2024 CAP Congress / Congrès de l'ACP 2024

Many-body mobility edges in 1D and 2D revealed by convolutional neural networks

27 May 2024, 16:15

30m

SSC Rm 2028 (cap. 135) (Social Science Centre, Western U)

Invited Speaker / Conférencier(ère) invité(e) Theoretical Physics / Physique théorique (DTP-DPT) (DTP) M3-2 Quantum and Condensed Matter Theory | Théorie quantique et de la matière condensée (DPT)

Speaker

Anffany Chen (University of Alberta)

Description

We adapt a machine-learning approach to study the many-body localization transition in interacting fermionic systems on disordered 1D and 2D lattices. We perform supervised training of convolutional neural networks (CNNs) using labelled many-body wavefunctions at weak and strong disorder. In these limits, the average validation accuracy of the trained CNNs exceeds 99.95%. We use the disorder-averaged predictions of the CNNs to generate energy-resolved phase diagrams, which exhibit many-body mobility edges. We provide finite-size estimates of the critical disorder strengths at $W_c\sim 2.8$ and $9.8$ for 1D and 2D systems of 16 sites respectively. Our results agree with the analysis of energy-level statistics and inverse participation ratio. By examining the convolutional layer, we unveil its feature extraction mechanism which highlights the pronounced peaks in localized many-body wavefunctions while rendering delocalized wavefunctions nearly featureless.

Presentation materials

CAP 2024-05-27.pdf