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[Preprint]. 2023 Nov 29:arXiv:2310.03121v2. Originally published 2023 Oct 4. [Version 2]

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, Sukrit Singh, Jason Swails, Philip Turner, Yuanqing Wang, Ivy Zhang, John D Chodera, Gianni De Fabritiis, Thomas E Markland
PMCID: PMC10659447  PMID: 37986730

Abstract

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.

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16 pages, 5 figures

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