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

Kenta Kanamori, Kazuaki Toyoura, Junya Honda, Kazuki Hattori, Atsuto Seko, Masayuki Karasuyama, Kazuki Shitara, Motoki Shiga, Akihide Kuwabara, and Ichiro Takeuchi
Phys. Rev. B 97, 125124 – Published 15 March 2018
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Abstract

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.

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  • Received 10 October 2017
  • Revised 18 January 2018

DOI:https://doi.org/10.1103/PhysRevB.97.125124

©2018 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Kenta Kanamori1, Kazuaki Toyoura2,*, Junya Honda3,4, Kazuki Hattori2, Atsuto Seko2,5,6,7, Masayuki Karasuyama1,6,7, Kazuki Shitara7,8, Motoki Shiga6,9, Akihide Kuwabara7,8, and Ichiro Takeuchi1,4,7,†

  • 1Department of Computer Science, Nagoya Institute of Technology, Nagoya 466-8555, Japan
  • 2Department of Materials Science and Engineering, Kyoto University, Kyoto 606-8501, Japan
  • 3Department of Complexity Science and Engineering, The University of Tokyo, Chiba 277-8561, Japan
  • 4RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
  • 5Center for Elements Strategy Initiative for Structure Materials (ESISM), Kyoto University, Kyoto 606-8501, Japan
  • 6JST, PRESTO, Kawaguchi 332-0012, Japan
  • 7Center for Materials Research by Information Integration, National Institute for Materials Science, Tsukuba 305-0047, Japan
  • 8Nanostructures Research Laboratory, Japan Fine Ceramics Center, Nagoya 456-8587, Japan
  • 9Department of Electrical, Electronic and Computer Engineering, Gifu University, Gifu 501-1193, Japan

  • *toyoura.kazuaki.5r@kyoto-u.ac.jp
  • takeuchi.ichiro@nitech.ac.jp

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Issue

Vol. 97, Iss. 12 — 15 March 2018

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