Funding is gratefully acknowledged from:
Our group is interested in the optimization, understanding and development of new sustainable catalytic systems in the transformative field of Digital Chemistry. The lab takes a hybrid approach - combining expertise in synthetic organic/organometallic chemistry and physical organic chemistry with computational-based techniques.
In silico design of new molecules, materials and catalysts as well as molecular featurization and understanding of reaction mechanism.
Data-driven, statistical and visualization techniques for extracting insights from chemical data and guiding experimental design.
Machine learning algorithms for predicting chemical properties, optimizing reaction conditions and studying reaction mechanisms.
Techniques for rapidly testing and analyzing large numbers of chemical reactions and data sets.
Automation and robotics for accelerating experimental and computational workflows.
Traditional and intuition-driven Chemistry and Chemical Engineering can be enhanced by Digital tools and methodologies. It enables deeper understanding of chemical systems/processes and reaction mechanisms. Reaction process/system optimization can be expedited in terms of acceleration and de-risking experiments - all enabling sustainability. Each project in the group will use at least one of the five pillars of Digital Chemistry above.
We are interested in elucidating key ligands effects which lead to productive catalysis in 1st row transition metal systems. Using a range of experimental measurements and computational techniques, we aim to understand the underlying principles governing these effects alongside mechanistic investigation in order to develop new catalytic systems.
Making a chemical process more sustainable is usually an afterthought in many optimization campaigns. Our research focuses on integrating sustainability considerations from the outset, using advanced computational and experimental techniques to optimize reactions for both efficiency and environmental impact.
We are interesting in developing closed-loop, self-optimizing reaction systems using flow chemistry techniques, but with the end user in mind, ensuring practical applicability and ease of use. The design of web-based interfaces and control systems is a key aspect of this research which directly enables non-expert users to operate the system for the development of new chemical processes.
We are interested in using data-driven techniques to design predictive digital compound libraries in the context of early-stage drug discovery. By leveraging machine learning and computational modeling, we aim to create libraries that can efficiently explore chemical space and identify promising candidates which can be generated using automated chemistry.