Moment tensor potentials as a promising tool to study diffusion processes

I. I. Novoselov, A. V. Yanilkin, A. V. Shapeev, E. V. Podryabinkin

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

A recently proposed class of machine-learning interatomic potentials—Moment tensor potentials (MTPs)—is investigated in this work. MTPs are able to actively select configurations and parametrize the potential on-the-fly. It is shown that MTPs accurately reproduce energies, forces and stresses calculated ab initio. As a more comprehensive test, MTPs are employed to calculate vacancy diffusion rates in Al, Mo and Si. We demonstrate that the results are in a good agreement with ab initio data for the materials considered.

Original languageEnglish
Pages (from-to)46-56
Number of pages11
JournalComputational Materials Science
Volume164
DOIs
Publication statusPublished - 15 Jun 2019

Keywords

  • Active learning
  • Aluminum
  • Diffusion
  • Molybdenum
  • Moment tensor potentials
  • Silicon

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