(645c) Applying Automated Reaction Prediction to Heterogeneous Catalysis Problems

Automated reaction prediction has the potential to elucidate complex reaction networks for many important applications. Although substantial progress has been made in predicting specific reaction pathways and resolving mechanisms, the computational cost and inconsistent reaction coverage of automated reaction prediction are still obstacles to exploring deep reaction networks. In a recent study, we have shown that cost can be reduced and reaction coverage can be increased simultaneously by relatively straightforward modifications of the reaction enumeration, geometry initialization, and transition state convergence algorithms that are common to many emerging prediction methodologies. These changes are implemented in the context of Yet Another Reaction Program (YARP), a fully automated reaction prediction method. In this talk, we will describe how YARP has been extended to study heterogeneous Gallium catalyzed oligomerization, which represents a new application area for automatic reaction prediction methods. Without any specific heuristic guidance, YARP generates a deep reaction network and reproduces two types of reactions proposed in a previous study (i.e. olefin insertion and β-hydride elimination). In addition, YARP also discovers another reaction class that has relatively higher activation energies but are still kinetically accessible (α-hydride elimination), which helps to explain the formation of alkanes in experimental results. Meanwhile, YARP decreases the computational cost compared with conventional approaches which makes such deep reaction network construction possible. These results establish the viability of applying YARP to heterogeneous catalysis problems, in which domain heuristics driven approaches fail to fully describe the reaction mechanisms.