This code implements HEroBM, a method for reconstructing atomistic structures from coarse-grained (CG) molecular simulations. HEroBM is based on deep equivariant graph neural networks and a hierarchical approach. It is accurate, versatile, and efficient, and can handle any type of CG mapping and is transferable to systems of varying sizes.
1 - Create a new virtual environment with python>=3.8
Suggested version is 3.10
Using conda, the script to run is conda create -n "herobm" python=3.10
Using python, the script to run is python -m venv ./herobm-venv
2 - Activate the newly created virtual environment: conda activate herobm
or source herobm-venv/bin/activate
3 - Clone the GEqTrain repository using git clone https://github.com/limresgrp/GEqTrain.git and go inside GEqTrain folder
4 - Run ./install.sh to install Pytorch, selecting your CUDA version (or using cpu only), and GEqTrain dependencies
5 - Clone the CGMap repository using git clone https://github.com/limresgrp/CGmap.git and go inside the CGMap folder
6 - Run pip install -e . to install cgmap and all its required packages inside your virtual environment
7 - Return inside the HEroBM folder and run pip install -e . to install herobm and all its required packages inside your virtual environment
--- Soon GEqTrain and CGMap repositories will be provided as a Conda package, greatly simplifying the installation process ---