Machine-Learning Recognition of Dzyaloshinskii-Moriya Interaction from Magnetometry
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909976402853888 |
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| author | Fugetta, Bradley J. Chen, Zhijie Bhattacharya, Dhritiman Yue, Kun Liu, Kai Liu, Amy Y. Yin, Gen |
| author_facet | Fugetta, Bradley J. Chen, Zhijie Bhattacharya, Dhritiman Yue, Kun Liu, Kai Liu, Amy Y. Yin, Gen |
| contents | 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 non-trivial 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_05905 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine-Learning Recognition of Dzyaloshinskii-Moriya Interaction from Magnetometry Fugetta, Bradley J. Chen, Zhijie Bhattacharya, Dhritiman Yue, Kun Liu, Kai Liu, Amy Y. Yin, Gen Materials Science Disordered Systems and Neural Networks 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 non-trivial 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. |
| title | Machine-Learning Recognition of Dzyaloshinskii-Moriya Interaction from Magnetometry |
| topic | Materials Science Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2304.05905 |