More and Faster: Simultaneously Improving Reaction Coverage and Computational Cost in Automated Reaction Prediction Tasks

Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation. Although substantial progress has been made in predicting specific reaction pathways, the computational cost and inconsistent reaction coverage of automated reaction prediction are still obstacles to exploring deep reaction networks. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by modifications of the reaction enumeration, geometry initialization, and transition state convergence algorithms. These changes are implemented in the context of Yet Another Reaction Program (YARP), our reaction prediction package. Compared with prevailing methods, YARP near perfectly reproduces established reaction pathways and products, without the use of domain knowledge. In addition, YARP also discovers new kinetically relevant reaction pathways. This is achieved while simultaneously reducing the cost of reaction characterization nearly 100-fold and increasing transition state intended rates over 10-fold. This combination of ultra-low cost and high reaction-coverage creates opportunities to explore the reactivity of larger systems and more complex reaction networks.