Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics

01 November 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Ab initio molecular dynamics (AIMD) has become one of the most popular and robust approaches for modeling complicated chemical, liquid, and material systems. However, the formidable computational cost often limits its widespread application in simulations of the largest scale systems. The situation becomes even more severe in cases where the hydrogen nuclei may be better described as quantized particles using a path integral representation. Here, we present a computational approach that combines machine learning with recent advances in path integral contraction schemes, and we achieve a two-order-of-magnitude acceleration over direct path integral AIMD simulation while at the same time maintaining its accuracy.

Keywords

Ab Initio
Molecular Dynamcis
AIMD
Theoretical Chemistry
Computation
simulations
nuclear
quantum
Nuclear Quantum
NQE
Newtonian Dynamics

Supplementary materials

Title
Description
Actions
Title
Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics
Description
Simulation Details
Actions

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