2020/02/29 by Ehsan Khatami, Elmer Guardado-Sanchez, Benjamin M. Spar +5 · 1 citation
Mathematics · Physics and Astronomy · #Advanced Condensed Matter Physics #Artificial intelligence #Artificial neural network #Basis (linear algebra) #Cold Atom Physics and Bose-Einstein Condensates #Computer science #Condensed matter physics #Convolutional neural network #Fermi Gamma-ray Space Telescope #Hubbard model #Mathematics #Physics #Quantum many-body systems #Spin (aerodynamics) #Statistical physics #Superconductivity #cond-mat.dis-nn #cond-mat.quant-gas #cond-mat.str-el
paper · pdf · doi:10.1103/physreva.102.033326
published as Phys. Rev. A 102, 033326 (2020) · 12 pages, 9 figures; updated in accord with the published version
openalex publication_date 2020/09/17 · openalex created_date 2020/12/21 · arxiv created 2020/12/23 · arxiv updated 2020/12/24 · openalex updated_date 2026/08/06
Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm for phases with subtle, or even unknown, patterns. Long- and short-range spin correlations spontaneously emerge in filters of a convolutional neural network trained on snapshots of single atomic species. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi-liquid behavior. Our technique can be employed to characterize correlations unique to other phases with no obvious order parameters or signatures in projective measurements, and has implications for science discovery through AI beyond strongly correlated systems.