Computer-Aided Catalyst Design

The discovery of new catalysts drives chemistry forwards, yet this is still dependent on trial-and-error experimentation. Screening large number of molecules, additives and solvent systems is innefficient, costly and wasteful. We explore computational approaches to understand and explore structure, mechanism and selectivity in catalytic transformations. Increasingly, this is carried out predictively, rather than retrospectively, in the design and optimization of new chiral catalysts to achieve high levels of stereocontrol. Collaborations with leading research groups in catalysis and synthetic organic chemistry have been established to pursue these goals.

Key papers: Science 2018 360, 638; Nat. Commun. 2016, 7, 10109; Angew. Chem. Int. Ed. 2015, 127, 4981
Collaborations: Veronique Gouverneur (Oxford); Ed Anderson (Oxford); Darren Dixon (Oxford)

asymmetric catalysis noncovalent interactions informatics

Data-driven Chemistry

Like most scientists, chemists are drowning in data from laboratory experiments and from calculations. We are developing tools to automate the analysis of quantum-chemistry. Another area in need of automation is in the development of quantitative structure-property relationships, particularly where flexible molecules are concerned.

Key papers: Chem. Sci. 2018, 9, 2628; J. Am. Chem. Soc. 2017, 39, 1296;
Collaborations: Tom Rovis (Columbia); Steven Fletcher (Oxford)