TorchMD: A deep learning framework for molecular simulations

• URL: http://arxiv.org/abs/2012.12106v1
• Date: Tue, 22 Dec 2020 15:43:27 GMT
• Title: TorchMD: A deep learning framework for molecular simulations
• Authors: Stefan Doerr, Maciej Majewsk, Adri\`a P\'erez, Andreas Kr\"amer, Cecilia Clementi, Frank Noe, Toni Giorgino and Gianni De Fabritiis
• Abstract summary: We present TorchMD, a framework for molecular simulations with mixed classical and machine learning 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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