Machine learning for structure determination and investigating the structure-property relationships of interfaces

Interfaces, in which the atomic structures are greatly different from those in the bulk, play a crucial role in the material properties. Therefore, determination of a central structure that is involved with the interface properties is an important task in materials research. However, determination of the interface structure requires a huge number of calculations. We previously proposed a powerful machine learning technique based on virtual screening (VS) to determine interface structures (Kiyohara et al 2016 Sci. Adv. 2 e1600746). Here, we discuss the feasibility, versatility, and robustness of the prediction model for VS. Through this study, the prediction model constructed using only 5 types of grain boundaries determines the energies and structures of the 52 grain boundaries. Furthermore, based on the constructed prediction models, we investigated the geometrical differences between the grain boundaries of different rotation axes. We also investigated the structure-property relationship at the grain boundary (GB). We found that a short bond at the GB is the key factor for preferential vacancy formation at the GB.