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arXiv · 2511.14505

Neural network impurity solver for real-frequency dynamical mean-field theory

Fenglin Deng·Yi Lu·Xiaodong Cao·Zhicheng Zhong
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Abstract

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.

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Fenglin Deng, Yi Lu, Xiaodong Cao, Zhicheng Zhong. 2025-11-18. Neural network impurity solver for real-frequency dynamical mean-field theory. https://arxiv.org/abs/2511.14505

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