{
  "abstract": "We present Experiment Automation Agents (EAA), a vision-language-model-driven agentic system designed to automate complex experimental microscopy workflows. EAA integrates multimodal reasoning, tool-augmented action, and optional long-term memory to support both autonomous procedures and interactive user-guided measurements. Built on a flexible task-manager architecture, the system enables workflows ranging from fully agent-driven automation to logic-defined routines that embed localized LLM queries. EAA further provides a modern tool ecosystem with two-way compatibility for Model Context Protocol (MCP), allowing instrument-control tools to be consumed or served across applications. We demonstrate EAA at an imaging beamline at the Advanced Photon Source, including automated zone plate focusing, natural language-described feature search, and interactive data acquisition. These results illustrate how vision-capable agents can enhance beamline efficiency, reduce operational burden, and lower the expertise barrier for users.",
  "abstract_shown": true,
  "affiliations": [
    "Advanced Photon Source, Argonne National Laboratory, Lemont, IL, USA",
    "Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA",
    "Department of Radiation Oncology, Northwestern University, Chicago, IL, USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ming Du"
    },
    {
      "affs": [
        0
      ],
      "name": "Yanqi Luo",
      "orcid": "0000-0001-5624-9583"
    },
    {
      "affs": [
        1
      ],
      "name": "Srutarshi Banerjee",
      "orcid": "0000-0002-9365-5529"
    },
    {
      "affs": [
        0
      ],
      "name": "Michael Wojcik",
      "orcid": "0000-0003-1257-870X"
    },
    {
      "affs": [
        2
      ],
      "name": "Jelena Popovic"
    },
    {
      "affs": [
        0
      ],
      "name": "Mathew J. Cherukara"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1c831gvq8pmq9.bib",
  "cited_by": 6,
  "doi": "10.1038/s41524-026-02213-8",
  "fulltext_url": null,
  "id": "1c831gvq8pmq9",
  "issns": [
    "2057-3960"
  ],
  "landing_url": "https://doi.org/10.1038/s41524-026-02213-8",
  "language": "en",
  "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": "W7171797689",
  "pdf_url": "https://www.nature.com/articles/s41524-026-02213-8_reference.pdf",
  "pmcid": "PMC13633048",
  "pmid": "42828383",
  "published": "2026-07-30",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/1c831gvq8pmq9.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:21:07Z",
      "native_id": "W7171797689",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41524-026-02213-8"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Experiment automation agents: automating materials characterization with vision language model agents",
  "title_text": "Experiment automation agents: automating materials characterization with vision language model agents",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:23Z",
  "url": "https://research.draconitic.com/paper/1c831gvq8pmq9",
  "venue": "npj Computational Materials",
  "versions": []
}