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.
CLI
The models defined in this library can be conveniently trained using the pytorch-lightning CLI utilities.
Examples
Please take a look into the examples folder to see how to use this code to train a model over an existing dataset.