From Platform to Knowledge Graph: Distributed Self-Driving Laboratories

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 to enable distributed self-driving laboratories as part of The World Avatar project, which seeks to demonstrate how to create an all-encompassing digital twin based on a dynamic knowledge graph. Our approach utilises ontologies to capture the data and material flows involved in design-make-test-analyse cycles, and employs autonomous agents as executable knowledge components to carry out the experimentation workflow. All data provenance is recorded following the FAIR principles, ensuring its accessibility and interoperability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore to achieve a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real time. The knowledge graph evolves autonomously 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 three days of operation.

A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics

“…25 In particular, TSEMO has been widely utilized to solve various multi-objective problems including optimization of reaction conditions, [26][27][28][29] solvent selection, 30 optimization including life-cycle assessment, 31 and distributed self-driving laboratories. 32 Algorithmic optimization of the process parameters of an ultra-fast lithium-halogen exchange reaction via Bayesian optimization has recently been demonstrated by Ahn and coworkers. 16 A single-objective optimization approach was used, which provided limited information about the trade-offs between the conflicting objectives (yield vs. impurity) and the influence of the different process parameters on the target objectives.…”