Machine Learning‐Accelerated Specific Surface Prediction Strategy in Janus‐Based Z‐Scheme Heterostructures for Efficient Photocatalytic Water Splitting

The rational design of efficient photocatalysts based on 2D Z-Scheme heterostructures is crucial for achieving solar-driven overall water splitting. Herein, a novel bidirectional data-mining strategy of Janus-based Z-Scheme heterostructures is proposed, which synergistically predict reduction photocatalysts (RP) and oxidation photocatalysts (OP) through a combined approach of machine learning and first-principles calculations. Guided by this theoretical strategy, the series of TiBrTe/ZrSeS heterostructures are successfully identified as representative catalysts for efficient photocatalytic water splitting. The most stable stacking configurations of the TiBrTe/ZrSeS heterostructure, Te-S (dipole-aligned) and Te-Se (dipole-misaligned), are identified to explore the effects of dipole alignment in Janus-based heterostructures. Further non-adiabatic molecular dynamics (NAMD) simulations revealed unique carrier dynamics in these two configurations: the Te-Se interface exhibits faster interlayer electron and hole transfer (τ_e _e = 0.35 ps, τ_h _h = 0.36 ps), whereas the Te-S interface features slower transfer (τ_e _e = 0.95 ps, τ_h _h = 0.51 ps) but comparable recombination behavior (τ_e _h = 0.40 ps vs. 0.27 ps). Notably, the interfacial charge transfer behavior exhibits a distinct acceleration trend under dipole-misaligned configurations, which is herein termed the Dipole Misalignment-Driven Carrier Behavior (DMDCB) mechanism. The elucidation of this mechanism lays a theoretical foundation for the design of high-performance Z-Scheme heterostructures water splitting photocatalysts.