Neural network impurity solver for real-frequency dynamical mean-field theory
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866917089301757952 |
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| author | Deng, Fenglin Lu, Yi Cao, Xiaodong Zhong, Zhicheng |
| author_facet | Deng, Fenglin Lu, Yi Cao, Xiaodong Zhong, Zhicheng |
| contents | We introduce a neural network impurity solver for real-frequency DMFT that employs a multihead cross-attention mechanism to map hybridization functions to spectral functions, conditioned on impurity parameters. Trained on high-quality MPS data from complex contour time evolution and incorporating derivative constraints with respect to the complex-time angle, our model achieves smooth generalization to the real-frequency axis. Benchmarking on the single-band Hubbard model for the Bethe lattice demonstrates quantitative accuracy across metallic, strongly correlated, and insulating regimes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14505 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural network impurity solver for real-frequency dynamical mean-field theory Deng, Fenglin Lu, Yi Cao, Xiaodong Zhong, Zhicheng Strongly Correlated Electrons We introduce a neural network impurity solver for real-frequency DMFT that employs a multihead cross-attention mechanism to map hybridization functions to spectral functions, conditioned on impurity parameters. Trained on high-quality MPS data from complex contour time evolution and incorporating derivative constraints with respect to the complex-time angle, our model achieves smooth generalization to the real-frequency axis. Benchmarking on the single-band Hubbard model for the Bethe lattice demonstrates quantitative accuracy across metallic, strongly correlated, and insulating regimes. |
| title | Neural network impurity solver for real-frequency dynamical mean-field theory |
| topic | Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2511.14505 |