Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929391659909120 |
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| author | Lee, Woo Seok Song, Taegeun Kim, Kyoung-Min |
| author_facet | Lee, Woo Seok Song, Taegeun Kim, Kyoung-Min |
| contents | The application of twist engineering in van der Waals magnets has opened new frontiers in the field of two-dimensional magnetism, yielding distinctive magnetic domain structures. Despite the introduction of numerous theoretical methods, limitations persist in terms of accuracy or efficiency due to the complex nature of the magnetic Hamiltonians pertinent to these systems. In this study, we introduce a deep-learning approach to tackle these challenges. Utilizing customized, fully connected networks, we develop two deep-neural-network kernels that facilitate efficient and reliable analysis of twisted van der Waals magnets. Our regression model is adept at estimating the magnetic Hamiltonian parameters of twisted bilayer CrI3 from its magnetic domain images generated through atomistic spin simulations. The generative model excels in producing precise magnetic domain images from the provided magnetic parameters. The trained networks for these models undergo thorough validation, including statistical error analysis and assessment of robustness against noisy injections. These advancements not only extend the applicability of deep-learning methods to twisted van der Waals magnets but also streamline future investigations into these captivating yet poorly understood systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11434 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets Lee, Woo Seok Song, Taegeun Kim, Kyoung-Min Strongly Correlated Electrons Mesoscale and Nanoscale Physics Computational Physics The application of twist engineering in van der Waals magnets has opened new frontiers in the field of two-dimensional magnetism, yielding distinctive magnetic domain structures. Despite the introduction of numerous theoretical methods, limitations persist in terms of accuracy or efficiency due to the complex nature of the magnetic Hamiltonians pertinent to these systems. In this study, we introduce a deep-learning approach to tackle these challenges. Utilizing customized, fully connected networks, we develop two deep-neural-network kernels that facilitate efficient and reliable analysis of twisted van der Waals magnets. Our regression model is adept at estimating the magnetic Hamiltonian parameters of twisted bilayer CrI3 from its magnetic domain images generated through atomistic spin simulations. The generative model excels in producing precise magnetic domain images from the provided magnetic parameters. The trained networks for these models undergo thorough validation, including statistical error analysis and assessment of robustness against noisy injections. These advancements not only extend the applicability of deep-learning methods to twisted van der Waals magnets but also streamline future investigations into these captivating yet poorly understood systems. |
| title | Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets |
| topic | Strongly Correlated Electrons Mesoscale and Nanoscale Physics Computational Physics |
| url | https://arxiv.org/abs/2402.11434 |