论文标题
用于分子动力学应用的物理信息神经网络软件
Physics-informed Neural-Network Software for Molecular Dynamics Applications
论文作者
论文摘要
我们基于分子动力学模拟器的物理信息神经网络开发了一种名为PND的新型微分方程求解器软件。基于Pytorch提供的自动差异化技术，我们的软件允许用户灵活地实现原子运动，初始和边界条件以及保护法作为损失功能来训练网络的损失功能。 PND带有平行分子动力学（MD）发动机，以便用户检查和优化损失功能设计，不同的保护法和边界条件以及超参数，从而加速了基于Pinn的分子应用开发。
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.
