Abstract
Photocatalytic water splitting is considered a promising pathway for hydrogen production. However, the practical application of this process is hindered by various challenges, including (i) rapid charge carrier recombination, (ii) photocorrosion, and (iii) particle aggregation, which collectively limit the efficiency and stability of photocatalysts. Herein, cadmium sulfide (CdS) has gained significant attention as a visible-light-active photocatalyst due to its suitable band gap and favorable band edge positions. Nevertheless, pristine CdS suffers intrinsically from ultrafast charge recombination and severe photocorrosion under illumination, restricting its practical application. This review provides critical insights into the design and performance of pristine, binary, and ternary CdS-based photocatalysts for efficient water splitting. This review comprehensively surveys density functional theory (DFT) studies elucidating the effects of defect engineering, cocatalysts, heterojunctions, and CdS-related systems, including CdS/polymer composites, CdS/MOFs, and other hybrid structures, on photocatalytic performance. Furthermore, we examine the emerging role of machine learning (ML) in accelerating the discovery and optimization of CdS-based systems through predictive modeling and high-throughput screening. We conclude by identifying critical research gaps and offering future recommendations, emphasizing the integration of DFT and ML within closed-loop experimental frameworks to rationally design stable, high-performance CdS photocatalysts for practical solar hydrogen production.