Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All of force computations including bond, angle, dihedral, Lennard-Jones and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab-initio potential, performing an end-to-end training and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool-set to support molecular simulations of machine learning potentials. Code and data are freely available at \url{github.com/torchmd}.
@article{arxiv.2012.12106,
title = {TorchMD: A deep learning framework for molecular simulations},
author = {Stefan Doerr and Maciej Majewsk and Adrià Pérez and Andreas Krämer and Cecilia Clementi and Frank Noe and Toni Giorgino and Gianni De Fabritiis},
journal= {arXiv preprint arXiv:2012.12106},
year = {2021}
}