Automated Flow Synthesis: In-Line Reaction Monitoring and Machine Learning for the Optimisation of Continuous Flow Photocatalytic Reactions

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This is a PhD research project in Chemistry. We propose a new and ambitious project to utilise in-line benchtop NMR and/or UV-vis monitoring of continuous flow photocatalytic radical decarboxylation reactions, exploiting real time data analytics in a digital feedback-loop to optimise several reaction parameters. Photocatalytic direct C-H Minisci functionalisations of heterocycles will be developed and optimised in this way, as these scaffolds are highly relevant to drug discovery programmes. Our system will enable technology for more efficient photocatalytic reactions including scale up processes compared to batch reactions. The project aims to provide proof-of-principle towards an ultimate goal of automated development and optimisation, using control theory and machine learning techniques, of continuous flow photocatalytic reactions. Stepwise objectives are therefore: 1. Optimise in continuous flow the photochemical Minisci transformations 2. Integrate in-line/on-line monitoring systems (NMR and UV-Vis) with the flow system as a unified platform for the generation of training data 3. Model the input/output processes of the continuous flow and design control/reinforcement learning algorithms to maximise meaningful metrics (e.g., selectivities, conversions and yields)