High-throughput computation and machine learning screening of van der Waals heterostructures for Z-scheme photocatalysis

Abstract

Although van der Waals (vdW) heterostructures have shown significant photocatalytic applications, the discovery of high-performance vdW heterostructure photocatalysts is limited by the computational cost in the high-dimensional search space and the complexity of large-scale atomic models. Here, we utilize big-data analysis, high-throughput screening, high-fidelity calculations, and machine learning to discover Z-scheme heterostructure photocatalysts from 11 935 vdW heterostructures, constructed using 155 two-dimensional (2D) semiconductors with diverse structures from our 2DMatPedia database. We first perform high-throughput high-fidelity hybrid functional calculations on the 155 monolayer 2D semiconductors to obtain their high-accuracy band information. Using the explainable descriptor and deep reinforcement learning algorithm, we identify 1062 potential Z-scheme vdW heterostructures. Finally, the best 33 Z-scheme heterostructure photocatalysts from the pool of 1062 candidates are verified and validated through high-fidelity hybrid functional calculations. Among these Z-scheme heterojunctions, our photocatalytic calculations indicate that SnO₂/WSe₂, Bi₂Se₃/VI₂, Bi₂Se₃/Sb, and Bi₂Te₂S/Sr(SnAs)₂ have the best redox abilities. Using machine learning techniques, we further identified 29 new high-potential Z-scheme heterostructures from the pool, making a total of 62 candidates. The combination of high-throughput, descriptor, and machine learning techniques helps to narrow down the candidates of high-performance photocatalytic heterostructures in a very large material space and accelerate the discovery process of Z-scheme photocatalysts in the experiment.