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Estimating the effective fields of spin configurations using a deep learning technique
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 e...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8616938/ https://www.ncbi.nlm.nih.gov/pubmed/34824339 http://dx.doi.org/10.1038/s41598-021-02374-0 |
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author | Lee, D. B. Yoon, H. G. Park, S. M. Choi, J. W. Kwon, H. Y. Won, C. |
author_facet | Lee, D. B. Yoon, H. G. Park, S. M. Choi, J. W. Kwon, H. Y. Won, C. |
author_sort | Lee, D. B. |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8616938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-86169382021-11-29 Estimating the effective fields of spin configurations using a deep learning technique Lee, D. B. Yoon, H. G. Park, S. M. Choi, J. W. Kwon, H. Y. Won, C. Sci Rep Article 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. Nature Publishing Group UK 2021-11-25 /pmc/articles/PMC8616938/ /pubmed/34824339 http://dx.doi.org/10.1038/s41598-021-02374-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lee, D. B. Yoon, H. G. Park, S. M. Choi, J. W. Kwon, H. Y. Won, C. Estimating the effective fields of spin configurations using a deep learning technique |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8616938/ https://www.ncbi.nlm.nih.gov/pubmed/34824339 http://dx.doi.org/10.1038/s41598-021-02374-0 |
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