{
  "abstract": "Autonomous experimentation is emerging across materials synthesis, synchrotron and neutron characterization, real-time analysis, and scientific decision-making. Yet increasingly capable artificial intelligence does not by itself create autonomous science. Scientific user facilities must also provide machine-operable experiments, machine-readable and interoperable data and interfaces, persistent provenance across the experimental lifecycle, scientific capabilities that can be discovered and orchestrated across instruments and facilities, and access models compatible with iterative experimental campaigns. We argue that these infrastructure requirements should be treated as a coupled scientific design problem. Their value is not limited to higher throughput: reducing the latency between observation, interpretation, decision, and the next measurement can make metastable states, competing phases / phase coexistence, degradation, failure pathways, phase boundaries, and other difficult-to-access phenomena experimentally observable. Drawing on recent demonstrations in autonomous laboratories and advanced characterization, along with discussions from the 2026 Advanced Light Source Automation and Self-Driving Labs Workshop, we outline a practical progression from automated instruments to distributed autonomous discovery ecosystems that link synthesis, synchrotron and neutron characterization, computing, theory, and human scientific judgment.",
  "abstract_shown": true,
  "affiliations": [
    "Advanced Light Source",
    "Lawrence Berkeley National Laboratory",
    "Applied Mathematics and Computational Research Division",
    "Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Chenhui Zhu"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Marcus M. Noack"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Wiebke Koepp"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Thomas Ferron"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Oleg Kostko"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Damon English"
    },
    {
      "affs": [
        3
      ],
      "name": "Jeff Dickert"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Chris Jozwiak"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jinghua Guo"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Dilworth Y. Parkinson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4cs4e4hrx457n.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009126/v1",
  "fulltext_url": null,
  "id": "4cs4e4hrx457n",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009126/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": "W7213871660",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009126/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/4cs4e4hrx457n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15009126",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009126/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "From Automated Beamlines to Autonomous Science",
  "title_text": "From Automated Beamlines to Autonomous Science",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/4cs4e4hrx457n",
  "venue": "ChemRxiv",
  "versions": []
}