{
  "abstract": "Machine-learning models of firefighting surfactants can appear persuasive when repeated measurements or closely related formulations occur on both sides of a random split. We reconstructed the public Hinnant surfactant workbook at cell-level provenance and evaluated fire extinction with explicit failure labels and a hierarchy of increasingly stringent chemical splits. The reconstructed data contain 917 heptane and 595 gasoline fire tests; heptane extinction comprised 733 successes, 179 documented failures, and 5 unlabeled observations, whereas gasoline comprised 554 successes and 41 failures. Structure-based analyses retained 114 formulations built from 50 single-structure and 22 polydisperse represented materials. For the fixed Extra Trees/combined representation, pooled heptane success ROC–AUC was 0.812 for row-random, 0.813 for replicate-disjoint, 0.805 for formulation-disjoint, and 0.783 for component-set-disjoint evaluation. At the prespecified 0.55 Tanimoto stress threshold, 114 formulations formed seven families of sizes 87, 11, 10, 3, 1, 1, and 1; only five families had labeled heptane observations. A fixed FireSuppressionFP/Extra Trees leave-one-family-out stress test gave pooled ROC–AUC 0.557. The three unique historical family partitions gave macro, size-weighted, and pooled ROC–AUC values of 0.694, 0.572, and 0.557, respectively; model-seed repeats were not counted as independent partitions. Extinction-time prediction remained limited: formulation-disjoint MAE was 38.9 s with pooled R2 0.306, while the family stress test yielded MAE 45.8 s and pooled R2 0.076; the partition-macro family R2 was -0.105. Thus, extinction success generalized across unseen formulations and component sets within represented chemistry, but coarse-family extrapolation was unstable and poorly constrained. Results are retrospective associations for 19 cm liquid-pool fires, not prospective validation or evidence of wildland-fire performance.",
  "abstract_shown": true,
  "affiliations": [
    "Stanford Online High School, 415 Broadway, Redwood City, California, 94063"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Aris Tsai",
      "orcid": "0009-0004-8641-7647"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aye4gw24k9mgm.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009599/v1",
  "fulltext_url": null,
  "id": "aye4gw24k9mgm",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009599/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7214703609",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009599/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/aye4gw24k9mgm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009599",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009599/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Chemical Generalization Boundaries in Fire-Extinction Prediction from a Public Surfactant Formulation Database",
  "title_text": "Chemical Generalization Boundaries in Fire-Extinction Prediction from a Public Surfactant Formulation Database",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/aye4gw24k9mgm",
  "venue": "ChemRxiv",
  "versions": []
}