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We study a system of hard-core bosons on a two-dimensional periodic honeycomblattice subjected to an on-site potential with alternating signs along$y$-direction, using machine learning (ML) techniques. The model hosts a richphase diagram consisting of six different phases including a charge densitywave, a superfluid phase and two dimer insulator phases, one of which is also aweak topological insulator with zero Chern number but a non-trivial Berry phase[SciPost Phys. 10, 059 (2021)]. Using two distinct correlation functionscomputed via quantum Monte Carlo method, a relatively simple ML model is ableto learn information from the various phases simultaneously and accuratelypredict their phase boundaries. By employing our ML model trained on thenon-interacting dataset, we determine the phase structure of the system in thepresence of nearest-neighbor interactions. Additionally, we investigate therobustness of the weak topological insulator phase against interactions bypredicting the topological invariant, which is otherwise difficult to obtain.

Supervised learning of an interacting 2D hard-core boson model of a weak topological insulator using correlation functions