Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning

Abstract

Significant progress has been made in recent years in the use of AI and Machine Learning (ML) for catalyst discovery and optimisation. The effectiveness of ML and data science techniques was demonstrated in predicting and optimising enantioselectivity and regioselectivity in catalytic reactions through optimisation of the ligands, counterions and reaction conditions. Direct discovery of new catalysts/reactions is more difficult and requires efficient exploration of transition metal chemical space. A range of computational techniques for descriptor generation, ranging from molecular mechanics to DFT methods, have been successfully demonstrated, often in conjunction with ML to reduce computational cost associated with TS calculations. Complex aspects of catalytic reactions, such as solvent, temperature, etc., have also been successfully incorporated into the ML optimisation and discovery workflow.