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A dynamic knowledge graph approach to distributed self-driving laboratories

Author

Listed:
  • Jiaru Bai

    (University of Cambridge)

  • Sebastian Mosbach

    (University of Cambridge
    Cambridge Centre for Advanced Research and Education in Singapore (CARES))

  • Connor J. Taylor

    (Astex Pharmaceuticals
    University of Cambridge
    University of Nottingham)

  • Dogancan Karan

    (Cambridge Centre for Advanced Research and Education in Singapore (CARES))

  • Kok Foong Lee

    (CMCL Innovations)

  • Simon D. Rihm

    (University of Cambridge
    Cambridge Centre for Advanced Research and Education in Singapore (CARES))

  • Jethro Akroyd

    (University of Cambridge
    Cambridge Centre for Advanced Research and Education in Singapore (CARES))

  • Alexei A. Lapkin

    (University of Cambridge
    Cambridge Centre for Advanced Research and Education in Singapore (CARES)
    University of Cambridge)

  • Markus Kraft

    (University of Cambridge
    Cambridge Centre for Advanced Research and Education in Singapore (CARES)
    Nanyang Technological University
    The Alan Turing Institute)

Abstract

The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist’s research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.

Suggested Citation

  • Jiaru Bai & Sebastian Mosbach & Connor J. Taylor & Dogancan Karan & Kok Foong Lee & Simon D. Rihm & Jethro Akroyd & Alexei A. Lapkin & Markus Kraft, 2024. "A dynamic knowledge graph approach to distributed self-driving laboratories," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44599-9
    DOI: 10.1038/s41467-023-44599-9
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    References listed on IDEAS

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    1. Benjamin J. Shields & Jason Stevens & Jun Li & Marvin Parasram & Farhan Damani & Jesus I. Martinez Alvarado & Jacob M. Janey & Ryan P. Adams & Abigail G. Doyle, 2021. "Bayesian reaction optimization as a tool for chemical synthesis," Nature, Nature, vol. 590(7844), pages 89-96, February.
    2. Sourav Chatterjee & Mara Guidi & Peter H. Seeberger & Kerry Gilmore, 2020. "Automated radial synthesis of organic molecules," Nature, Nature, vol. 579(7799), pages 379-384, March.
    3. Eric Bradford & Artur M. Schweidtmann & Alexei Lapkin, 2018. "Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm," Journal of Global Optimization, Springer, vol. 71(2), pages 407-438, June.
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