Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy

Shin Kiyohara, Tomohiro Miyata, Koji Tsuda, Teruyasu Mizoguchi

研究成果: Article査読

14 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号13548
ジャーナルScientific reports
8
1
DOI
出版ステータスPublished - 2018 12 1
外部発表はい

ASJC Scopus subject areas

  • General

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