{
  "abstract": "Predicting properties from coordinate-category data-sets of vectors paired with categorical information-is fundamental to computational science, manifesting in materials science as predicting formation energies or elastic moduli from crystal structures. While large language models (LLMs) have increasingly been applied to such tasks, optimal strategies for achieving reliable predictions remain elusive. Here we report fundamental limitations in LLM's ability to learn from coordinate information in coordinate-category data. Through systematic experiments using synthetic datasets with tunable coordinate and category contributions, and a comprehensive benchmarking framework (MatText), we find that LLMs consistently fail to capture coordinate information while excelling at category patterns. This geometric blindness persists regardless of model size (up to 70 billion parameters), dataset scale (up to 2 × 10⁶ structures) or text representation strategy. For material property prediction tasks dominated by structural effects, specialized geometric architectures consistently outperform LLMs, as evidenced by a clear 'Graph Neural Network Language Model (GNN-LM) wall' in performance benchmarks. Our analysis provides concrete guidelines for architecture selection in scientific machine learning.",
  "abstract_shown": true,
  "affiliations": [
    "Laboratory of Organic and Macromolecular Chemistry, Friedrich Schiller University Jena, Jena, Germany. nawaf.alampara@uni-jena.de",
    "Intel Labs, Santa Clara, CA, USA",
    "Lila Sciences, Cambridge, MA, USA",
    "Center for Energy and Environmental Chemistry Jena, Friedrich Schiller University Jena, Jena, Germany. mail@kjablonka.com",
    "Helmholtz Institute for Polymers in Energy Applications Jena, Jena, Germany. mail@kjablonka.com",
    "Jena Center for Soft Matter, Friedrich Schiller University Jena, Jena, Germany. mail@kjablonka.com",
    "Laboratory of Organic and Macromolecular Chemistry, Friedrich Schiller University Jena, Jena, Germany. mail@kjablonka.com"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nawaf Alampara",
      "orcid": "0009-0001-7697-7315"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Santiago Miret",
      "orcid": "0000-0002-5121-3853"
    },
    {
      "affs": [
        3,
        4,
        5,
        6
      ],
      "name": "Kevin Maik Jablonka",
      "orcid": "0000-0003-4894-4660"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7t5116bam8d6k.bib",
  "cited_by": 33,
  "doi": "10.1038/s43588-026-01045-2",
  "fulltext_url": "https://europepmc.org/articles/PMC13686937",
  "id": "7t5116bam8d6k",
  "issns": [
    "2662-8457"
  ],
  "landing_url": "https://europepmc.org/article/MED/42864435",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W4400064820",
  "pdf_url": "https://europepmc.org/articles/PMC13686937?pdf=render",
  "pmcid": "PMC13686937",
  "pmid": "42864435",
  "published": "2026-10-12",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/7t5116bam8d6k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:00Z",
      "native_id": "MED:42864435",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42864435"
    },
    {
      "first_seen": "2026-10-30T13:27:00Z",
      "native_id": "W4400064820",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s43588-026-01045-2"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Less can be more for predicting properties with large language models.",
  "title_text": "Less can be more for predicting properties with large language models.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:22Z",
  "url": "https://research.draconitic.com/paper/7t5116bam8d6k",
  "venue": "Nature computational science",
  "versions": []
}