Welcome to mlcg’s documentation!¶
This is the base code to create models such as the ones described in [TransCGSchnet] and [CGSchnet].
This repository collects a set of tools to apply machine learning techniques to coarse grain atomic systems.
Installation¶
First we suggest to create a new clean empty virtual environment with python 3.12, then clone the repo and install the following prerequisites:
git clone git@github.com:ClementiGroup/mlcg.git
cd mlcg
pip install -r env_with_hashes.in
pip install --no-deps git+https://github.com/ACEsuit/mace.git@v0.3.13
pip install --no-deps nequip==0.12.1 nequip-allegro==0.7.0
Then install this repository with:
pip install .
This will likely rise an error due to some dependency issue about e3nn that you can safely ignore.
Examples¶
Please take a look into the examples folder of the repository to see how to use this code to train a model over an existing dataset.
CLI¶
The models defined in this library can be conveniently trained using the pytorch-lightning CLI utilities.