The design of a catalyst is commonly performed through human rational design, which requires detailed understanding of the key catalytic steps . In line with the early developments of statistical modelling through Hammett parameters to correlate substrate properties to kinetic properties of the reaction (section 3), advanced machine-learning tools can help to unravel key mechanistic features which can be used (Figure ) . Based on their findings that π-π interactions between the catalyst's triazole substituent and the substrate is key for stereoinduction, they designed new catalyst structures maximising the predicted selectivity. The predictions were experimentally validated confirming that their model can be used to guide the design of highly selective catalysts (Figure ).