{
  "abstract": "Pretraining a network on QM9 quantum-chemical labels and transferring it to a data-scarce molecular endpoint is a common strategy in chemical machine learning, and a growing literature proposes tasksimilarity descriptors for deciding which source task to transfer from. We audit both decisions on two non-synthetic computational chemistry datasets — DFT-derived oxidation potentials (OxPot, 15,238 molecules) and redox reaction energies (RedDB, 15,551 molecules) — using 24 independently initialised QM9 source models spanning eight quantum-chemical tasks. We first establish, in a controlled synthetic benchmark with known informative coordinates, that magnitude-pruning masks can be aggregated into interpretable task-level feature-usage signatures that recover ground-truth feature overlap (pooled mean Spearman 0.2749, versus 0.1755 for unpruned first-layer weights and 0.0614 for a random control). Eleven signature series were compared; their relative ordering proved sensitive to pooling, with gradient saliency ahead in the unmatched pooled summary (0.2855) and behind by 0.211 in a paired same-setting audit. This mechanism did not translate into deployable source selection: on the two molecular datasets every selector produced negative mean transfer gain under full-weight transfer (feature-similarity top-1: −0.124, negative-transfer rate 95.8%; pseudo-label top-1: −0.177, 100%), and an agreement gate limited harm mainly by abstaining in 87.5% of cases. We then evaluated 144 matched case–source pairs under four transfer protocols (576 transfer runs). At the target-case level, resetting the output head improved relative gain by 0.087 (case-bootstrap 95% CI 0.070–0.106; 18/18 cases positive), and restricting transfer to the first layer added 0.044 (95% CI 0.034–0.055; 18/18 positive). Protocol changes produced the largest and most consistent shifts in realised gain. Only under the most conservative protocol did any dataset reach non-negative mean transfer (OxPot, +0.003; RedDB remained negative). For this setting, transfer protocol and an explicit abstention option deserve priority over increasingly elaborate sourcesimilarity descriptors.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Yueru Zhang",
      "orcid": "0009-0000-3912-3882"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9mvx6hft9w2fs.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008164/v1",
  "fulltext_url": null,
  "id": "9mvx6hft9w2fs",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008164/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7204904671",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008164/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/9mvx6hft9w2fs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008164",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008164/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Transfer Protocol Matters More Than Source-Task Selection in Low-Data Molecular Property Prediction: An Audit of QM9 Pretraining on Computed Oxidation Potentials and Redox Reaction Energies",
  "title_text": "Transfer Protocol Matters More Than Source-Task Selection in Low-Data Molecular Property Prediction: An Audit of QM9 Pretraining on Computed Oxidation Potentials and Redox Reaction Energies",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/9mvx6hft9w2fs",
  "venue": "ChemRxiv",
  "versions": []
}