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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João PGLM Rodrigues, Jaime Rodriguez-Guerra, Andrew C. Simmonett, Jason Swails, Ivy Zhang, John D. Chodera, Gianni De Fabritiis, Thomas E. Markland
Department of Chemistry, Stanford University, Stanford, CA 94305, USA
title={OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials},
author={Peter Eastman and Raimondas Galvelis and Raúl P. Peláez and Charlles R. A. Abreu and Stephen E. Farr and Emilio Gallicchio and Anton Gorenko and Michael M. Henry and Frank Hu and Jing Huang and Andreas Krämer and Julien Michel and Joshua A. Mitchell and Vijay S. Pande and João PGLM Rodrigues and Jaime Rodriguez-Guerra and Andrew C. Simmonett and Jason Swails and Ivy Zhang and John D. Chodera and Gianni De Fabritiis and Thomas E. Markland},
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.
