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arXiv · 2011.03490

Physics-informed Neural-Network Software for Molecular Dynamics Applications

Taufeq Mohammed Razakh·Beibei Wang·Shane Jackson·Rajiv K. Kalia·Aiichiro Nakano·Ken-ichi Nomura·Priya Vashishta
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Abstract

We have developed a novel differential equation solver software called PND based on the physics-informed neural network for molecular dynamics simulators. Based on automatic differentiation technique provided by Pytorch, our software allows users to flexibly implement equation of atom motions, initial and boundary conditions, and conservation laws as loss function to train the network. PND comes with a parallel molecular dynamics (MD) engine in order for users to examine and optimize loss function design, and different conservation laws and boundary conditions, and hyperparameters, thereby accelerate the PINN-based development for molecular applications.

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Taufeq Mohammed Razakh, Beibei Wang, Shane Jackson, Rajiv K. Kalia, Aiichiro Nakano, Ken-ichi Nomura, Priya Vashishta. 2020-11-06. Physics-informed Neural-Network Software for Molecular Dynamics Applications. https://arxiv.org/abs/2011.03490

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