論文

査読有り
2018年3月

Exploring a potential energy surface by machine learning for characterizing atomic transport

PHYSICAL REVIEW B
  • Kanamori, Kenta
  • ,
  • Toyoura, Kazuaki
  • ,
  • Honda, Junya
  • ,
  • Hattori, Kazuki
  • ,
  • Seko, Atsuto
  • ,
  • Karasuyama, Masayuki
  • ,
  • Shitara, Kazuki
  • ,
  • Shiga, Motoki
  • ,
  • Kuwabara, Akihide
  • ,
  • Takeuchi, Ichiro

97
12
開始ページ
125124
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.97.125124
出版者・発行元
AMER PHYSICAL SOC

We propose a machine-learning method for evaluating the potential barrier governing atomic transport based on the preferential selection of dominant points for atomic transport. The proposed method generates numerous random samples of the entire potential energy surface (PES) from a probabilistic Gaussian process model of the PES, which enables defining the likelihood of the dominant points. The robustness and efficiency of the method are demonstrated on a dozen model cases for proton diffusion in oxides, in comparison with a conventional nudge elastic band method.

Web of Science ® 被引用回数 : 18

リンク情報
DOI
https://doi.org/10.1103/PhysRevB.97.125124
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000427602000005&DestApp=WOS_CPL
ID情報
  • DOI : 10.1103/PhysRevB.97.125124
  • ISSN : 2469-9950
  • eISSN : 2469-9969
  • Web of Science ID : WOS:000427602000005

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