ArticleMacromolecular rapid communications2026
Benchmarking Large Language Models for Polymer Property Predictions.
Article in Macromolecular rapid communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Self-driving lab for the data-driven design of single-chain polymer nanoparticles.Cell press blue · 2026Article
- Benchmarking Large Language Models for Polymer Property Predictions.Macromolecular rapid communications · 2026Article
- Identifying Polymers that Bind or Reject Proteins with Machine Learning: Handling Categorical Features within a GPR Model.ACS polymers Au · 2026Article
- Machine Learning for Green Solvents: Assessment, Selection and Substitution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Machine learning and artificial intelligence have revolutionized polymer science by enhancing the ability to rapidly predict key polymer properties and enabling generative design. The utilization of large language models (LLMs) in polymer informatics may offers additional opportunities for advancement. Unlike traditional methods that depend on large labeled datasets, hand-crafted representations of the materials, and complex feature engineering, LLM-based methods utilize natural language inputs via a transfer learning process and eliminate the need for complex representation and fingerprinting, thus significantly simplifying the training process. In this study, we fine-tune 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 (
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Registered trials
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