Self-Attention Assistant Classification of Non-Hermitian Phases in Two-Dimensional Lattice
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866915332278452224 |
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| author | Jiang, Hengxuan Wang, Xiumei Zhou, Xingping |
| author_facet | Jiang, Hengxuan Wang, Xiumei Zhou, Xingping |
| contents | Classification of the non-Hermitian phases in high-dimensional lattice becomes challenging due to interplay of the band topology and non-Hermiticity. The significant increase in data dimensions and the number of categories has rendered traditional supervised learning and unsupervised manifold learning failed. Here, we propose the self-attention assistant machine learning for clustering non-Hermitian phases in two-dimensional lattice. By incorporating the self-attention mechanism, the model can effectively capture long-range dependencies and important patterns, resulting in a more compact and information-rich latent space. It can achieve Altland-Zirnbauer classification with Bloch vector dataset and distinguish the phases of eigenstates' localized behavior with the competition between non-Hermitian skin effect and topological localization. Our results provide a general method for characterizing non-Hermitian phases in two-dimensional lattice via machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14453 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-Attention Assistant Classification of Non-Hermitian Phases in Two-Dimensional Lattice Jiang, Hengxuan Wang, Xiumei Zhou, Xingping Other Condensed Matter Classification of the non-Hermitian phases in high-dimensional lattice becomes challenging due to interplay of the band topology and non-Hermiticity. The significant increase in data dimensions and the number of categories has rendered traditional supervised learning and unsupervised manifold learning failed. Here, we propose the self-attention assistant machine learning for clustering non-Hermitian phases in two-dimensional lattice. By incorporating the self-attention mechanism, the model can effectively capture long-range dependencies and important patterns, resulting in a more compact and information-rich latent space. It can achieve Altland-Zirnbauer classification with Bloch vector dataset and distinguish the phases of eigenstates' localized behavior with the competition between non-Hermitian skin effect and topological localization. Our results provide a general method for characterizing non-Hermitian phases in two-dimensional lattice via machine learning. |
| title | Self-Attention Assistant Classification of Non-Hermitian Phases in Two-Dimensional Lattice |
| topic | Other Condensed Matter |
| url | https://arxiv.org/abs/2409.14453 |