Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.11756 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929761396195328 |
|---|---|
| author | Li, Xiang Chen, Yixiao Li, Bohao Chen, Haoxiang Wu, Fengcheng Chen, Ji Ren, Weiluo |
| author_facet | Li, Xiang Chen, Yixiao Li, Bohao Chen, Haoxiang Wu, Fengcheng Chen, Ji Ren, Weiluo |
| contents | Electronic topological phases of matter, characterized by robust boundary states derived from topologically nontrivial bulk states, are pivotal for next-generation electronic devices. However, understanding their complex quantum phases, especially at larger scales and fractional fillings with strong electron correlations, has long posed a formidable computational challenge. Here, we employ a deep learning framework to express the many-body wavefunction of topological states in twisted ${\rm MoTe_2}$ systems, where diverse topological states are observed. Leveraging neural networks, we demonstrate the ability to identify and characterize topological phases, including the integer and fractional Chern insulators as well as the $Z_2$ topological insulators. Our deep learning approach significantly outperforms traditional methods, not only in computational efficiency but also in accuracy, enabling us to study larger systems and differentiate between competing phases such as fractional Chern insulators and charge density waves. Our predictions align closely with experimental observations, highlighting the potential of deep learning techniques to explore the rich landscape of topological and strongly correlated phenomena. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11756 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning Sheds Light on Integer and Fractional Topological Insulators Li, Xiang Chen, Yixiao Li, Bohao Chen, Haoxiang Wu, Fengcheng Chen, Ji Ren, Weiluo Strongly Correlated Electrons Mesoscale and Nanoscale Physics Materials Science Computational Physics Electronic topological phases of matter, characterized by robust boundary states derived from topologically nontrivial bulk states, are pivotal for next-generation electronic devices. However, understanding their complex quantum phases, especially at larger scales and fractional fillings with strong electron correlations, has long posed a formidable computational challenge. Here, we employ a deep learning framework to express the many-body wavefunction of topological states in twisted ${\rm MoTe_2}$ systems, where diverse topological states are observed. Leveraging neural networks, we demonstrate the ability to identify and characterize topological phases, including the integer and fractional Chern insulators as well as the $Z_2$ topological insulators. Our deep learning approach significantly outperforms traditional methods, not only in computational efficiency but also in accuracy, enabling us to study larger systems and differentiate between competing phases such as fractional Chern insulators and charge density waves. Our predictions align closely with experimental observations, highlighting the potential of deep learning techniques to explore the rich landscape of topological and strongly correlated phenomena. |
| title | Deep Learning Sheds Light on Integer and Fractional Topological Insulators |
| topic | Strongly Correlated Electrons Mesoscale and Nanoscale Physics Materials Science Computational Physics |
| url | https://arxiv.org/abs/2503.11756 |