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 EgHSE − EgPBE for the corrected insulating population. The curated XGBoost regressor reconstructs HSE06 gaps with a mean absolute error of 0.108~eV (R2=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.
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BibTeX
@misc{chakraborty2026repairing,
title = {{Repairing PBE-Spurious Metallicity for HSE06-Level Screening of 2D Photocatalysts for Green Hydrogen Production}},
author = {Ritam Chakraborty and Arpan Das},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.02883},
eprint = {2609.02883},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.02883},
}
RIS
TY - UNPB
TI - Repairing PBE-Spurious Metallicity for HSE06-Level Screening of 2D Photocatalysts for Green Hydrogen Production
AU - Ritam Chakraborty
AU - Arpan Das
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2609.02883
UR - https://arxiv.org/abs/2609.02883
AB - 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.
ER -