Synergistic DFT and machine learning screening of Z-scheme g-SiC/TMD heterostructures for efficient overall water splitting

Abstract

To mitigate the heavy reliance on non-renewable resources such as fossil fuels, photocatalytic water splitting has been recognized as a promising method for generating clean and renewable energy. However, achieving Z-scheme heterostructures with suitable band edge positions for photocatalysis remains a challenge. In this work, a synergistic strategy combining density functional theory (DFT) and machine learning (ML) is proposed and implemented to efficiently screen 1T transition metal dichalcogenide (TMD) monolayers for the oxygen evolution reaction (OER) by incorporating graphene-like silicon carbide (g-SiC) monolayers with high hydrogen evolution reaction (HER) performance, thereby achieving an ideal Z-scheme band alignment. DFT calculation results reveal that the g-SiC/SZrSe (S–C stacking) heterostructure and g-SiC/SeZrS (Se–C stacking) heterostructure are determined to be the most stable configurations for Z-scheme heterostructures, where both the HER and the OER can proceed spontaneously under light irradiation. Non-Adiabatic Molecular Dynamics (NAMD) simulations further verify the Z-scheme pathway, with ultrafast interlayer electron–hole recombination (∼1 ps). The heterostructure also achieves strong light absorption (>10⁵ cm⁻¹) and a solar-to-hydrogen efficiency exceeding 33%, beyond the conventional limit, thus demonstrating its great potential for efficient overall water splitting.