While traditional linear free energy relationships such as those using Hammett parameters used linear models, the emergence of ML has led to the development of more complex algorithms, better suited for extracting hidden patterns in data. The ability of ML to efficiently capture complex relationships allows to extract influences on catalyst properties and thus makes it suited towards the accelerated design of chemicals and materials, including organocatalysts . Due to this potential, an increasing number of research groups have used ML to predict and develop new organocatalytic reactions.