Repairing PBE-Spurious Metallicity for HSE06-Level Screening of 2D Photocatalysts for Green Hydrogen Production

Abstract

Semilocal PBE calculations can remove viable photocatalysts before screening by labeling narrow-gap semiconductors as metals. We address this failure mode in the Computational 2D Materials Database (C2DB) by combining leakage-aware repair of PBE-spurious metallicity with HSE06--PBE $Δ$-learning. Stage~I classifies HSE06-unknown PBE metals using structural, chemical, magnetic, and stability descriptors, while excluding HSE06/GW quantities and PBE electronic shortcuts. Stage~II learns $E_g^{HSE} - E_g^{PBE}$ for the corrected insulating population. The curated XGBoost regressor reconstructs HSE06 gaps with a mean absolute error of 0.108~eV ($R^2=0.989$), compared with 1.036~eV for raw PBE. The Stage~I classifier is used only for triage because the labeled true-metal class contains 29 materials; its best holdout performance gives 87.5\% accuracy, 0.286 true-metal recall, and 0.643 balanced accuracy. The corrected pH~0 electronic screen yields 10 strict and 29 initial relaxed green-hydrogen photocatalyst candidates. Four strict and 18 relaxed candidates would fail the same 1.6--2.8~eV gap window at the PBE level. Targeted VASP HSE06 calculations for six ML-predicted compounds give material-level MAEs of 0.417, 0.182, and 0.110~eV for the C2DB-native, Magpie+structural, and curated models, respectively; \feat{1AgBr-1} shifts above the upper gap cutoff, leaving 28 retained relaxed candidates. The workflow shows that high-fidelity correction must be evaluated by candidate membership, not only by global regression error.