DFT boosts machine-learning models of nucleophilic aromatic substitution reactions

Researchers in the UK and Sweden have combined density functional theory (DFT) and machine learning to develop a method that accurately predicts the kinetics of nucleophilic aromatic substitution reactions. The new method makes accurate predictions for a reaction class that is challenging to approximate with DFT, but doesn’t require the very large data sets that are typical of pure machine learning methods.