  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.
