  Additive manufacturing has enabled the fabrication of advanced reactor
geometries, permitting larger, more complex design spaces. Identifying
promising configurations within such spaces presents a significant challenge
for current approaches. Furthermore, existing parameterisations of reactor
geometries are low-dimensional with expensive optimisation limiting more
complex solutions. To address this challenge, we establish a machine
learning-assisted approach for the design of the next-generation of chemical
reactors, combining the application of high-dimensional parameterisations,
computational fluid dynamics, and multi-fidelity Bayesian optimisation. We
associate the development of mixing-enhancing vortical flow structures in novel
coiled reactors with performance, and use our approach to identify key
characteristics of optimal designs. By appealing to the principles of flow
dynamics, we rationalise the selection of novel design features that lead to
experimental plug flow performance improvements of 60% over conventional
designs. Our results demonstrate that coupling advanced manufacturing
techniques with `augmented-intelligence' approaches can lead to superior design
performance and, consequently, emissions-reduction and sustainability.
