mlcg

This repository collects a set of tools to apply machine learning techniques to coarse grain atomic systems.

Installation

The dependencies are defined in requirements.txt but some packages are not well handled by pip. So start by installing pytorch and pytorch-geometric with conda, e.g.:

conda install pytorch cudatoolkit=11.3 -c pytorch
conda install pyg -c pyg -c conda-forge

Support for the MACE model can be enabled with:

pip install git+https://github.com/felixmusil/mace.git@develop

Support for the TorchMD-Net models can be enabled with:

pip install git+https://github.com/felixmusil/torchmd-net.git

CLI

The models defined in this library can be convinietly trained using the pytorch-lightning cli utilities.

Documentation

Documentation is available here and here are some references on how to work with it.

Dependencies

pip install sphinx sphinx_rtd_theme sphinx-autodoc-typehints

How to build

cd docs
sphinx-build -b html source build

How to update the online documentation

This udapte should be done after any update of the main branch so that the documentation is synchronised with the main version of the repository.

git checkout gh-pages
git rebase main
cd docs
sphinx-build -b html source ./
git commit -a
git push

Test Coverage

The test coverage of this library is monitored with coverage for each pull requests using github actions. To produce a report locally run:

coverage run -m pytest
coverage report