{
  "abstract": "High-throughput computational databases have become central to the discovery and screening of inorganic materials, yet the reproducibility of candidate selection across database versions has rarely been quantified. Here, a frozen 2018 Materials Project snapshot was harmonized with database version 2026.04.13 to evaluate the cross-version stability of thermodynamic and electronic screening. Among 78,756 historical mp-* records in the primary longitudinal cohort, 76,931 remained traceable (97.68%). Despite high cross-version property rank agreement, screening reversal rates ranged from 1.2% to 8.1% across the 16 combined threshold pairs. Under the representative criterion Ehull ≤ 0.05 eV·atom−1 and Eg ≥ 2 eV, 15,754 materials were selected in 2018 and 14,191 in 2026; 1818 original candidates exited and 255 entered, corresponding to 88.46% candidate retention and a Jaccard similarity of 0.871. Instability was concentrated near decision boundaries and remained chemically structured after multivariable adjustment. Cl-, Br-, and N-containing materials were dominated by energy-above-hull threshold crossings, whereas the 3d-series metal group showed a larger contribution from band-gap changes. These results distinguish property reproducibility from decision reproducibility: strong cross-version property agreement does not guarantee stable candidate sets. The database version should therefore be treated as essential reproducibility metadata in high-throughput inorganic materials screening.",
  "abstract_shown": true,
  "affiliations": [
    "Intelligent Manufacturing School, Jilin Tiedao University, Jilin 132299, China",
    "School of Energy and Mechanical Engineering, Dezhou University, Dezhou 253023, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Yunfei Lou"
    },
    {
      "affs": [
        1
      ],
      "name": "Ningning Wei",
      "orcid": "0009-0000-6888-6192"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8ce5tv2w1ejqh.bib",
  "cited_by": 0,
  "doi": "10.3390/inorganics14110269",
  "fulltext_url": null,
  "id": "8ce5tv2w1ejqh",
  "issns": [
    "2304-6740"
  ],
  "landing_url": "https://www.mdpi.com/2304-6740/14/11/269",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7223704044",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/8ce5tv2w1ejqh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:46Z",
      "native_id": "10.3390/inorganics14110269",
      "source": "crossref",
      "url": "https://www.mdpi.com/2304-6740/14/11/269"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Reproducibility of Thermodynamic Stability and Band-Gap Screening of Inorganic Materials Across Materials Project Versions",
  "title_text": "Reproducibility of Thermodynamic Stability and Band-Gap Screening of Inorganic Materials Across Materials Project Versions",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:46Z",
  "url": "https://research.draconitic.com/paper/8ce5tv2w1ejqh",
  "venue": "Inorganics",
  "versions": []
}