Development of a new parameter optimization scheme for a reactive force field based on a machine learning approach

Hiroya Nakata, Shandan Bai

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Reactive molecular dynamics (MD) simulation is performed using a reactive force field (ReaxFF). To this end, we developed a new method to optimize the ReaxFF parameters based on a machine learning approach. This approach combines the k-nearest neighbor and random forest regressor algorithm to efficiently locate several possible ReaxFF parameter sets. As a pilot test of the developed approach, the optimized ReaxFF parameter set was applied to perform chemical vapor deposition (CVD) of an α-Al2O3 crystal. The crystal structure of α-Al2O3 was reasonably reproduced even at a relatively high temperature (2000 K). The reactive MD simulation suggests that the (11 (Formula presented.) 0) surface grows faster than the (0001) surface, indicating that the developed parameter optimization technique could be used for understanding the chemical reaction in the CVD process.

Original languageEnglish
Pages (from-to)2000-2012
Number of pages13
JournalJournal of Computational Chemistry
Volume40
Issue number23
DOIs
Publication statusPublished - 2019 Sep 5
Externally publishedYes

Keywords

  • chemical vapor deposition
  • machine learning
  • reactive molecular dynamics

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics

Fingerprint

Dive into the research topics of 'Development of a new parameter optimization scheme for a reactive force field based on a machine learning approach'. Together they form a unique fingerprint.

Cite this