High-Throughput Computational Study and Machine Learning Prediction of Electronic Properties in Transition Metal Dichalcogenide/Two-Dimensional Layered Halide Perovskite Heterostructures

Heterostructures formed by transition metal dichalcogenides (TMDs) and two-dimensional layered halide perovskites (2D-LHPs) have attracted significant attention due to their unique optoelectronic properties. However, theoretical studies face challenges due to the large number of atoms and the need for lattice matching. With the discovery of more 2D-LHPs, there is an urgent need for methods to rapidly predict and screen TMDs/2D-LHPs heterostructures. This study employs first-principles calculations to perform high-throughput computations on 602 TMDs/2D-LHPs heterostructures. Results show that different combinations exhibit diverse band alignments, with MoS