Journal
PHYSICAL REVIEW B
Volume 97, Issue 12, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.97.125124
Keywords
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Funding
- Japanese Ministry of Education, Culture, Sports, Science and Technology [17H04948, 25106002, 16H00881, 15H04116, 17H04694, 16H06538, 16H00736, 16H02866, 17H00758]
- JST PRESTO [JPMJPR15N7, JPMJPR15N2, JPMJPR16N6]
- JST CREST [JPMJCR1302, JPMJCR1502]
- RIKEN Center for Advanced Intelligence Project
- JST support program for starting up the innovation-hub on materials research by information integration initiative
- Grants-in-Aid for Scientific Research [25106008, 17H04948] Funding Source: KAKEN
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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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