Machine learning has revolutionized polymer science by enabling rapid property prediction and generative design. Large language models (LLMs) offer further opportunities in polymer informatics by simplifying workflows that traditionally rely on large labeled datasets, handcrafted representations, and complex feature engineering. LLMs leverage natural language inputs through transfer learning, eliminating the need for explicit fingerprinting and streamlining training. In this study, we finetune general purpose LLMs -- open-source LLaMA-3-8B and commercial GPT-3.5 -- on a curated dataset of 11,740 entries to predict key thermal properties: glass transition, melting, and decomposition temperatures. Using parameter-efficient fine-tuning and hyperparameter optimization, we benchmark these models against traditional fingerprinting-based approaches -- Polymer Genome, polyGNN, and polyBERT -- under single-task (ST) and multi-task (MT) learning. We find that while LLM-based methods approach traditional models in performance, they generally underperform in predictive accuracy and efficiency. LLaMA-3 consistently outperforms GPT-3.5, likely due to its tunable open-source architecture. Additionally, ST learning proves more effective than MT, as LLMs struggle to capture cross-property correlations, a key strength of traditional methods. Analysis of molecular embeddings reveals limitations of general purpose LLMs in representing nuanced chemo-structural information compared to handcrafted features and domain-specific embeddings. These findings provide insight into the interplay between molecular embeddings and natural language processing, guiding LLM selection for polymer informatics.
Gupta et al. (2025) studied this question.