Transition Metal Computational Catalysis: Mechanistic Approaches and Development of Novel Performance Metrics

Computational catalysis is an ever-growing field, thanks in part to the incredible progression of computational power and the efficiency offered by our current methodologies. Additionally, the accuracy of computation and the emergence of new methods that can decompose energetics and sterics into quantitative descriptors has allowed for researchers to begin to identify important structure-function relationships that predict the properties of unexplored subspaces within the overall chemical space. Catalytic descriptors have been used frequently in data driven high-throughput computational screenings. With the use of machine learning, a large portion of the chemical space an be predicted in matter of minutes or hours, instead of months and years. Herein, a full story of quantitative descriptors and computational catalysis is presented, where we have focused on developed metrics for understanding the underlying nature of dative bonding in main-group complexes and extended this into transition metal complexes. Additionally, the complexities of various catalytic reactions (hydrogen atom abstraction, aziridination, epoxidation and ring-opening metathesis polymerization) have been studied in depth to highlight the key features that lead to increased and decreased catalytic efficiency.