RxnNet: An AI Framework for Reaction Mechanism Discovery - A Case Study of Carbocations

Understanding complex chemical reaction cascades remains a major challenge in chemistry. Scalable investigation of their thermodynamic and kinetic properties requires the use of automated reaction prediction tools, which is a rapidly growing area in the study of chemical reactivity. However, the systematic exploration of intricate reaction networks involving highly reactive intermediates continues to pose significant difficulties. Here, we introduce RxnNet, a novel artificial intelligence-assisted platform for the automated prediction of chemical reaction mechanisms. RxnNet integrates heuristic rules with domain-specific chemical knowledge including stereochemistry, regiochemistry, conformational preferences, and isotope labeling, to construct mechanistically informed reaction networks. These networks are represented as graphs and are coupled with on-the-fly quantum chemical evaluations to identify all feasible intermediates and transition states. In this work, we apply RxnNet to carbocation chemistry, a notoriously complex and computationally demanding type of reaction. We demonstrate the method’s capabilities by analyzing three multistep reactions with known mechanisms, each of which poses significant challenges even for expert computational and synthetic chemists. RxnNet provides a robust approach for uncovering reaction mechanisms, which can accelerate the understanding and design of transformations in complex chemical systems.