Machine learning screening and high-throughput computation of 3d-transition-metal intercalated Janus PtXY/ζ-phosphorene (X ≠ Y; X, Y = S, Se, Te) heterostructures for photocatalytic water splitting

Abstract

The rising demand for clean and green energy sources has sparked global interest in sustainable hydrogen production technologies. To address this problem, photocatalytic water splitting has emerged as a promising solution for the sustainable production of green hydrogen and oxygen. We investigate the hydrogen adsorption Gibbs free energy for hydrogen evaluation reaction (HER) and rate-limiting Gibbs free energy for oxygen evolution reaction (OER) to analyse the catalytic activity of the transition metal (TM) intercalated PtXY/ζ-phosphorene (X ≠ Y; X, Y = S, Se, Te) van der Waals heterostructures (vdWHs). Our workflow involves generating a large dataset, followed by performing high-throughput first-principles density functional theory (DFT) calculations on a small fraction of the dataset to obtain the training dataset for a machine learning (ML) framework. Incorporating the ML with the DFT workflow, we obtained 13 potential catalysts for HER and 6 potential catalysts for OER. The interlayer distance of the heterostructures and the bond length between the Pt and X atom emerged as the most influential features for HER, whereas the choice of adsorption site is one of the major OER descriptors. Overall, ML approach integrated with high-throughput first principles calculations is promising for the prediction of potential TM-intercalated vdWHs photocatalysts for water splitting.