Data Driven Discovery of New Catalysts for Asymmetric Synthesis

This project aims to discover new chiral transition-metal catalysts for the selective functionalisation of unreactive carbon-hydrogen bonds within organic molecules. A combination of statistical modelling techniques and asymmetric synthesis tactics will be harnessed to generate a data rich feedback loop that establishes an innovative platform for predictable catalyst design. Ultimately, this research is expected to deliver new catalytic processes that deliver cost savings and reduced environmental impact for the synthesis of valuable chiral amines that will find application in the development of new agrochemicals, life-saving medicines, and functional materials including photonics.