AI‐Guided Intrinsic and Extrinsic Photocorrosion Inhibition of III–V Semiconductors for Durable Photoelectrochemical Water Splitting

Group III–V semiconductors are suitable for solar‐driven hydrogen production due to precise lattice matching (for tandem device combinations), high carrier mobilities (>10 ³  cm ² V ^–1 s ^–1 ), tunable bandgaps (1.1–2.5 eV), strong absorption coefficients (>10 ⁵  cm ^–1 ), and a large range of doping possibilities. GaInP ₂ , GaAs, and InP have a theoretical solar‐to‐hydrogen efficiency of more than 20%. Still, their high susceptibility to chemical and photochemical corrosion during prolonged photoelectrochemical water splitting (PEC‐WS) is a major industrial hurdle. This review comprehensively covers critical strategies to enhance the photostability of group III–V semiconductors for industrial‐scale PEC‐WS, emphasizing artificial intelligence (AI) and machine learning (ML) support. We discuss three main intrinsic and extrinsic strategies that include (i) AI‐driven crystal structure modification and faceted growth control, (ii) physical and chemical surface passivation coupled with machine learning for passivation material screening strategies, and (iii) combining III–V semiconductors with appropriate band‐aligned materials via preferable heterojunctions, utilizing algorithms to select optimal compositions. We examine the underlying mechanisms of each strategy and conclude with perspectives on achieving optimal materials for PEC‐WS using advanced AI and ML screening tools. Implementing these effective modification strategies, the wide commercial potential of III–V photoelectrodes for PEC‐WS technology can be unlocked.