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PreprintCC BY-NC-NDChemRxiv

A sustainability-aware self-driving laboratory

  • Junyi Zhou1,2,3,4
  • Luyao Ge1,2
  • Lulu Guo1,2
  • Chen Qian3,4
  • Jun Jiang1,2,5,4
  • Weiwei Shang1,2,3,4
  • Xiaobo Li1,2
5 affiliations
  1. 1Key Laboratory of Precision and Intelligent Chemistry
  2. 2Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China
  3. 3School of Information Science and Technology
  4. 4University of Science and Technology of China
  5. 5Hefei National Laboratory
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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.

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Cite

BibTeXRIS
BibTeX
@misc{zhou2026sustainabilityaware,
  title = {{A sustainability-aware self-driving laboratory}},
  author = {Junyi Zhou and Luyao Ge and Lulu Guo and Chen Qian and Jun Jiang and Weiwei Shang and Xiaobo Li},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15010960/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010960/v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - A sustainability-aware self-driving laboratory
AU  - Junyi Zhou
AU  - Luyao Ge
AU  - Lulu Guo
AU  - Chen Qian
AU  - Jun Jiang
AU  - Weiwei Shang
AU  - Xiaobo Li
PY  - 2026
DA  - 2026/10/23
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15010960/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010960/v1
AB  - 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.
ER  -