Physic-informed Neural-Network Software for Molecular Dynamics Applications

11/06/2020

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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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Physic-informed Neural-Network Software for Molecular Dynamics Applications

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Physic-informed Neural-Network Software for Molecular Dynamics Applications

Related Research

Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks

Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture

Solving higher-order Lane-Emden-Fowler type equations using physics-informed neural networks: benchmark tests comparing soft and hard constraints

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