{
  "abstract": "Scientific communication has repeatedly accelerated when the objects used to preserve and exchange knowledge became easier to validate, search, and reuse. The scientific paper remains a human-readable compression of a richer scientific state: quantitative physical and observational models, their uncertainty and validity, and the evidence that supports or challenges them. We propose Physical Knowledge Models (PKMs) as explicit, versioned, composable, and transferable representations of this state. The intellectual object per se is not fundamentally new: expert mental models, comprehensive textbooks, and domain model repositories already integrate equations, mechanisms, observations, and validity judgments. The proposed advance is to make that integrated understanding machine-readable and directly connected to experimentation. Broad PKMs can be projected into reduced experiment-specific models; local evidence produces versioned knowledge proposals rather than silently overwriting community knowledge; and a Knowledge Integration Layer aligns partially overlapping models, evaluates evidence and beliefs, preserves competing branches, and exposes contradictions requiring further tests. Because the ontology itself can change, PKMs retain persistent links to original measurements, sample histories, and instrument and decision traces so that prior evidence can be reinterpreted under revised concepts and models. Coupled to experimental planning, this creates a structured route from shared knowledge to local experiment and back. Data, code, digital twins, and agents remain essential, but they become mechanisms for populating, interrogating, testing, and updating durable physical knowledge rather than substitutes for it.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Materials Science and Engineering",
    "University of Tennessee",
    "Department of Theory and Practice in Teacher Education, College of Education, Health, and Human Sciences",
    "Min H. Kao Department of Electrical Engineering and Computer Science"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Sergei V. Kalinin"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Boris N. Slautin",
      "orcid": "0000-0002-1237-7070"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Sheryl Sanchez"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Yu Liu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Mahshid Ahmadi"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Mehmet Aydeniz"
    },
    {
      "affs": [
        3,
        1
      ],
      "name": "Hairong Qi"
    },
    {
      "affs": [
        3,
        1
      ],
      "name": "Qing Cao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4eafxhtkcmjxy.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009660/v1",
  "fulltext_url": null,
  "id": "4eafxhtkcmjxy",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009660/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",
      "1802|Scientific Computing and Data Management"
    ]
  ],
  "openalex": "W7214807782",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009660/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/4eafxhtkcmjxy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009660",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009660/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "From Publications to Physical Knowledge Models: Transferable physical understanding for AI-native experimental science",
  "title_text": "From Publications to Physical Knowledge Models: Transferable physical understanding for AI-native experimental science",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/4eafxhtkcmjxy",
  "venue": "ChemRxiv",
  "versions": []
}