{
  "abstract": "Self-driving laboratories are transforming experimental science, shifting laboratory operation from human-centric schedules towards continuous robotic execution. Beyond accelerating discovery, this transition creates new flexibility in when scientific research consumes electricity, offering an opportunity to coordinate experimental activity with changing electricity-system conditions. Here, we develop and deploy a dynamic, sustainability-aware laboratory-level scheduling framework that combines reinforcement learning with constraint programming to jointly optimize completion time and electricity cost under scientific constraints. Physical execution of 36 experimental tasks spanning six chemical workflow categories shortened completion time by 39.1% and reduced estimated electricity costs by 28.9% relative to a simulated human-centric scheduling baseline. Across five representative tariff cases, estimated per-workflow savings were equivalent to the cost of 0.83–9.14 kWh of off-peak electricity. Our findings demonstrate how self-driving laboratories can translate operational freedom into electricity-demand flexibility, establishing energy responsiveness as a design consideration for scientific research infrastructure.",
  "abstract_shown": true,
  "affiliations": [
    "Key Laboratory of Precision and Intelligent Chemistry",
    "Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China",
    "School of Information Science and Technology",
    "University of Science and Technology of China",
    "Hefei National Laboratory"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Junyi Zhou"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Luyao Ge"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Lulu Guo"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Chen Qian"
    },
    {
      "affs": [
        0,
        1,
        4,
        3
      ],
      "name": "Jun Jiang"
    },
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Weiwei Shang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Xiaobo Li",
      "orcid": "0000-0002-2752-749X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5hp64s6bg4gb8.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010960/v1",
  "fulltext_url": null,
  "id": "5hp64s6bg4gb8",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010960/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2208|Smart Grid Energy Management"
    ]
  ],
  "openalex": "W7223288082",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010960/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/5hp64s6bg4gb8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15010960",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010960/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "A sustainability-aware self-driving laboratory",
  "title_text": "A sustainability-aware self-driving laboratory",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/5hp64s6bg4gb8",
  "venue": "ChemRxiv",
  "versions": []
}