{
  "abstract": "Experimental polymer property data are scarce and costly, whereas large language models (LLMs) have absorbed much of the chemical literature and can be queried for a fraction of a cent per polymer. We ask whether LLM-produced data can replace or supplement experimental data for predicting the glass transition temperature (Tg). With Claude Sonnet 5 as the teacher we built two synthetic datasets for USD 10.28: a generated set of 7,912 polymers whose structures and Tg values were both written by the LLM, and a labeled set of 9,925 hypothetical PI1M polymers whose Tg the LLM predicted. Both were cleaned with RDKit, deduplicated with a cut-invariant polymer key, and purged of every polymer of a held-out test set (1,471 polymers) and validation set (589) drawn from a curated experimental collection. A fine-tuned MoLFormer student was trained along a learning curve of 250 to 5,297 real polymers, with and without a preceding stage on synthetic data, and compared with a fingerprint random forest, two controls (self-training pseudo-labels and shuffled labels) and the teacher’s own zero-shot predictions, over 5 seeds. Trained on synthetic data alone, the student reached a mean absolute error (MAE) of 36.8 ◦C (generated) and 38.5 ◦C (labeled), within about 1 ◦C of the teacher (37.3 ◦C) and far from a student trained on all real data (25.2 ◦C). Synthetic data helped only when real data were scarce: with 250 or 500 real polymers, a synthetic first stage reduced the MAE by 3 ◦C to 5 ◦C on average and on every seed, a gain that shuffled labels abolished and that free self-training pseudo-labels reproduced only in part; against a baseline trained with more patient early stopping it was 3 ◦C to 4 ◦C at 250 real polymers and about 2 ◦C at 500; with 5,297 real polymers the gain was below 1 ◦C and not distinguishable from zero. Of the unique generated polymers, 8.3 % were copies of real polymers, 56 % of the valid generated pairs were repeats, and the teacher’s labels were rounded to a few values and shifted to lower Tg. LLM-produced data are thus a cheap and, below a few hundred measurements, useful supplement, not a substitute for measurements.",
  "abstract_shown": true,
  "affiliations": [
    "Burgas State University “Prof. Dr. Assen Zlatarov"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Rostislav Rusev",
      "orcid": "0009-0008-0836-2918"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9gkcceebtvz68.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009353/v1",
  "fulltext_url": null,
  "id": "9gkcceebtvz68",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009353/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": "W7214125875",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009353/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/9gkcceebtvz68.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009353",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009353/v2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Language-model-derived training data for polymer glass transition prediction: a controlled benchmark",
  "title_text": "Language-model-derived training data for polymer glass transition prediction: a controlled benchmark",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/9gkcceebtvz68",
  "venue": "ChemRxiv",
  "versions": []
}