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Searching for the ground state of complex spin-ice systems using deep learning techniques

Searching for the ground state of a given system is one of the most fundamental and classical questions in scientific research fields. However, when the system is complex and large, it often becomes an intractable problem; there is essentially no possibility of finding a global energy minimum state...

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Autores principales: Kwon, H. Y., Yoon, H. G., Park, S. M., Lee, D. B., Shi, D., Wu, Y. Z., Choi, J. W., Won, C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440018/
https://www.ncbi.nlm.nih.gov/pubmed/36056094
http://dx.doi.org/10.1038/s41598-022-19312-3
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author Kwon, H. Y.
Yoon, H. G.
Park, S. M.
Lee, D. B.
Shi, D.
Wu, Y. Z.
Choi, J. W.
Won, C.
author_facet Kwon, H. Y.
Yoon, H. G.
Park, S. M.
Lee, D. B.
Shi, D.
Wu, Y. Z.
Choi, J. W.
Won, C.
author_sort Kwon, H. Y.
collection PubMed
description Searching for the ground state of a given system is one of the most fundamental and classical questions in scientific research fields. However, when the system is complex and large, it often becomes an intractable problem; there is essentially no possibility of finding a global energy minimum state with reasonable computational resources. Recently, a novel method based on deep learning techniques was devised as an innovative optimization method to estimate the ground state. We apply this method to one of the most complicated spin-ice systems, aperiodic Penrose P3 patterns. From the results, we discover new configurations of topologically induced emergent frustrated spins, different from those previously known. Additionally, a candidate of the ground state for a still unexplored type of Penrose P3 spin-ice system is first proposed through this study. We anticipate that the capabilities of the deep learning techniques will not only improve our understanding on the physical properties of artificial spin-ice systems, but also bring about significant advances in a wide range of scientific research fields requiring computational approaches for optimization.
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spelling pubmed-94400182022-09-04 Searching for the ground state of complex spin-ice systems using deep learning techniques Kwon, H. Y. Yoon, H. G. Park, S. M. Lee, D. B. Shi, D. Wu, Y. Z. Choi, J. W. Won, C. Sci Rep Article Searching for the ground state of a given system is one of the most fundamental and classical questions in scientific research fields. However, when the system is complex and large, it often becomes an intractable problem; there is essentially no possibility of finding a global energy minimum state with reasonable computational resources. Recently, a novel method based on deep learning techniques was devised as an innovative optimization method to estimate the ground state. We apply this method to one of the most complicated spin-ice systems, aperiodic Penrose P3 patterns. From the results, we discover new configurations of topologically induced emergent frustrated spins, different from those previously known. Additionally, a candidate of the ground state for a still unexplored type of Penrose P3 spin-ice system is first proposed through this study. We anticipate that the capabilities of the deep learning techniques will not only improve our understanding on the physical properties of artificial spin-ice systems, but also bring about significant advances in a wide range of scientific research fields requiring computational approaches for optimization. Nature Publishing Group UK 2022-09-02 /pmc/articles/PMC9440018/ /pubmed/36056094 http://dx.doi.org/10.1038/s41598-022-19312-3 Text en © The Author(s) 2022 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
Kwon, H. Y.
Yoon, H. G.
Park, S. M.
Lee, D. B.
Shi, D.
Wu, Y. Z.
Choi, J. W.
Won, C.
Searching for the ground state of complex spin-ice systems using deep learning techniques
title Searching for the ground state of complex spin-ice systems using deep learning techniques
title_full Searching for the ground state of complex spin-ice systems using deep learning techniques
title_fullStr Searching for the ground state of complex spin-ice systems using deep learning techniques
title_full_unstemmed Searching for the ground state of complex spin-ice systems using deep learning techniques
title_short Searching for the ground state of complex spin-ice systems using deep learning techniques
title_sort searching for the ground state of complex spin-ice systems using deep learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440018/
https://www.ncbi.nlm.nih.gov/pubmed/36056094
http://dx.doi.org/10.1038/s41598-022-19312-3
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