Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.15741 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910067331170304 |
|---|---|
| author | Valenti, Agnes Park, Ina Georges, Antoine Millis, Andrew J. Parcollet, Olivier |
| author_facet | Valenti, Agnes Park, Ina Georges, Antoine Millis, Andrew J. Parcollet, Olivier |
| contents | Quantum impurity solvers are the computational bottleneck of quantum embedding approaches to correlated materials, such as dynamical mean-field theory (DMFT). We show that neural networks trained on synthetic, material-agnostic data learn the impurity mapping from hybridization functions and local interactions to Green's functions with quantitative accuracy for both model systems and real materials, providing fast solvers for single- and multi-orbital models. Benchmarks against numerically controlled quantum Monte Carlo show that the method reproduces the Mott transition, multi-orbital phase diagrams of Hubbard-Kanamori models, and the electronic properties of SrVO$_3$ and SrMnO$_3$. The learned solvers achieve orders-of-magnitude speedup and can initialize controlled calculations, dramatically accelerating DMFT while preserving accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15741 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neural-Network Quantum Embedding Solvers for Correlated Materials Valenti, Agnes Park, Ina Georges, Antoine Millis, Andrew J. Parcollet, Olivier Strongly Correlated Electrons Quantum impurity solvers are the computational bottleneck of quantum embedding approaches to correlated materials, such as dynamical mean-field theory (DMFT). We show that neural networks trained on synthetic, material-agnostic data learn the impurity mapping from hybridization functions and local interactions to Green's functions with quantitative accuracy for both model systems and real materials, providing fast solvers for single- and multi-orbital models. Benchmarks against numerically controlled quantum Monte Carlo show that the method reproduces the Mott transition, multi-orbital phase diagrams of Hubbard-Kanamori models, and the electronic properties of SrVO$_3$ and SrMnO$_3$. The learned solvers achieve orders-of-magnitude speedup and can initialize controlled calculations, dramatically accelerating DMFT while preserving accuracy. |
| title | Neural-Network Quantum Embedding Solvers for Correlated Materials |
| topic | Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2603.15741 |