Led by John Ward (University of Liverpool)
Creating a data-rich, digitally enabled framework for accelerating the discovery and optimisation of sustainable organocatalysts.
While asymmetric organocatalysis has transformed synthetic chemistry, mechanistic understanding—particularly in heterogeneous systems—lags behind that of metal catalysis . This project integrates advanced kinetic methodologies (RPKA, VTNA and TSR), automated reaction monitoring, and digital modelling to capture full reaction profiles rather than single end-point measurements . By directly comparing homogeneous diarylprolinol ether catalysts with immobilised variants embedded in covalent organic frameworks (COFs), the team will disentangle intrinsic catalytic turnover from extrinsic transport and adsorption effects —a long-standing barrier to the rational design of recyclable heterogeneous organocatalysts.
A central innovation is the creation of machine-learning-ready kinetic datasets and digital pipelines that treat reaction profiles as first-class mechanistic descriptors. Automated experimentation, cloud-based data repositories and ML-based mechanistic discrimination will establish a reproducible platform for digital catalysis, directly advancing the Hub’s Digital and Advanced Characterisation themes. The project benefits from exceptional industrial and national engagement, with in-kind support from AstraZeneca and GSK and integration with the AIchemy Hub to embed AI-driven kinetic analysis. By combining kinetics, flow technologies and data science in a tightly coordinated programme, this work positions the UK at the forefront of digitally enabled, mechanism-led catalyst design and provides a scalable platform for sustainable organocatalysis in pharmaceutical and fine chemical manufacture.
