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Application of machine learning-based selective sampling to determine BaZrO3 grain boundary structures

Abstract

A selective sampling procedure is applied to reduce the number of density functional theory calculations needed to find energetically favorable grain boundary structures. The procedure is based on a machine learning algorithm involving a Gaussian process, and uses statistical modelling to map the energies of the all grain boundaries. Using the procedure, energetically favorable grain boundaries in BaZrO3 are identified with up to 85% lower computational cost than the brute force alternative of calculating all possible structures. Furthermore, our results suggest that using a grid size of 0.3 Å in each dimension is sufficient when creating grain boundary structures using such sampling procedures.
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Category

Academic article

Language

English

Author(s)

Affiliation

  • SINTEF Industry / Sustainable Energy Technology
  • University of Oslo
  • Japan

Year

2019

Published in

Computational Materials Science

ISSN

0927-0256

Volume

164

Page(s)

57 - 65

View this publication at Norwegian Research Information Repository