{
  "abstract": "Foundation models have emerged as a powerful paradigm for learning transferable knowledge from broad, heterogeneous data and adapting them to diverse downstream tasks, driving a growing interest in AI-accelerated scientific discovery. In polymer science, this paradigm offers a promising route beyond conventional machine learning methods that are often confined to isolated tasks and struggle to generalize under data scarcity and across heterogeneous downstream settings. Polymer foundation models (PFMs), however, are not merely general scientific foundation models applied to polymer datasets. They must address representation, generation, transfer, and evaluation challenges specific to polymer informatics. This review synthesizes recent progress in PFMs for representation learning and generative modeling, spanning models designed for polymer systems and those adapted from general foundation models. We organize this review around two stages: (1) polymer modeling as the foundation, covering representations, datasets, model architectures, and pretraining objectives; and (2) downstream applications, evaluation, and adaptation. By synthesizing recent progress, open challenges, and future directions, this review outlines a roadmap toward robust and generalizable foundation models for polymer informatics.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science and Engineering",
    "University of Notre Dame",
    "Department of Chemistry and Biochemistry",
    "Department of Aerospace and Mechanical Engineering",
    "Lucy Family Institute for Data and Society",
    "Department of Chemical and Biomolecular Engineering"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Yihan Zhu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Eric Inae"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Yuhan Liu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Weijiang Li"
    },
    {
      "affs": [
        3,
        1,
        4
      ],
      "name": "Sobin Alosious"
    },
    {
      "affs": [
        3,
        1
      ],
      "name": "Renzheng Zhang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Gang Liu"
    },
    {
      "affs": [
        3,
        1,
        4,
        5
      ],
      "name": "Tengfei Luo"
    },
    {
      "affs": [
        0,
        1,
        4
      ],
      "name": "Meng Jiang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/69fz5zes195t7.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009949/v1",
  "fulltext_url": null,
  "id": "69fz5zes195t7",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009949/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": "W7220681316",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009949/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/69fz5zes195t7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15009949",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009949/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "A review on foundation models for polymer informatics",
  "title_text": "A review on foundation models for polymer informatics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/69fz5zes195t7",
  "venue": "ChemRxiv",
  "versions": []
}