Estimating the effective fields of spin configurations using a deep learning technique

Abstract The properties of complicated magnetic domain structures induced by various spin–spin interactions in magnetic systems have been extensively investigated in recent years. To understand the statistical and dynamic properties of complex magnetic structures, it is crucial to obtain information...

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Autores principales:	D. B. Lee, H. G. Yoon, S. M. Park, J. W. Choi, H. Y. Kwon, C. Won
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a5b2c4f69290400c8e56ae1cdf3bb41d
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spelling	oai:doaj.org-article:a5b2c4f69290400c8e56ae1cdf3bb41d2021-11-28T12:21:48ZEstimating the effective fields of spin configurations using a deep learning technique10.1038/s41598-021-02374-02045-2322https://doaj.org/article/a5b2c4f69290400c8e56ae1cdf3bb41d2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02374-0https://doaj.org/toc/2045-2322Abstract The properties of complicated magnetic domain structures induced by various spin–spin interactions in magnetic systems have been extensively investigated in recent years. To understand the statistical and dynamic properties of complex magnetic structures, it is crucial to obtain information on the effective field distribution over the structure, which is not directly provided by magnetization. In this study, we use a deep learning technique to estimate the effective fields of spin configurations. We construct a deep neural network and train it with spin configuration datasets generated by Monte Carlo simulation. We show that the trained network can successfully estimate the magnetic effective field even though we do not offer explicit Hamiltonian parameter values. The estimated effective field information is highly applicable; it is utilized to reduce noise, correct defects in the magnetization data, generate spin configurations, estimate external field responses, and interpret experimental images.D. B. LeeH. G. YoonS. M. ParkJ. W. ChoiH. Y. KwonC. WonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q D. B. Lee H. G. Yoon S. M. Park J. W. Choi H. Y. Kwon C. Won Estimating the effective fields of spin configurations using a deep learning technique
description	Abstract The properties of complicated magnetic domain structures induced by various spin–spin interactions in magnetic systems have been extensively investigated in recent years. To understand the statistical and dynamic properties of complex magnetic structures, it is crucial to obtain information on the effective field distribution over the structure, which is not directly provided by magnetization. In this study, we use a deep learning technique to estimate the effective fields of spin configurations. We construct a deep neural network and train it with spin configuration datasets generated by Monte Carlo simulation. We show that the trained network can successfully estimate the magnetic effective field even though we do not offer explicit Hamiltonian parameter values. The estimated effective field information is highly applicable; it is utilized to reduce noise, correct defects in the magnetization data, generate spin configurations, estimate external field responses, and interpret experimental images.
format	article
author	D. B. Lee H. G. Yoon S. M. Park J. W. Choi H. Y. Kwon C. Won
author_facet	D. B. Lee H. G. Yoon S. M. Park J. W. Choi H. Y. Kwon C. Won
author_sort	D. B. Lee
title	Estimating the effective fields of spin configurations using a deep learning technique
title_short	Estimating the effective fields of spin configurations using a deep learning technique
title_full	Estimating the effective fields of spin configurations using a deep learning technique
title_fullStr	Estimating the effective fields of spin configurations using a deep learning technique
title_full_unstemmed	Estimating the effective fields of spin configurations using a deep learning technique
title_sort	estimating the effective fields of spin configurations using a deep learning technique
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a5b2c4f69290400c8e56ae1cdf3bb41d
work_keys_str_mv	AT dblee estimatingtheeffectivefieldsofspinconfigurationsusingadeeplearningtechnique AT hgyoon estimatingtheeffectivefieldsofspinconfigurationsusingadeeplearningtechnique AT smpark estimatingtheeffectivefieldsofspinconfigurationsusingadeeplearningtechnique AT jwchoi estimatingtheeffectivefieldsofspinconfigurationsusingadeeplearningtechnique AT hykwon estimatingtheeffectivefieldsofspinconfigurationsusingadeeplearningtechnique AT cwon estimatingtheeffectivefieldsofspinconfigurationsusingadeeplearningtechnique
_version_	1718408034763407360

Estimating the effective fields of spin configurations using a deep learning technique

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