Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations.

Published

Journal Article

Ab initio quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulation is a useful tool to calculate thermodynamic properties such as potential of mean force for chemical reactions but intensely time consuming. In this paper, we developed a new method using the internal force correction for low-level semiempirical QM/MM molecular dynamics samplings with a predefined reaction coordinate. As a correction term, the internal force was predicted with a machine learning scheme, which provides a sophisticated force field, and added to the atomic forces on the reaction coordinate related atoms at each integration step. We applied this method to two reactions in aqueous solution and reproduced potentials of mean force at the ab initio QM/MM level. The saving in computational cost is about 2 orders of magnitude. The present work reveals great potentials for machine learning in QM/MM simulations to study complex chemical processes.

Full Text

Duke Authors

Cited Authors

  • Wu, J; Shen, L; Yang, W

Published Date

  • October 2017

Published In

Volume / Issue

  • 147 / 16

Start / End Page

  • 161732 -

PubMed ID

  • 29096448

Pubmed Central ID

  • 29096448

Electronic International Standard Serial Number (EISSN)

  • 1089-7690

International Standard Serial Number (ISSN)

  • 0021-9606

Digital Object Identifier (DOI)

  • 10.1063/1.5006882

Language

  • eng