Machine Learning Assisted Design of Type-II Two-Dimensional Heterostructures for Photocatalytic Water Splitting

Type-II two-dimensional (2D) heterostructures
are promising
for
photocatalytic water splitting but face exploration challenges due
to high experimental/computational costs. Here, we propose an efficient
data-driven approach for the rapid discovery of type-II van der Waals
heterostructures (vdWHs) without the need for preoptimization of structures
or precise stacking information. To meet this end, a specially designed
matrix descriptor is developed to capture the important interlayer
interactions. Coupled with a one-dimensional convolutional neural
network, this descriptor can well describe weak interlayer interactions
in heterostructures, allowing direct prediction of bandgap and band
edge positions of arbitrary 2D heterostructures. 800 potential candidates
are successfully screened out of nearly 105 heterostructures
for type-II vdWHs, and further comprehensive band structure and optical
absorption spectra calculations reveal the potential of WS2/Rh2Br6 and Al2S2/PtS2 as water splitting photocatalysts. This work provides a data-driven
approach to energy materials discovery and offers a cost-effective
alternative to traditional methods.