@article{e05fbe1d201a441cb91fe2188bdef74d,
title = "Exploring a potential energy surface by machine learning for characterizing atomic transport",
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.",
author = "Kenta Kanamori and Kazuaki Toyoura and Junya Honda and Kazuki Hattori and Atsuto Seko and Masayuki Karasuyama and Kazuki Shitara and Motoki Shiga and Akihide Kuwabara and Ichiro Takeuchi",
note = "Funding Information: We gratefully acknowledge the insightful discussion with S. Nakajima. This work was partially supported by grants from the Japanese Ministry of Education, Culture, Sports, Science and Technology to K.T. (17H04948, 25106002), J.H. (16H00881), A.S. (15H04116), M.K. (17H04694, 16H06538), M.S. (16H00736, 16H02866), and I.T. (17H00758, 16H06538), and JST PRESTO to A.S. (Grant No. JPMJPR15N7), M.K. (Grant No. JPMJPR15N2), M.S. (Grant No. JPMJPR16N6), JST CREST to I.T. (Grants No. JPMJCR1302 and No. JPMJCR1502), RIKEN Center for Advanced Intelligence Project to J.H and I.T., and by JST support program for starting up the innovation-hub on materials research by information integration initiative to A.S., M.K., K.S., A.K., and I.T. Publisher Copyright: {\textcopyright} 2018 American Physical Society.",
year = "2018",
month = mar,
day = "15",
doi = "10.1103/PhysRevB.97.125124",
language = "English",
volume = "97",
journal = "Physical Review B",
issn = "2469-9950",
publisher = "American Physical Society",
number = "12",
}