? Don't worry, it's much easier to configure the DeePMD-kit than configuring the VASP. First, let's download an example and save to input.json:

1
wget https://raw.githubusercontent.com/deepmodeling/deepmd-kit/v1.3.3/examples/water/train/water_se_a.json -O input.json

The strength of the DeePMD-kit is that the same training parameters are suitable for different systems, so we only need to slightly modify input.json to start training. Here is the first parameter to modify:

1
"type_map":     ["O", "H"],

In the DeePMD-kit data, each atom type is numbered as an integer starting from 0. The parameter gives an element name to each atom in the numbering system. Here, we can copy from the content of data/type_map.raw. For example,

1
"type_map":    ["A", "B","C"],

Next, we are going to modify the neighbour searching parameter:

1
"sel":       [46, 92],

Each number in this list gives the maximum number of atoms of each type among neighbor atoms of an atom. For example, 46 means there are at most 46 O (type 0) neighbours. Here, our elements were modified to A, B, and C, so this parameters is also required to modify. What to do if you don't know the maximum number of neighbors? You can be roughly estimate one by the density of the system, or try a number blindly. If it is not big enough, the DeePMD-kit will shoot WARNINGS. Below we changed it to

1
"sel":       [64, 64, 64]

In addtion, we need to modify

1
"systems":     ["../data/"],

to

1
"systems":     ["./data/"],

It is because that the directory to write to is ./data/ in the current directory. Here I'd like to introduce the definition of the data system. The DeePMD-kit considers that data with corresponding element types and atomic numbers form a system. Our data is generated from a molecular dynamics simulation and meets this condition, so we can put them into one system. Dpdata works the same way. If data cannot be put into a system, multiple systems is required to be set as a list here:

1
2
"training": {
       "systems": ["system1", "system2"]

Finnally, we are likely to modify another two parameters:

1
2
"stop_batch":   1000000,
"batch_size":   1,

stop_batch is the numebr of training step using the SGD method of deep learning, and batch_size is the mini-batch size of data in each step.
If we want to reduce stop_batch and use batch_size that the DeePMD-kit recommends, we can use

1
2
"stop_batch":   500000,
"batch_size":   "auto",

Now we have succesfully set a input file! To start training, we execuate

1
dp train input.json

and wait for results. During the training process, we can see lcurve.out to observe the error reduction.Among them, Column 4 and 5 are the test and training errors of energy (normalized by the number of atoms), and Column 6 and 7 are the test and training errors of the force.

3. Freeze the Model

After training, we can use the following script to freeze the model:

1
dp freeze

The default filename of the output model is frozen_model.pb. As so, we have got a good or bad DP model. As for the reliability of this model and how to use it, I will give you a detailed tutorial in the next post.

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\ No newline at end of file diff --git a/tutorial2/index.html b/tutorial2/index.html index 0baa33e..01dd4d7 100644 --- a/tutorial2/index.html +++ b/tutorial2/index.html @@ -1 +1 @@ -DP Tutorial 2: DeePMD-kit: Install with Conda & Offline Packages & Docker | DeepModeling

DeepModeling

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DP Tutorial 2: DeePMD-kit: Install with Conda & Offline Packages & Docker

Posted on In Word count in article: 305 Reading time ≈ 1 mins.

Do you prepare to read a long article before clicking the tutorial? Since we can teach you how to setup a DeePMD-kit training in 5 minutes, we can also teach you how to install DeePMD-kit in 5 minutes. The installation manual will be introduced as follows:

Install with conda

After you install conda, you can install the CPU version with the following command:

1
conda install deepmd-kit=*=*cpu lammps-dp=*=*cpu -c deepmodeling

To install the GPU version containing CUDA 10.1:

1
conda install deepmd-kit=*=*gpu lammps-dp=*=*gpu -c deepmodeling

If you want to use the specific version, just replace * with the version:

1
conda install deepmd-kit=1.3.3=*cpu lammps-dp=1.3.3=*cpu -c deepmodeling

Install with offline packages

Download offline packages in the Releases page, or use wget:

1
wget https://github.com/deepmodeling/deepmd-kit/releases/download/v1.3.3/deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh -O deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh

Take an example of v1.3.3. Execuate the following commands and just follow the prompts.

1
sh deepmd-kit-1.3.1-cuda10.1_gpu-Linux-x86_64.sh

With Docker

To pull the CPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cpu
To pull the GPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cuda10.1_gpu

Tips

dp is the program of DeePMD-kit and lmp is the program of LAMMPS.

1
2
dp -h
lmp -h

GPU version has contained CUDA Toolkit. Note that different CUDA versions support different NVIDIA driver versions. See NVIDIA documents for details.

Don't hurry up and try such a convenient installation process. But I still want to remind everyone that the above installation methods only support the official version released by DeePMD-kit. If you need to use the devel version, you still need to go through a long compilation process. Please refer to the installation manual.

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\ No newline at end of file +DP Tutorial 2: DeePMD-kit: Install with Conda & Offline Packages & Docker | DeepModeling

DeepModeling

Define the future of scientific computing together

DP Tutorial 2: DeePMD-kit: Install with Conda & Offline Packages & Docker

Posted on In Word count in article: 305 Reading time ≈ 1 mins.

Do you prepare to read a long article before clicking the tutorial? Since we can teach you how to setup a DeePMD-kit training in 5 minutes, we can also teach you how to install DeePMD-kit in 5 minutes. The installation manual will be introduced as follows:

Install with conda

After you install conda, you can install the CPU version with the following command:

1
conda install deepmd-kit=*=*cpu lammps-dp=*=*cpu -c deepmodeling

To install the GPU version containing CUDA 10.1:

1
conda install deepmd-kit=*=*gpu lammps-dp=*=*gpu -c deepmodeling

If you want to use the specific version, just replace * with the version:

1
conda install deepmd-kit=1.3.3=*cpu lammps-dp=1.3.3=*cpu -c deepmodeling

Install with offline packages

Download offline packages in the Releases page, or use wget:

1
wget https://github.com/deepmodeling/deepmd-kit/releases/download/v1.3.3/deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh -O deepmd-kit-1.3.3-cuda10.1_gpu-Linux-x86_64.sh

Take an example of v1.3.3. Execuate the following commands and just follow the prompts.

1
sh deepmd-kit-1.3.1-cuda10.1_gpu-Linux-x86_64.sh

With Docker

To pull the CPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cpu
To pull the GPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:1.2.2_cuda10.1_gpu

Tips

dp is the program of DeePMD-kit and lmp is the program of LAMMPS.

1
2
dp -h
lmp -h

GPU version has contained CUDA Toolkit. Note that different CUDA versions support different NVIDIA driver versions. See NVIDIA documents for details.

Don't hurry up and try such a convenient installation process. But I still want to remind everyone that the above installation methods only support the official version released by DeePMD-kit. If you need to use the devel version, you still need to go through a long compilation process. Please refer to the installation manual.

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