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Magnetic Hamiltonian parameter estimation using deep learning techniques

Understanding spin textures in magnetic systems is extremely important to the spintronics and it is vital to extrapolate the magnetic Hamiltonian parameters through the experimentally determined spin. It can provide a better complementary link between theories and experimental results. We demonstrat...

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Detalles Bibliográficos
Autores principales: Kwon, H. Y., Yoon, H. G., Lee, C., Chen, G., Liu, K., Schmid, A. K., Wu, Y. Z., Choi, J. W., Won, C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7518863/
https://www.ncbi.nlm.nih.gov/pubmed/32978161
http://dx.doi.org/10.1126/sciadv.abb0872
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author Kwon, H. Y.
Yoon, H. G.
Lee, C.
Chen, G.
Liu, K.
Schmid, A. K.
Wu, Y. Z.
Choi, J. W.
Won, C.
author_facet Kwon, H. Y.
Yoon, H. G.
Lee, C.
Chen, G.
Liu, K.
Schmid, A. K.
Wu, Y. Z.
Choi, J. W.
Won, C.
author_sort Kwon, H. Y.
collection PubMed
description Understanding spin textures in magnetic systems is extremely important to the spintronics and it is vital to extrapolate the magnetic Hamiltonian parameters through the experimentally determined spin. It can provide a better complementary link between theories and experimental results. We demonstrate deep learning can quantify the magnetic Hamiltonian from magnetic domain images. To train the deep neural network, we generated domain configurations with Monte Carlo method. The errors from the estimations was analyzed with statistical methods and confirmed the network was successfully trained to relate the Hamiltonian parameters with magnetic structure characteristics. The network was applied to estimate experimentally observed domain images. The results are consistent with the reported results, which verifies the effectiveness of our methods. On the basis of our study, we anticipate that the deep learning techniques make a bridge to connect the experimental and theoretical approaches not only in magnetism but also throughout any scientific research.
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spelling pubmed-75188632020-10-02 Magnetic Hamiltonian parameter estimation using deep learning techniques Kwon, H. Y. Yoon, H. G. Lee, C. Chen, G. Liu, K. Schmid, A. K. Wu, Y. Z. Choi, J. W. Won, C. Sci Adv Research Articles Understanding spin textures in magnetic systems is extremely important to the spintronics and it is vital to extrapolate the magnetic Hamiltonian parameters through the experimentally determined spin. It can provide a better complementary link between theories and experimental results. We demonstrate deep learning can quantify the magnetic Hamiltonian from magnetic domain images. To train the deep neural network, we generated domain configurations with Monte Carlo method. The errors from the estimations was analyzed with statistical methods and confirmed the network was successfully trained to relate the Hamiltonian parameters with magnetic structure characteristics. The network was applied to estimate experimentally observed domain images. The results are consistent with the reported results, which verifies the effectiveness of our methods. On the basis of our study, we anticipate that the deep learning techniques make a bridge to connect the experimental and theoretical approaches not only in magnetism but also throughout any scientific research. American Association for the Advancement of Science 2020-09-25 /pmc/articles/PMC7518863/ /pubmed/32978161 http://dx.doi.org/10.1126/sciadv.abb0872 Text en Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Kwon, H. Y.
Yoon, H. G.
Lee, C.
Chen, G.
Liu, K.
Schmid, A. K.
Wu, Y. Z.
Choi, J. W.
Won, C.
Magnetic Hamiltonian parameter estimation using deep learning techniques
title Magnetic Hamiltonian parameter estimation using deep learning techniques
title_full Magnetic Hamiltonian parameter estimation using deep learning techniques
title_fullStr Magnetic Hamiltonian parameter estimation using deep learning techniques
title_full_unstemmed Magnetic Hamiltonian parameter estimation using deep learning techniques
title_short Magnetic Hamiltonian parameter estimation using deep learning techniques
title_sort magnetic hamiltonian parameter estimation using deep learning techniques
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7518863/
https://www.ncbi.nlm.nih.gov/pubmed/32978161
http://dx.doi.org/10.1126/sciadv.abb0872
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