Emergent Wigner phases in moiré superlattice from deep learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914836580925440 |
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| author | Li, Xiang Qian, Yubing Ren, Weiluo Xu, Yang Chen, Ji |
| author_facet | Li, Xiang Qian, Yubing Ren, Weiluo Xu, Yang Chen, Ji |
| contents | Moiré superlattice designed in stacked van der Waals material provides a dynamic platform for hosting exotic and emergent condensed matter phenomena. However, the relevance of strong correlation effects and the large size of moiré unit cells pose significant challenges for traditional computational techniques. To overcome these challenges, we develop an unsupervised deep learning approach to uncover electronic phases emerging from moiré systems based on variational optimization of neural network many-body wavefunction. Our approach has identified diverse quantum states, including novel phases such as generalized Wigner crystals, Wigner molecular crystals, and previously unreported Wigner covalent crystals. These discoveries provide insights into recent experimental studies and suggest new phases for future exploration. They also highlight the crucial role of spin polarization in determining Wigner phases. More importantly, our proposed deep learning approach is proven general and efficient, offering a powerful framework for studying moiré physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_11134 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emergent Wigner phases in moiré superlattice from deep learning Li, Xiang Qian, Yubing Ren, Weiluo Xu, Yang Chen, Ji Computational Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Chemical Physics Moiré superlattice designed in stacked van der Waals material provides a dynamic platform for hosting exotic and emergent condensed matter phenomena. However, the relevance of strong correlation effects and the large size of moiré unit cells pose significant challenges for traditional computational techniques. To overcome these challenges, we develop an unsupervised deep learning approach to uncover electronic phases emerging from moiré systems based on variational optimization of neural network many-body wavefunction. Our approach has identified diverse quantum states, including novel phases such as generalized Wigner crystals, Wigner molecular crystals, and previously unreported Wigner covalent crystals. These discoveries provide insights into recent experimental studies and suggest new phases for future exploration. They also highlight the crucial role of spin polarization in determining Wigner phases. More importantly, our proposed deep learning approach is proven general and efficient, offering a powerful framework for studying moiré physics. |
| title | Emergent Wigner phases in moiré superlattice from deep learning |
| topic | Computational Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Chemical Physics |
| url | https://arxiv.org/abs/2406.11134 |