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 ...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.展开更多
基金The STXM experiment was performed with the approval of the Photon Factory Program Advisory Committee(Proposal No.2015MP004)The XAS and XMCD experiments were performed at HSRC with the approval of the Proposal Assessing Committee(Proposal No.11-B-14)T.U.acknowledges the support of JSPS KAKENHI Grant Number 15K17458.H.H.is supported by JSPS KAKENHI Grant Numbers 16K16108 and 25120011.
文摘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.
