Organic reaction mechanism classification using machine learning

A mechanistic understanding of catalytic organic reactions is crucial for the design of new catalysts, modes of reactivity, and the development of greener and more sustainable chemical processes1-13. Kinetic analysis lies at the core of mechanistic elucidation by allowing direct testing of mechanistic hypotheses from experimental data. Traditionally, kinetic analysis has relied on the use of initial rates14, logarithmic plots and, more recently, visual kinetic methods15-18, in combination with mathematical rate law derivations. However, the derivation of rate laws and their interpretation requires numerous mathematical approximations, and as a result they are prone to human error and are limited to reaction networks with only a few steps operating under steady state. Here we show that a deep neural network model can be trained to analyze ordinary kinetic data and automatically elucidate its corresponding mechanism class, without any additional user input. The model identifies a wide variety of classes of mechanisms with outstanding accuracy, including mechanisms out of steady-state, such as those involving catalyst activation and deactivation steps, and performs excellently even when the kinetic data contains substantial error or only a few time points. Our results demonstrate that artificial intelligence-guided mechanism classification is a powerful new tool that can streamline and automate mechanistic elucidation. We are making this model freely available to the community and we anticipate this work will lead to further advances in the development of fully automated organic reaction discovery and development.