{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.02883",
  "author_total": 2,
  "authors": [
    {
      "name": "Ritam Chakraborty"
    },
    {
      "name": "Arpan Das"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8h85y0tj8xnbp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.02883",
  "fulltext_url": null,
  "id": "8h85y0tj8xnbp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.02883",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7207553178",
  "pdf_url": "https://arxiv.org/pdf/2609.02883",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8h85y0tj8xnbp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2609.02883",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.02883"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Repairing PBE-Spurious Metallicity for HSE06-Level Screening of 2D Photocatalysts for Green Hydrogen Production",
  "title_text": "Repairing PBE-Spurious Metallicity for HSE06-Level Screening of 2D Photocatalysts for Green Hydrogen Production",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:16Z",
  "url": "https://research.draconitic.com/paper/8h85y0tj8xnbp",
  "venue": "arXiv",
  "versions": []
}