{
  "abstract": "The singlet–triplet gap ∆ESTis the screening parameter for thermally activated delayed fluorescence (TADF), yet independent experimental reports of the same compound scatter by 0.072 eV RMS, and the per-molecule median regressed on is known only to 0.049 eV, about half of any model’s error. This label noise sets a ceiling on every predictor, yet the models sit below it: neither noise nor capacity alone accounts for the remaining error. Against that ceiling, two-dimensional structure predicts as accurately as a semiempirical excited-state calculation: on 231 donor–acceptor molecules (212 Bemis–Murcko scaffolds, scaffold cross-validation), random forests from Morgan fingerprints (MAE 0.091 eV) and RDKit descriptors (0.100 eV) match natural-transition-orbital descriptors from a GFN2-xTB/sTDA excited state (0.096 eV) to within 0.017 eV on paired folds, so the excited-state step buys no accuracy. Two further results are specific to this corpus. First, under a source-paper split the accuracy advantage over a mean-value baseline disappears, but the excited-state descriptors keep the rank correlation the fingerprints lose (paired ∆ρ = 0.19 on a single split, 95 % CI narrowly excluding zero): they order candidates across laboratories without predicting the gap. Second, the leakage that inflates many computed-gap models is learner-dependent—given the S1 and T1 energies as features, a linear model returns ∆EST ≡ ES1 − ET1 exactly (R2 = 1.000) while an axis-aligned forest on the same features recovers only R2 = 0.59 and clears cross-validation unremarked. Progress past the ≈ 0.1eV ceiling needs a curated, solvent-controlled, single-source experimental benchmark—one that would also expose the remaining modelling headroom. Data, models, and code are available (Zenodo DOI: 10.5281/zenodo.17436069).",
  "abstract_shown": true,
  "affiliations": [
    "Department of Physics, Faculty of Science",
    "University of Yaoundé",
    "University of Douala",
    "Faculty of Medicine and Health Sciences, Université de Sherbrooke, 3001, 12th Avenue Nord, Sherbrooke, QC, Canada, J1H 5N4"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Jean-Pierre TCHAPET NJAFA",
      "orcid": "0000-0002-1936-8353"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Steve Cabrel Teguia Kouam"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Patrick Sorrel Mvoto Kongo"
    },
    {
      "affs": [
        3
      ],
      "name": "Panebei Samafou",
      "orcid": "0000-0002-9683-7678"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Serge Guy Nana Engo",
      "orcid": "0000-0002-7484-3508"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ehemrc5jph55c.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010017/v1",
  "fulltext_url": null,
  "id": "ehemrc5jph55c",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010017/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": "W7220854461",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010017/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/ehemrc5jph55c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15010017",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010017/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Experimental Inter-Report Scatter Bounds Machine-Learning Accuracy for TADF Singlet–Triplet Gaps—Structure and Semi-Empirical Features Reach the Same Limit",
  "title_text": "Experimental Inter-Report Scatter Bounds Machine-Learning Accuracy for TADF Singlet–Triplet Gaps—Structure and Semi-Empirical Features Reach the Same Limit",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/ehemrc5jph55c",
  "venue": "ChemRxiv",
  "versions": []
}