Automated stopping criterion for spectral measurements with active learning

Abstract 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 t...

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Autores principales:	Tetsuro Ueno, Hideaki Ishibashi, Hideitsu Hino, Kanta Ono
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/7d0e960186a94c6d849eea07d0d17fa7
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spelling	oai:doaj.org-article:7d0e960186a94c6d849eea07d0d17fa72021-12-02T16:35:05ZAutomated stopping criterion for spectral measurements with active learning10.1038/s41524-021-00606-52057-3960https://doaj.org/article/7d0e960186a94c6d849eea07d0d17fa72021-08-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00606-5https://doaj.org/toc/2057-3960Abstract 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 high-throughput experiments in the era of materials informatics.Tetsuro UenoHideaki IshibashiHideitsu HinoKanta OnoNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Tetsuro Ueno Hideaki Ishibashi Hideitsu Hino Kanta Ono Automated stopping criterion for spectral measurements with active learning
description	Abstract 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 high-throughput experiments in the era of materials informatics.
format	article
author	Tetsuro Ueno Hideaki Ishibashi Hideitsu Hino Kanta Ono
author_facet	Tetsuro Ueno Hideaki Ishibashi Hideitsu Hino Kanta Ono
author_sort	Tetsuro Ueno
title	Automated stopping criterion for spectral measurements with active learning
title_short	Automated stopping criterion for spectral measurements with active learning
title_full	Automated stopping criterion for spectral measurements with active learning
title_fullStr	Automated stopping criterion for spectral measurements with active learning
title_full_unstemmed	Automated stopping criterion for spectral measurements with active learning
title_sort	automated stopping criterion for spectral measurements with active learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7d0e960186a94c6d849eea07d0d17fa7
work_keys_str_mv	AT tetsuroueno automatedstoppingcriterionforspectralmeasurementswithactivelearning AT hideakiishibashi automatedstoppingcriterionforspectralmeasurementswithactivelearning AT hideitsuhino automatedstoppingcriterionforspectralmeasurementswithactivelearning AT kantaono automatedstoppingcriterionforspectralmeasurementswithactivelearning
_version_	1718383763741736960

Automated stopping criterion for spectral measurements with active learning

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