Coarse graining for molecular dymamics (preprint)
Required:
numpypytorch(1.2 or higher)scipy
Optional:
mdtraj(forcgnet.moleculeonly)pandas(forcgnet.moleculeonly)sklearn(for testing)Jupyter(forexamples)matplotlib(forexamples)
Clone the repository:
git clone git@github.com:coarse-graining/cgnet.git
Install any missing dependencies, and then run:
cd cgnet
python setup.py install
For compatibility with pytorch==1.1, please use the pytorch-1.1 branch. This branch currently does not include the updates for variable size and Langevin dynamics, nor some normalization options.
Please cite the preprint:
@article{husic2020coarse,
title={Coarse Graining Molecular Dynamics with Graph Neural Networks},
author={Husic, Brooke E and Charron, Nicholas E and Lemm, Dominik and Wang, Jiang and Pérez, Adrià and Krämer, Andreas and Chen, Yaoyi and Olsson, Simon and de Fabritiis, Gianni and Noé, Frank and Clementi, Cecilia},
journal={arXiv preprint arXiv:2007.11412},
year={2020}
}Various methods are based off the following papers. CGnet:
@article{wang2019machine,
title={Machine learning of coarse-grained molecular dynamics force fields},
author={Wang, Jiang and Olsson, Simon and Wehmeyer, Christoph and Pérez, Adrià and Charron, Nicholas E and de Fabritiis, Gianni and Noé, Frank and Clementi, Cecilia},
journal={ACS Central Science},
year={2019},
publisher={ACS Publications},
doi={10.1021/acscentsci.8b00913}
}SchNet:
@article{schutt2018schnetpack,
title={SchNetPack: A deep learning toolbox for atomistic systems},
author={Schutt, KT and Kessel, Pan and Gastegger, Michael and Nicoli, KA and Tkatchenko, Alexandre and Müller, K-R},
journal={Journal of Chemical Theory and Computation},
volume={15},
number={1},
pages={448--455},
year={2018},
publisher={ACS Publications}
}