A dynamic knowledge graph approach to distributed self-driving laboratories

Jiaru Bai, Sebastian Mosbach, Connor J. Taylor, Dogancan Karan, Kok Foong Lee, Simon D. Rihm, Jethro Akroyd, Alexei A. Lapkin and Markus Kraft ()
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Jiaru Bai: University of Cambridge
Sebastian Mosbach: University of Cambridge
Connor J. Taylor: Astex Pharmaceuticals
Dogancan Karan: Cambridge Centre for Advanced Research and Education in Singapore (CARES)
Kok Foong Lee: CMCL Innovations
Simon D. Rihm: University of Cambridge
Jethro Akroyd: University of Cambridge
Alexei A. Lapkin: University of Cambridge
Markus Kraft: University of Cambridge

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-023-44599-9

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