TY - JOUR
T1 - Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy
AU - Kiyohara, Shin
AU - Miyata, Tomohiro
AU - Tsuda, Koji
AU - Mizoguchi, Teruyasu
N1 - Funding Information:
This study was supported by JST-PRESTO (JPM-JPR16NB 16814592), MEXT: Nos 25106003, 17H06094, and 26249092, and the special fund of the Institute of Industrial Science, University of Tokyo (Tenkai5504850104).
Publisher Copyright:
© 2018, The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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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U2 - 10.1038/s41598-018-30994-6
DO - 10.1038/s41598-018-30994-6
M3 - Article
C2 - 30190483
AN - SCOPUS:85052891921
VL - 8
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 13548
ER -