(361g) From Platform to Knowledge Graph: Distributed Self-Driving Laboratories

The ability to integrate resources and share knowledge across organisations enables scientists to expedite the scientific discovery process, which is especially crucial in addressing emerging global challenges that require global solutions [1, 2]. In this work, we develop an architecture to enable distributed self-driving laboratories as part of The World Avatar project, an all-encompassing digital twin based on dynamic knowledge graph. Our approach utilises ontologies to capture the data and material flows involved in a design-make-test-analyse cycle, and employs autonomous agents as executable knowledge components to carry out the experimentation workflow. All data provenance is recorded following FAIR principles, ensuring its accessibility and interoperability. We demonstrate the practical application of our framework by linking two robotic setups in Cambridge and Singapore to achieve a collaborative closed-loop optimisation for an aldol condensation reaction in real time. The knowledge graph evolves on its own while progressing towards the research goals set by the scientist. The two robots effectively produced a Pareto front for the cost-yield optimisation problem over the course of two days of operation. This proof-of-concept demonstration highlights the potential of the framework to establish a globally collaborative research network and further advance scientific frontiers.

[1] Seifrid, M. et al. Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab. Acc. Chem. Res. 55, 2454–2466 (2022).

[2] Bai, J. et al. From Platform to Knowledge Graph: Evolution of Laboratory Automation. JACS Au 2, 292–309 (2022).

Figure 1: Overall dynamic knowledge graph approach enabling a distributed network of self-driving laboratories.