Distortion/interaction analysis via machine learning

Machine learning (ML) models have provided a highly efficient pathway to quantum mechanical accurate reaction barrier predictions. Previous approaches have, however, stopped at prediction of these barriers instead of developing predictive capabilities in reactivity analysis tasks such as distortion/interaction-activation strain analysis. Such methods can provide insight into reactivity trends and ultimately guide rational reaction design. In this work we present the novel application of ML to the rapid and accurate prediction of distortion and interaction DFT energies across four datasets (three existing and one new dataset). We also show how our models can accurately predict on unseen, high impact literature examples where DFT-level distortion/interaction analysis has previously been used to explain reactivity trends for cycloadditions. This work thus provides support for ML to be utilised further in reactivity analysis across different reaction classes at a fraction of the cost of traditional methods such as DFT.