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

Abstract 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.