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 MoS₂ and WS₂ more likely to form type-II heterostructures with 2D-LHPs. The highest photoelectric conversion efficiency of type-II structures reaches 23.26%, demonstrating potential applications in solar cells. Notably, some MoS₂/2D-LHPs form type-S structures, showing promise in photocatalysis. Furthermore, we found that TMDs can significantly affect the conformation of organic molecules in 2D-LHPs, thus modulating the electronic properties of the heterostructures. To overcome computational cost limitations, we constructed a crystal graph convolutional neural network model based on the calculated data to predict the electronic properties of TMDs/2D-LHPs heterostructures. Using this model, we predicted the bandgaps and band alignment types of 9,360 TMDs/2D-LHPs heterostructures, providing a comprehensive theoretical reference for research in this field.