This repository contains a collection of resources and papers on materials discovery. If you have any relevant paper or codes to update the list, please pull a request or report an issue.
Capturing long-range interaction with reciprocal space neural network
Hongyu Yu, Liangliang Hong, Shiyou Chen, Xingao Gong, Hongjun Xiang
Arxiv 2022. [paper]
30 Nov 2022
Resolving the data ambiguity for periodic crystals
Daniel Widdowson, Vitaliy Kurlin
NeurIPS 2022. [paper]
18 Sep 2022
Periodic Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Yi Liu, Yuchao Lin, Shuiwang Ji
NeurIPS 2022. [paper]
23 Sep 2022
In-context Learning with Transformer Is Really Equivalent to a Contrastive Learning Pattern
Ruifeng Ren, Yong Liu
Arxiv 2023. [paper]
20 Oct 2023
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, Shuiwang Ji
Arxiv 2023. [paper]
7 Nov 2023
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji
ICLR 2024. [paper]
18 Mar 2024
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction
Haomin Yu, Yanru Song, Jilin Hu, Chenjuan Guo, Bin Yang
Arxiv 2023. [paper]
8 Jun 2023
Pretraining Strategies for Structure Agnostic Material Property Prediction
Hongshuo Huang, Rishikesh Magar, Amir Barati Farimani
Journal of Chemical Information and Modeling 2024. [paper]
February 1, 2024
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Nima Shoghi, Adeesh Kolluru, John R. Kitchin, Zachary W. Ulissi, C. Lawrence Zitnick, Brandon M. Wood
Arxiv 2024. [paper]
6 May 2024
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M. Elena, Dávid P. Kovács, Janosh Riebesell, Xavier R. Advincula, Mark Asta, Matthew Avaylon, William J. Baldwin, Fabian Berger, Noam Bernstein, Arghya Bhowmik, Samuel M. Blau, Vlad Cărare, James P. Darby, Sandip De, Flaviano Della Pia, Volker L. Deringer, Rokas Elijošius, Zakariya El-Machachi, Fabio Falcioni, Edvin Fako, Andrea C. Ferrari, Annalena Genreith-Schriever, Janine George, Rhys E. A. Goodall, Clare P. Grey, Petr Grigorev, Shuang Han, Will Handley, Hendrik H. Heenen, Kersti Hermansson, Christian Holm, Jad Jaafar, Stephan Hofmann, Konstantin S. Jakob, Hyunwook Jung, Venkat Kapil, Aaron D. Kaplan, Nima Karimitari, James R. Kermode, Namu Kroupa, Jolla Kullgren, Matthew C. Kuner, Domantas Kuryla, Guoda Liepuoniute, Johannes T. Margraf, Ioan-Bogdan Magdău, Angelos Michaelides, J. Harry Moore, Aakash A. Naik, Samuel P. Niblett, Sam Walton Norwood, Niamh O'Neill, Christoph Ortner, Kristin A. Persson, Karsten Reuter, Andrew S. Rosen, Lars L. Schaaf, Christoph Schran, Benjamin X. Shi, Eric Sivonxay, Tamás K. Stenczel, Viktor Svahn, Christopher Sutton, Thomas D. Swinburne, Jules Tilly, Cas van der Oord, Eszter Varga-Umbrich, Tejs Vegge, Martin Vondrák, Yangshuai Wang, William C. Witt, Fabian Zills, Gábor Csányi
Arxiv 2024. [paper]
29 Dec 2023
Capturing long-range interaction with reciprocal space neural network
Hongyu Yu, Liangliang Hong, Shiyou Chen, Xingao Gong, Hongjun Xiang
Arxiv 2024. [paper]
16 Oct 2023
Incompleteness of Atomic Structure Representations
Sergey N. Pozdnyakov, Michael J. Willatt, Albert P. Bartók, Christoph Ortner, Gábor Csányi, and Michele Ceriotti
Physical Review Letters 2022. [paper]
12 Oct 2020
Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery
Anthony K. Cheetham, Ram Seshadri
Chemistry of Materials 2024. [paper]
8 Apr 2024
Dynamic Gaussian Mesh: Consistent Mesh Reconstruction from Monocular Videos
Isabella Liu, Hao Su, Xiaolong Wang
Arxiv 2024. [paper]
22 Apr 2024
A Diffusion-Based Pre-training Framework for Crystal Property Prediction
Zixing Song, Ziqiao Meng, Irwin King
AAAI 2024. [paper]
23 Mar 2024
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction
Haomin Yu, Yanru Song, Jilin Hu, Chenjuan Guo, Bin Yang
Arxiv 2023. [paper]
9 Jun 2023
ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT
Jyotirmoy Deb, Lakshi Saikia, Kripa Dristi Dihingia, G Narahari Sastry
Arxiv 2024. [paper]
16 Oct 2023
SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation
Yen-Chi Cheng, Hsin-Ying Lee, Sergey Tulyakov, Alexander Schwing, Liangyan Gui
CVPR 2023. [paper]
22 Mar 2023
From Text to Insight: Large Language Models for Materials Science Data Extraction
Mara Schilling-Wilhelmi, Martiño Ríos-García, Sherjeel Shabih, María Victoria Gil, Santiago Miret, Christoph T. Koch, José A. Márquez, Kevin Maik Jablonka
Arxiv 2024. [paper]
23 Jul 2024
Multi-modal conditioning for metal-organic frameworks generation using 3D modeling techniques
*Junkil Park, Youhan Lee, Jihan Kim *
Arxiv 2024. [paper]
05 July 2024
MatText: Do Language Models Need More than Text & Scale for Materials Modeling?
Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka
Arxiv 2024. [paper]
28 Jun 2024
Multimodal learning for crystalline materials
Viggo Moro, Charlotte Loh, Rumen Dangovski, Ali Ghorashi, Andrew Ma, Zhuo Chen, Samuel Kim, Peter Y. Lu, Thomas Christensen, Marin Soljačić
Arxiv 2024. [paper]
12 Apr 2024
CrysMMNet: Multimodal Representation for Crystal Property Prediction
Kishalay Das, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly
Arxiv 2024. [paper]
9 Jun 2023
LLM-Prop: Predicting Physical And Electronic Properties of Crystalline Solids From Their Text Descriptions
Andre Niyongabo Rubungo, Craig Arnold, Barry P. Rand, Adji Bousso Dieng
Arxiv 2023. [paper]
21 Oct 2023
Graph-Text Contrastive Learning of Inorganic Crystal Structure toward a Foundation Model of Inorganic Materials
Keisuke Ozawa, Teppei Suzuki , Shunsuke Tonogai, Tomoya Itakura
Arxiv 2024. [paper]
15 April 2024
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary Ulissi
Arxiv 2024. [paper]
6 Feb 2024
Materials science in the era of large language models: a perspective
Ge Lei, Ronan Docherty, Samuel J. Cooper
Arxiv 2024. [paper]
11 Mar 2024
LLMatDesign: Autonomous Materials Discovery with Large Language Models
Shuyi Jia, Chao Zhang, Victor Fung
Arxiv 2024. [paper]
19 Jun 2024
Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering
Hongxuan Liu, Haoyu Yin, Zhiyao Luo, Xiaonan Wang
Arxiv 2024. [paper]
22 Apr 2024
Invariant Tokenization for Language Model Enabled Crystal Materials Generation
Keqiang Yan, Xiner Li, Hongyi Ling, Kenna Ashen, Carl Edwards, Raymundo Arroyave, Marinka Zitnik, Heng Ji, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji
NeurIPS 2024. [paper]
16 Oct 2023
LBNL: A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M. Elena, Dávid P. Kovács, Janosh Riebesell, Xavier R. Advincula, Mark Asta, Matthew Avaylon, William J. Baldwin, Fabian Berger, Noam Bernstein, Arghya Bhowmik, Samuel M. Blau, Vlad Cărare, James P. Darby, Sandip De, Flaviano Della Pia, Volker L. Deringer, Rokas Elijošius, Zakariya El-Machachi, Fabio Falcioni, Edvin Fako, Andrea C. Ferrari, Annalena Genreith-Schriever, Janine George, Rhys E. A. Goodall, Clare P. Grey, Petr Grigorev, Shuang Han, Will Handley, Hendrik H. Heenen, Kersti Hermansson, Christian Holm, Jad Jaafar, Stephan Hofmann, Konstantin S. Jakob, Hyunwook Jung, Venkat Kapil, Aaron D. Kaplan, Nima Karimitari, James R. Kermode, Namu Kroupa, Jolla Kullgren, Matthew C. Kuner, Domantas Kuryla, Guoda Liepuoniute, Johannes T. Margraf, Ioan-Bogdan Magdău, Angelos Michaelides, J. Harry Moore, Aakash A. Naik, Samuel P. Niblett, Sam Walton Norwood, Niamh O'Neill, Christoph Ortner, Kristin A. Persson, Karsten Reuter, Andrew S. Rosen, Lars L. Schaaf, Christoph Schran, Benjamin X. Shi, Eric Sivonxay, Tamás K. Stenczel, Viktor Svahn, Christopher Sutton, Thomas D. Swinburne, Jules Tilly, Cas van der Oord, Eszter Varga-Umbrich, Tejs Vegge, Martin Vondrák, Yangshuai Wang, William C. Witt, Fabian Zills, Gábor Csányi
Arxiv 2024. [paper]
1 Mar 2024
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, Steven Lovegrove
Arxiv 2019. [paper]
16 Jan 2019
Aligning Gradient and Hessian for Neural Signed Distance Function
Ruian Wang, Zixiong Wang, Yunxiao Zhang, Shuangmin Chen, Shiqing Xin, Changhe Tu, Wenping Wang, Great Hall
NeurIPS 2023 [paper]
16 Oct 2023
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi Jaakkola, Jake Smith
ICLR 2024. [paper]
16 Oct 2023
Multi-modal conditioning for metal-organic frameworks generation using 3D modeling techniques
Junkil Park, Youhan Lee, Jihan Kim
Chemrxiv 2024. [paper]
05 July 2024
UniMat: Scalable Diffusion for Materials Generation
Sherry Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, Ekin Dogus Cubuk
Arxiv 2024. [paper]
3 Jun 2024
GenMS: Generative Hierarchical Materials Search
Sherry Yang, Simon Batzner, Ruiqi Gao, Muratahan Aykol, Alexander L. Gaunt, Brendan McMorrow, Danilo J. Rezende, Dale Schuurmans, Igor Mordatch, Ekin D. Cubuk
Arxiv 2024. [paper]
10 Sep 2024