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A dynamic knowledge graph approach to distributed self-driving laboratories

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Taylor, Connor J 
Karan, Dogancan 
Lee, Kok Foong 


jats:titleAbstract</jats:title>jats:pThe 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.</jats:p>


Acknowledgements: This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, and Pharma Innovation Platform Singapore (PIPS) via grant to CARES Ltd “Data2Knowledge, C12”. This project was cofunded by European Regional Development Fund via the project “Innovation Centre in Digital Molecular Technologies”, UKRI via project EP/S024220/1 “EPSRC Centre for Doctoral Training in Automated Chemical Synthesis Enabled by Digital Molecular Technologies”. Part of this work was also supported by Towards Turing 2.0 under the EPSRC Grant EP/W037211/1. The authors thank Dr. Andrew C. Breeson for his helpful suggestions on graphical design. J.B. acknowledges financial support provided by CSC Cambridge International Scholarship from Cambridge Trust and China Scholarship Council. C.J.T. is a Sustaining Innovation Postdoctoral Research Associate at Astex Pharmaceuticals and thanks Astex Pharmaceuticals for funding, as well as his Astex colleagues Chris Johnson, Rachel Grainger, Mark Wade, Gianni Chessari, and David Rees for their support. S.D.R. acknowledges financial support from Fitzwilliam College, Cambridge, and the Cambridge Trust. M.K. gratefully acknowledges the support of the Alexander von Humboldt Foundation. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

Funder: Alexander von Humboldt-Stiftung (Alexander von Humboldt Foundation); doi:

Funder: Pharma Innovation Platform Singapore (PIPS) via grant to CARES 5 Ltd “Data2Knowledge, C12”


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Nature Communications

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Springer Science and Business Media LLC
RCUK | Engineering and Physical Sciences Research Council (EPSRC) (EP/W037211/1)
Cambridge Commonwealth, European and International Trust (Cambridge Commonwealth, European & International Trust) (CSC Cambridge International Scholarship)
China Scholarship Council (CSC) (CSC Cambridge International Scholarship)