Bayesian optimization for efficient determination of metal oxide grain boundary structures

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 ~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.