Authors: Jiaru Bai, Sebastian Mosbach, Connor J. Taylor, Dogancan Karan, Kok Foong Lee, Simon D. Rihm, Jethro Akroyd, Alexei A. Lapkin, and Markus Kraft*

• Two self-driving laboratories are connected via a dynamic knowledge graph.
• The knowledge graph self-evolves to achieve research goals set by scientists.
• Collaborative closed-loop optimisation enables fast advances to the Pareto front.
• FAIR data provenance is captured for all data flow and experimental operations.
• The approach is scalable with seamless integration of additional laboratories.

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.

• This paper draws from preprint 310: From Platform to Knowledge Graph: Distributed Self-Driving Laboratories
• Access the article at the publisher: DOI: 10.1038/s41467-023-44599-9

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