Distilling System Complexity to Enable Unbiased and Predictive Computational Reaction Investigations

Computational modelling is a powerful tool to study chemical reactions. Currently, human guidance is nearly always required to avoid the intractable complexity of all a priori possible reaction steps, which consequently greatly limits automated predictive applications. In this work, we present novel theoretical strategies based on the concept of atomic reactivity, as well as a "neophile" kinetic model, demonstrating how they enable unbiased automated reaction modelling with molecules of size typically encountered in experimental methodologies. Our framework allows the identification of unlikely or redundant reaction steps based on first principles and previous analyses, while the neophile kinetic model separates crucial reaction intermediates from inconsequential ones. These advances significantly improved modelling efficiency, allowing us to automatically model 17 unimolecular gold(I)-catalyzed reactions of increasing complexity, starting only from the reactant and catalyst. In 10 reactions, the experimental product distribution is closely reproduced, with 5 additional cases yielding essentially correct reaction networks. Our results demonstrate that it is possible to predictively model catalytic reactions without human guidance through an innovative reformulation of the problem.