Bayesian optimization for efficient determination of metal oxide grain boundary structures

Abstract 1 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.

[1]  R. W. Balluffi,et al.  Interfaces in crystalline materials , 2009 .

[2]  H. Ohta,et al.  Atomic structure of a Σ3 [110]/(111) grain boundary in CeO2 , 2012 .

[3]  Y. Ikuhara,et al.  Defect energetics in SrTiO3 symmetric tilt grain boundaries , 2011 .

[4]  Y. Ikuhara,et al.  Characterization and atomic modeling of an asymmetric grain boundary , 2011 .

[5]  Koji Tsuda,et al.  COMBO: An efficient Bayesian optimization library for materials science , 2016 .

[6]  Andrei V. Bandura,et al.  Derivation of Force Field Parameters for TiO2−H2O Systems from ab Initio Calculations , 2003 .

[7]  C. Elsässer,et al.  First-principles analysis of cation segregation at grain boundaries in α-Al2O3 , 2003 .

[8]  R. F. Wood,et al.  Ab initio calculations of rigid-body displacements at the {sigma}5 (210) tilt grain boundary in TiO{sub 2} , 2000 .

[9]  Koji Tsuda,et al.  Acceleration of stable interface structure searching using a kriging approach , 2016 .

[10]  N. Sata,et al.  Proton transport properties at the grain boundary of barium zirconate based proton conductors for intermediate temperature operating SOFC , 2010 .

[11]  Y. Ikuhara,et al.  Atomic Structures and Electrical Properties of ZnO Grain Boundaries , 2007 .

[12]  A. Maiti,et al.  Impurity-Induced Structural Transformation of a MgO Grain Boundary , 1998 .

[13]  B. Uberuaga,et al.  Point defect–grain boundary interactions in MgO: an atomistic study , 2013, Journal of physics. Condensed matter : an Institute of Physics journal.

[14]  Yonglai Fu.  Grain-boundary effects on the electrical resistivity and the ferromagnetic transition temperature of La0.8Ca0.2MnO3 , 2000 .

[15]  Y. Ikuhara,et al.  Atomic structure, electronic structure, and defect energetics in [001](310)Σ5 grain boundaries of SrTiO3 and BaTiO3 , 2008 .

[16]  Julian D. Gale,et al.  GULP: A computer program for the symmetry-adapted simulation of solids , 1997 .

[17]  R. Grimes,et al.  Defect cluster formation in M2O3-doped CeO2 , 1999 .

[18]  Kaoru Nakamura,et al.  First-principles study of grain boundary sliding inα−Al2O3 , 2007 .

[19]  Marc Van Meirvenne,et al.  Soil salinity mapping using spatio-temporal kriging and Bayesian maximum entropy with interval soft data , 2005 .

[20]  F. Wakai,et al.  Hardening in Creep of Alumina by Zirconium Segregation at the Grain Boundary , 2005 .

[21]  C. Fisher,et al.  The influence of grain boundary misorientation on ionic conductivity in YSZ , 1999 .

[22]  Shin Kiyohara,et al.  Prediction of interface structures and energies via virtual screening , 2016, Science Advances.