Searching for the ground state of complex spin-ice systems using deep learning techniques
Abstract 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 minim...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-19312-3 |
| _version_ | 1817993985553072128 |
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| author | H. Y. Kwon H. G. Yoon S. M. Park D. B. Lee D. Shi Y. Z. Wu J. W. Choi C. Won |
| author_facet | H. Y. Kwon H. G. Yoon S. M. Park D. B. Lee D. Shi Y. Z. Wu J. W. Choi C. Won |
| author_sort | H. Y. Kwon |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-14T01:47:04Z |
| format | Article |
| id | doaj.art-85b5a9c70edc45c3924854c12f516af8 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-14T01:47:04Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-85b5a9c70edc45c3924854c12f516af82022-12-22T02:19:30ZengNature PortfolioScientific Reports2045-23222022-09-011211910.1038/s41598-022-19312-3Searching for the ground state of complex spin-ice systems using deep learning techniquesH. Y. Kwon0H. G. Yoon1S. M. Park2D. B. Lee3D. Shi4Y. Z. Wu5J. W. Choi6C. Won7Center for Spintronics, Korea Institute of Science and TechnologyDepartment of Physics, Kyung Hee UniversityDepartment of Physics, Kyung Hee UniversityDepartment of Physics, Kyung Hee UniversitySchool of Physical Science and Technology, ShanghaiTech UniversityDepartment of Physics, State Key Laboratory of Surface Physics, Fudan UniversityCenter for Spintronics, Korea Institute of Science and TechnologyDepartment of Physics, Kyung Hee UniversityAbstract 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.https://doi.org/10.1038/s41598-022-19312-3 |
| spellingShingle | H. Y. Kwon H. G. Yoon S. M. Park D. B. Lee D. Shi Y. Z. Wu J. W. Choi C. Won Searching for the ground state of complex spin-ice systems using deep learning techniques Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-022-19312-3 |
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