Our goal is to accelerate the discovery of advanced functional materials. We employ robotics,  computational prediction and digital optimization to develop autonomous methods that can speed up and refine the exploration of the vast chemical space.

Our principal research activities include:


Our group is active in a broad range of traditional as well as (semi)automated and high throughput methods for materials synthesis and characterisation in areas including polymers,  porous materials (e.g., conjugated microporous polymers, porous organic cages, covalent organic frameworks, and hydrogen-bonded organic frameworks) and photocatalysis (e.g., for photochemical water splitting, hydrogen peroxide production, CO2 reduction, or organic transformations).

We also use computational methods to design targeted functionality into organic solids by understanding and predicting the underlying molecular assembly processes.  We are closely integrating computation and experiment to discover new molecular crystals; for example, in collaboration with Professor Graeme Day (University of Southampton) and with Dr Kim Jelfs (Imperial College London).


Following on from our breakthrough discovery of Mobile Robotic Chemist in 2020, our research in the area of digital chemistry focuses on the expansion of automated capabilities as well as combining such capabilities into complete materials discovery workflows. 



We use computational methods and data-driven approaches for materials exploration and optimisation. We developed intelligent chemical space exploration methodologies using existing knowledge of the underlying problem at hand. We also designed efficient and robust representations for molecular and crystal property prediction using Machine Learning and specifically Graph Neural Networks.