Journal
PHYSICA B-CONDENSED MATTER
Volume 532, Issue -, Pages 24-28Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.physb.2017.03.006
Keywords
Grain boundary structure; Bayesian optimization method; Kriging; Energy surface; Metal oxide
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Funding
- Japan Science and Technology Agency - Precursory Research for Embryonic Science and Technology (JST-PRESTO), Japan
- Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) [25106003, 26249092]
- Grants-in-Aid for Scientific Research [17H06094, 25106003] Funding Source: KAKEN
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Recently, powerful methods for determining grain boundary structures with the aid of machine learning techniques have been proposed. However, the application of these methods to oxide materials has not been reported. Herein, we describe a Bayesian optimization method (Kriging) for effective and accurate determination of grain boundary structures of complex materials, namely metal oxides, including MgO, TiO2, and CeO2. The efficiency of this method is similar to 500 times higher than that of conventional all candidate calculations. We reveal that the grain boundary energy surface of metal oxides is very similar to that of metallic materials, enabling the use of the Kriging method to determine grain boundary structures.
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