Language-model-derived training data for polymer glass transition prediction: a controlled benchmark
Year: 2026
Type: preprint
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.
Source: ChemRxiv
Author: Rostislav Rusev
Institution: Prof. Assen Zlatarov University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214125875