Functional materials by design
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).
Digital chemistry
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.
Computational and data-driven materials design
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.
Computationally-guided crystalline materials discovery
Our innovative approaches in the synthesis and characterisation of porous organic cages and framework materials have significantly advanced the field of materials science. Through collaborations with Professor Graeme Day (University of Southampton) and Professor Kim Jelfs (Imperial College London), we use computational predictions to guide the experimental synthesis of new materials, thereby streamlining the discovery process. These interdisciplinary efforts underscore the synergistic potential of combining experimental and computational expertise in materials discovery.
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