(234b) Uncovering Reaction Maps to Promote Active Catalysis

Complete knowledge of reaction mechanisms allows catalysts to be designed and operated at high activity, using minimal empiricism and guesswork. Learning the mechanism of catalysis, however, is a challenging task that is usually guided by substantial prior intuitions and analogies. To compound this difficulty, most mechanistic knowledge is centered on active catalytic cycles, and little information is available for side reactions which may hinder or even prohibit catalysis. In this presentation, computational methods will be introduced for exploring mechanisms in reactions catalyzed by transition metal complexes, with a special focus on off-cycle pathways and reactive steps which cripple the desired activity. A description of these new tools will be given alongside thoughts about future developments and incorporation of machine learning into the reaction prediction landscape. The focus applications of our tools will include C-C bond forming reactions catalyzed by group 10 metals, for instance the construction of advanced polymeric materials for solar cell applications.