May 26 – 31, 2024
Western University
America/Toronto timezone
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Many-body mobility edges in 1D and 2D revealed by convolutional neural networks

May 27, 2024, 4:15 PM
30m
SSC Rm 2028 (cap. 135) (Social Science Centre, Western U)

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 $W_c\sim2.8$ 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.

Keyword-1 Many-Body Localization
Keyword-2 Machine Learning
Keyword-3 Convolutional Neural Network

Author

Anffany Chen (University of Alberta)

Presentation materials