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Synergistic DFT and Machine Learning Screening of Z-Scheme g-SiC/TMD Heterostructures for Efficient Overall Water Splitting
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DOI：10.1039/D6TA02854C.png)
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Abstract
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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 remains a significant challenge. In this work; a synergistic strategy combining density functional theory (DFT) and machine learning (ML) is proposed and implemented; which efficiently screens 1T transition metal dichalcogenide (TMD) monolayers for the oxygen evolution reaction (OER) by incorporating graphene-carbide silicon (g-SiC) monolayers with high hydrogen evolution reaction (HER) performance; thereby achieving an ideal Z-scheme band alignment. The 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 OER can proceed spontaneously under light irradiation. Non-Adiabatic Molecular Dynamics (NAMD) simulations further verify that the Z-scheme pathway; with ultrafast interlayer electron-hole recombination (~1 ps). The heterostructure also achieves strong light absorption (>105 cm-1) and a remarkable solar-to-hydrogen efficiency exceeding 33%; far surpassing the conventional limit; thus demonstrating its great potential for efficient overall water splitting.
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