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

Shin Kiyohara, Tomohiro Miyata, Koji Tsuda, Teruyasu Mizoguchi

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number13548
JournalScientific reports
Volume8
Issue number1
DOIs
Publication statusPublished - 2018 Dec 1
Externally publishedYes

ASJC Scopus subject areas

  • General

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