Construction of an Interpretable Machine Learning Model for Yield Prediction and Mechanistic Elucidation Enabled by Global Reaction Route Mapping

In the past decade, machine learning has emerged as a powerful tool to predict reaction outcomes. However, mechanistic interpretability of the constructed machine learning models remains limited due to the use of domain-specific and often arbitrary descriptors. Herein we demonstrate that an energy descriptor comprising the energies of the possible intermediates in the reaction system serves as a suitable descriptor for the construction of an interpretable machine learning model for mechanistic elucidation. The energy descriptor was calculated using the single-component artificial force induced reaction (SC-AFIR) method, which autonomously and comprehensively searches for intermediates of a target reaction, and subsequently used to train machine learning models for reaction yield prediction. Linear models with regularization showed good predictions for the hold-out samples (RMSE < 7% yield) and the coefficients of the models provided information on how the energies of the intermediates should be adjusted to enhance the reaction yield. This work highlights the utility of energy descriptors in constructing mechanistically interpretable machine learning models for predictive tasks in chemistry.