The development of 'intelligent' reactors capable of autonomously optimising chemical processes is a critical challenge in synthetic chemistry, potentially enabling the production of higher performing materials with improved yields and at lower costs. Such reactors typically combine a network of fluidic components for carrying out each step of the reaction, in-line sensors for monitoring the reaction at one or more locations along the fluidic network, and machine learning systems that intelligently update the reaction conditions until products with desired properties are obtained. We are working to improve the performance of self-optimising reactors by developing:

1. Compatible chemistries capable of providing the high levels of control, reproducibility and synthetic versatility needed for self-optimisation and molecular discovery
2. Self-contained reactors that enable the aforementioned chemistries to be carried out in a fully automated manner, with precise control over all relevant reaction parameters
3. In-line analysis tools for the real-time interrogation of reaction progression and determination of product quality
4. Data-processing protocols and algorithms capable of extracting relevant data from the in-line analysis tools and iterating reaction conditions until a desired outcome has been achieved