A virtual screening method achieved a maximum boost in speed of several tens of thousands–fold while determining the interface structure.

Crystalline interfaces have a tremendous impact on the properties of materials. Determination of the atomic structure of the interface is crucial for a comprehensive understanding of the interface properties. Despite this importance, extensive calculation is necessary to determine even one interface structure. In this study, we apply a technique called kriging, borrowed from geostatistics, to accelerate the determination of the interface structure. The atomic structure of simplified coincidence-site lattice interfaces were determined using the kriging approach. Our approach successfully determined the most stable interface structure with an efficiency almost 2 orders of magnitude better than the traditional “brute force” approach.

Spectroscopy is indispensable for determining atomic configurations, chemical bondings, and vibrational behaviours, which are crucial information for materials development. Despite their importance, the interpretation of spectra using “human-driven” methods, such as the manual comparison of experimental spectra with reference/simulated spectra, is difficult due to the explosive increase in the number of experimental spectra to be observed. To overcome the limitations of the “human-driven” approach, we develop a new “data-driven” approach based on machine learning techniques by combining the layer clustering and decision tree methods. The proposed method is applied to the 46 oxygen-K edges of the ELNES/XANES spectra of oxide compounds. With this method, the spectra can be interpreted in accordance with the material information. Furthermore, we demonstrate that our method can predict spectral features from the material information. Our approach has the potential to provide information about a material that cannot be determined manually as well as predict a plausible spectrum from the geometric information alone.

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