Our research develops simulation methods that model chemical reactions as they happen inside a polymer melt, with the aim of designing the additives needed to recycle mixed plastic waste. Different plastics do not mix, so recycling them together requires small molecules that stitch the two materials to one another during processing — but a molecule that works for one pair of plastics often fails for another. We are particularly interested in the competition between how quickly these molecules move through the melt and how quickly they react, which determines whether they end up in the right place to do their job, and in turning that understanding into quantities that can be computed fast enough to screen thousands of candidate chemistries. More broadly, we aim to make these additives designable rather than discovered by trial and error, with machine learning proposing new candidates that are then synthesised and tested. The example shown illustrates this approach for a blend of two common plastics, where a single added molecule transforms a brittle, easily separated material into one that holds together under stretching.

Our research develops automated experimentation platforms that combine continuous-flow chemistry with machine learning-guided optimization to accelerate the discovery and understanding of new chemical reactions. We are particularly interested in integrating flow reactors with in-line analytical techniques and active-learning algorithms, allowing the outcome of each experiment to guide the design of the next and enabling efficient exploration of complex, multidimensional reaction spaces. More broadly, we aim to establish self-optimizing flow as a practical tool for developing new synthetic methodology, not merely for optimizing reactions whose conditions are already known. The example shown illustrates this approach through the optimization of a challenging reaction system, where systematic exploration of reaction conditions is used both to identify effective conditions and to reveal the factors that govern reaction performance.

Our research develops molecular simulation approaches that link the chemistry of individual molecules to the behaviour of the nanoparticles they assemble into, with the aim of designing drug carriers before they are made. We are particularly interested in calculating simple energetic quantities that predict what a loaded nanoparticle will do when it meets a cell membrane — whether it stays intact, dissolves into the membrane, or releases its cargo first — allowing large numbers of candidate chemistries to be ranked quickly and the most promising few to be examined in atomistic detail. More broadly, we aim to establish molecular design as a practical route to programmable drug release, not merely as a way of explaining the behaviour of carriers that already exist. The example shown illustrates this approach for fluorinated nanocarriers, where small changes in molecular structure are used both to control the shape of the particle and to select the mechanism by which it delivers its cargo.