Auto-differentiable orbital-free density functional theory (OFDFT) package in PyTorch.
Details, documentation and examples can be found at PROFESS-AD's website.
It is recommended for users to create a virtual environemnt to install all the required Python packages. For example, a conda environment,
conda create -n professad python
conda activate professad
To use PROFESS-AD, one can fork or clone this repository and pip install it. The necessary requirements will be installed, including torch and xitorch.
This might take a few minutes.
git clone https://github.com/profess-dev/profess-ad.git
cd profess-ad
pip install .
To check that all the dependencies have been installed correctly, one can perform tests as follows.
cd profess-ad/tests
python -m unittest
C.W. Tan, C.J. Pickard, and W.C. Witt. Automatic Differentiation for Orbital-Free Density Functional Theory. J. Chem. Phys. 158, 124801 (2023)