Adaptive coupling of a deep neural network potential to a classical force field

Linfeng Zhang, Han Wang, E. Weinan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the molecular simulation community. The AMM simulated system is decomposed into three types of regions. The first type captures the important phenomena in the system and requires high accuracy, for which we use the Deep Potential Molecular Dynamics (DeePMD) model in this work. The DeePMD model is trained to accurately reproduce the statistical properties of the ab initio molecular dynamics. The second type does not require high accuracy, and a classical force field is used to describe it in an efficient way. The third type is used for a smooth transition between the first and the second types of regions. By using a force interpolation scheme and imposing a thermodynamics force in the transition region, we make the DeePMD region embedded in the AMM simulated system as if it were embedded in a system that is fully described by the accurate potential. A representative example of the liquid water system is used to show the feasibility and promise of this method.

Original languageEnglish (US)
Article number154107
JournalJournal of Chemical Physics
Volume149
Issue number15
DOIs
StatePublished - Oct 21 2018

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

Fingerprint Dive into the research topics of 'Adaptive coupling of a deep neural network potential to a classical force field'. Together they form a unique fingerprint.

Cite this