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
The automated stopping of a spectral measurement with active learning is proposed.The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the ...The automated stopping of a spectral measurement with active learning is proposed.The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the generalisation error of the Gaussian process regression.It is revealed that the automated stopping criterion of the spectral measurement gives an approximated X-ray absorption spectrum with sufficient accuracy and reduced data size.The proposed method is not only a proof-of-concept of the optimal stopping problem in active learning but also the key to enhancing the efficiency of spectral measurements for highthroughput experiments in the era of materials informatics.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.
基金This work was supported by JST-Mirai Program Grant Numbers JPMJMI19G1 and JPMJMI21G2T.U.acknowledges the support of JSPS KAKENHI Grant Number JP18K13984 and QST President’s Strategic Grant(Exploratory Research).H.H.acknowledges the support of NEDO Grant Number JPNP18002 and JST CREST Grant Number JPMJCR1761+2 种基金This work was carried out under the ISM Cooperative Research Program(H30-J-4302 and 2019-ISMCRP-4206)The XAS experiment was performed under the approval of the Photon Factory Program Advisory Committee(Proposal No.2018MP001)The authors thank Dr.Yasuo Takeichi for the support of the experiments at the Photon Factory.
文摘The automated stopping of a spectral measurement with active learning is proposed.The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the generalisation error of the Gaussian process regression.It is revealed that the automated stopping criterion of the spectral measurement gives an approximated X-ray absorption spectrum with sufficient accuracy and reduced data size.The proposed method is not only a proof-of-concept of the optimal stopping problem in active learning but also the key to enhancing the efficiency of spectral measurements for highthroughput experiments in the era of materials informatics.
