期刊
SCIENTIFIC REPORTS
卷 8, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41598-018-30994-6
关键词
-
资金
- JST-PRESTO [JPM-JPR16NB 16814592]
- MEXT [25106003, 17H06094, 26249092]
- Institute of Industrial Science, University of Tokyo [Tenkai5504850104]
- Grants-in-Aid for Scientific Research [26249092, 17H06094] Funding Source: KAKEN
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据