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

作     者：Kenta Kanamori Kazuaki Toyoura Junya Honda Kazuki Hattori Atsuto Seko Masayuki Karasuyama Kazuki Shitara Motoki Shiga Akihide Kuwabara Ichiro Takeuchi 

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

出 版 物：《Physical Review B》 (Phys. Rev. B)

年 卷 期：2018年第97卷第12期

页      面：125124-125124页

核心收录：

基　　金：Japan Society for the Promotion of Science, JSPS, (16H02866, 17H00758, 17H04694, 17H04948) Ministry of Education, Culture, Sports, Science and Technology, MEXT, (15H04116, 16H00736, 16H00881, 16H06538, 25106002) Japan Science and Technology Agency, JST Core Research for Evolutional Science and Technology, CREST, (JPMJCR1302, JPMJCR1502) RIKEN Precursory Research for Embryonic Science and Technology, PRESTO, (JPMJPR15N2, JPMJPR15N7, JPMJPR16N6) 

主　　题：Diffusion Potential energy surfaces Ab initio calculations First-principles calculations Machine learning 

摘      要：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.