Solving Newton's Equations of Motion with Large Timesteps using Recurrent Neural Networks based Operators

2020/04/12 by JCS Kadupitiya, Kadupitiya, JCS, Geoffrey Fox +3 · 2 citations
Computer Science · Physics and Astronomy · #Computational Physics (physics.comp-ph) #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Parallel Computing and Optimization Techniques #Soft Condensed Matter (cond-mat.soft)

paper · pdf · doi:10.48550/arxiv.2004.06493

openalex publication_date 2020/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Classical molecular dynamics simulations are based on solving Newton's equations of motion. Using a small timestep, numerical integrators such as Verlet generate trajectories of particles as solutions to Newton's equations. We introduce operators derived using recurrent neural networks that accurately solve Newton's equations utilizing sequences of past trajectory data, and produce energy-conserving dynamics of particles using timesteps up to 4000 times larger compared to the Verlet timestep. We demonstrate significant speedup in many example problems including 3D systems of up to 16 particles.

Cited by

• Predicting Thermodynamics of Liquid Water from Time Series Analysis
• FlashMD: long-stride, universal prediction of molecular dynamics

Related

• Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers
• Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation
• Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks
• Physics-informed Neural-Network Software for Molecular Dynamics Applications
• NewtonNet: A Newtonian message passing network for deep learning of interatomic potentials and forces