Active learning of uniformly accurate interatomic potentials for materials simulation

Linfeng Zhang, De Ye Lin, Han Wang, Roberto Car, Weinan E

Research output: Contribution to journalArticle

34 Scopus citations

Abstract

An active learning procedure called deep potential generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: Exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg, and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.

Original languageEnglish (US)
Article number023804
JournalPhysical Review Materials
Volume3
Issue number2
DOIs
StatePublished - Feb 25 2019

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Physics and Astronomy (miscellaneous)

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