Publication Date

9-1-2020

Document Type

Article

Department

Physics and Astronomy

Publication Title

Physical Review A

Volume

102

Issue

3

DOI

10.1103/PhysRevA.102.033326

Abstract

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- A nd 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.

Funding Number

1918572

Funding Sponsor

National Science Foundation

Comments

©2020 American Physical Society

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