Machine-learning recognition of Dzyaloshinskii-Moriya interaction from magnetometry
The Dzyaloshinskii-Moriya interaction (DMI), which is the antisymmetric part of the exchange interaction between neighboring local spins, winds the spin manifold and can stabilize nontrivial topological spin textures. Since topology is a robust information carrier, characterization techniques that c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2023-10-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.5.043012 |
| _version_ | 1797210355415384064 |
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| author | Bradley J. Fugetta Zhijie Chen Dhritiman Bhattacharya Kun Yue Kai Liu Amy Y. Liu Gen Yin |
| author_facet | Bradley J. Fugetta Zhijie Chen Dhritiman Bhattacharya Kun Yue Kai Liu Amy Y. Liu Gen Yin |
| author_sort | Bradley J. Fugetta |
| collection | DOAJ |
| description | The Dzyaloshinskii-Moriya interaction (DMI), which is the antisymmetric part of the exchange interaction between neighboring local spins, winds the spin manifold and can stabilize nontrivial topological spin textures. Since topology is a robust information carrier, characterization techniques that can extract the DMI magnitude are important for the discovery and optimization of spintronic materials. Existing experimental techniques for quantitative determination of DMI, such as high-resolution magnetic imaging of spin textures and measurement of magnon or transport properties, are time-consuming and require specialized instrumentation. Here we show that a convolutional neural network can extract the DMI magnitude from minor hysteresis loops, or magnetic “fingerprints,” of a material. These hysteresis loops are readily available by conventional magnetometry measurements. This provides a convenient tool to investigate topological spin textures for next-generation information processing. |
| first_indexed | 2024-04-24T10:09:17Z |
| format | Article |
| id | doaj.art-1349dc66ca8e4e76b54f65539573c853 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:09:17Z |
| publishDate | 2023-10-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-1349dc66ca8e4e76b54f65539573c8532024-04-12T17:34:42ZengAmerican Physical SocietyPhysical Review Research2643-15642023-10-015404301210.1103/PhysRevResearch.5.043012Machine-learning recognition of Dzyaloshinskii-Moriya interaction from magnetometryBradley J. FugettaZhijie ChenDhritiman BhattacharyaKun YueKai LiuAmy Y. LiuGen YinThe Dzyaloshinskii-Moriya interaction (DMI), which is the antisymmetric part of the exchange interaction between neighboring local spins, winds the spin manifold and can stabilize nontrivial topological spin textures. Since topology is a robust information carrier, characterization techniques that can extract the DMI magnitude are important for the discovery and optimization of spintronic materials. Existing experimental techniques for quantitative determination of DMI, such as high-resolution magnetic imaging of spin textures and measurement of magnon or transport properties, are time-consuming and require specialized instrumentation. Here we show that a convolutional neural network can extract the DMI magnitude from minor hysteresis loops, or magnetic “fingerprints,” of a material. These hysteresis loops are readily available by conventional magnetometry measurements. This provides a convenient tool to investigate topological spin textures for next-generation information processing.http://doi.org/10.1103/PhysRevResearch.5.043012 |
| spellingShingle | Bradley J. Fugetta Zhijie Chen Dhritiman Bhattacharya Kun Yue Kai Liu Amy Y. Liu Gen Yin Machine-learning recognition of Dzyaloshinskii-Moriya interaction from magnetometry Physical Review Research |
| title | Machine-learning recognition of Dzyaloshinskii-Moriya interaction from magnetometry |
| title_full | Machine-learning recognition of Dzyaloshinskii-Moriya interaction from magnetometry |
| title_fullStr | Machine-learning recognition of Dzyaloshinskii-Moriya interaction from magnetometry |
| title_full_unstemmed | Machine-learning recognition of Dzyaloshinskii-Moriya interaction from magnetometry |
| title_short | Machine-learning recognition of Dzyaloshinskii-Moriya interaction from magnetometry |
| title_sort | machine learning recognition of dzyaloshinskii moriya interaction from magnetometry |
| url | http://doi.org/10.1103/PhysRevResearch.5.043012 |
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