Computationally-Guided and Synthetic Realisation of Novel Catalysts for Cross-Coupling Reactions

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In this project, property descriptors of new ligand designs will be calculated computationally. In parallel, descriptors for known ligands/catalysts will be combined with experimental screening data and will then be analysed using Principal Component Analysis (PCA), multivariate-regression and machine learning (ML), leading to mechanistic hypotheses and predictions about the properties of the catalysts. Promising catalysts will then be synthesised and tested in the lab, while the mechanism of reaction will also be studied computationally, with a view to developing additional descriptors to capture catalyst-substrate matching. A combination of design of experiments (DoE) and high throughput screening will be used to maximise the efficiency of this process. Experimental results will then be fed back into the initial models to improve the design of the ligands. This will be an iterative process, giving insight into important properties for catalyst design and catalyst-substrate matching.