Abstract

Spectroscopy is a widely used experimental technique, and enhancing its efficiency can have a strong impact on materials research. We propose an adaptive design for spectroscopy experiments that uses a machine learning technique to improve efficiency. We examined X-ray magnetic circular dichroism (XMCD) spectroscopy for the applicability of a machine learning technique to spectroscopy. An XMCD spectrum was predicted by Gaussian process modelling with learning of an experimental spectrum using a limited number of observed data points. Adaptive sampling of data points with maximum variance of the predicted spectrum successfully reduced the total data points for the evaluation of magnetic moments while providing the required accuracy. The present method reduces the time and cost for XMCD spectroscopy and has potential applicability to various spectroscopies.

Details

Title
Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling
Author
Ueno, Tetsuro 1   VIAFID ORCID Logo  ; Hino, Hideitsu 2 ; Hashimoto, Ai 3 ; Takeichi, Yasuo 3 ; Sawada, Masahiro 4 ; Ono, Kanta 5 

1  Quantum Beam Science Research Directorate, National Institutes for Quantum and Radiological Science and Technology, Sayo, Hyogo, Japan; Institute of Materials Structure Science, High Energy Accelerator Research Organization, Tsukuba, Ibaraki, Japan; Elements Strategy Initiative Center for Magnetic Materials, Research Center for Magnetic and Spintronic Materials, National Institute for Materials Science, Tsukuba, Ibaraki, Japan 
2  Department of Computer Science, University of Tsukuba, Tsukuba, Ibaraki, Japan 
3  Institute of Materials Structure Science, High Energy Accelerator Research Organization, Tsukuba, Ibaraki, Japan 
4  Hiroshima Synchrotron Radiation Center, Hiroshima University, Higashihiroshima, Hiroshima, Japan 
5  Institute of Materials Structure Science, High Energy Accelerator Research Organization, Tsukuba, Ibaraki, Japan; Elements Strategy Initiative Center for Magnetic Materials, Research Center for Magnetic and Spintronic Materials, National Institute for Materials Science, Tsukuba, Ibaraki, Japan; Center for Materials Research by Information Integration (CMI2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan 
Pages
1-8
Publication year
2018
Publication date
Jan 2018
Publisher
Nature Publishing Group
e-ISSN
20573960
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41524-017-0057-4
ProQuest document ID
1991171521
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.