論文

査読有り
2016年2月

Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides

PHYSICAL REVIEW B
  • Kazuaki Toyoura
  • ,
  • Daisuke Hirano
  • ,
  • Atsuto Seko
  • ,
  • Motoki Shiga
  • ,
  • Akihide Kuwabara
  • ,
  • Masayuki Karasuyama
  • ,
  • Kazuki Shitara
  • ,
  • Ichiro Takeuchi

93
5
開始ページ
054112
終了ページ
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1103/PhysRevB.93.054112
出版者・発行元
AMER PHYSICAL SOC

In this paper, we propose a selective sampling procedure to preferentially evaluate a potential energy surface (PES) in a part of the configuration space governing a physical property of interest. The proposed sampling procedure is based on a machine-learning method called the Gaussian process, which is used to construct a statistical model of the PES for identifying the region of interest in the configuration space. We demonstrate the efficacy of the proposed procedure for atomic diffusion and ionic conduction, specifically, the proton conduction in a well-studied proton-conducting oxide, barium zirconate (BaZrO3). The results of the demonstration study indicate that our procedure can efficiently identify the low-energy region characterizing the proton conduction in the host crystal lattice and that the descriptors used for the statistical PES model have a great influence on the performance.

Web of Science ® 被引用回数 : 38

リンク情報
DOI
https://doi.org/10.1103/PhysRevB.93.054112
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000370244300001&DestApp=WOS_CPL
ID情報
  • DOI : 10.1103/PhysRevB.93.054112
  • ISSN : 2469-9950
  • eISSN : 2469-9969
  • Web of Science ID : WOS:000370244300001

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