@article{oai:repo.qst.go.jp:00048692,
 author = {Ueno, Tetsuro and Hino, Hideitsu and Hashimoto, Ai and Takeichi, Yasuo and Sawada, Masahiro and Ono, Kanta and 上野 哲朗},
 issue = {1},
 journal = {npj Computational Materials},
 month = {Jan},
 note = {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.},
 pages = {4-1--4-8},
 title = {Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling},
 volume = {4},
 year = {2018}
}