Identifying 1H/2T Phases and Defects in MoS₂ Using Boltzmann Machines

We use Boltzmann machines (BMs), an energy-based learning model, to identify semiconducting (2H) and metallic (1T) phases and defects in molecular dynamics (MD) simulations of strained MoS₂ monolayer. We compare various BM models, i.e. Restricted BMs (RBM) versus Limited BMs (LBM) with intra-layer couplings, and measure their performances. Our use of BMs gives insight into the structure of the underlying MD data and is amenable to implementation using sampling via an adiabatic quantum annealer. We show our LBMs have superior performance over RBMs, examine connectivity within our BM variants, explore hardware qubit mapping schemes, and discuss what performance differences may imply about locality within the data without prior knowledge.