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