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We adapt a machine-learning approach to study the many-body localizationtransition in interacting fermionic systems on disordered 1D and 2D lattices.We perform supervised training of convolutional neural networks (CNNs) usinglabelled many-body wavefunctions at weak and strong disorder. In these limits,the average validation accuracy of the trained CNNs exceeds 99.95%. We use thedisorder-averaged predictions of the CNNs to generate energy-resolved phasediagrams, which exhibit many-body mobility edges. We provide finite-sizeestimates of the critical disorder strengths at $W_c\sim2.8$ and $9.8$ for 1Dand 2D systems of 16 sites respectively. Our results agree with the analysis ofenergy-level statistics and inverse participation ratio. By examining theconvolutional layer, we unveil its feature extraction mechanism whichhighlights the pronounced peaks in localized many-body wavefunctions whilerendering delocalized wavefunctions nearly featureless.

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