CAS: Developing Methods for Reaction Modeling to Tune Catalysts for Substrate Variability

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With this award, the Chemical Catalysis Program of the NSF Division of Chemistry is supporting the research of Professor Aaron Odom of the Department of Chemistry at Michigan State University to develop a model to predict catalyst efficiency. The catalysts that Professor Odom and his research group are studying are complexes of titanium and vanadium, which are early transition metals that are earth abundant and offer unique chemical products. This more sustainable reactivity model will minimize the time and effort required for the optimization of catalytic systems that are commonly used in industry. While similar studies have been performed for late transition metal catalysts that are used for different types of applications, this model has been designed for early transition metal catalysts. These studies are advancing an initial model previously developed by Professor Odom to be able to function with a larger number of reaction variables and different types of catalyst-substrate interactions. The program is also discovering new ways to adapt the model to catalysts that are attached to solid supports. A diverse group of graduate and undergraduate students are being trained through their participation in these activities and a chemical demonstration team is being organized to engage local K-12 students in STEM disciplines.Ligand parametrization methods have recently been established as valuable tools for catalyst optimization with late transition metal systems. Although early transition metal catalysts are used in a variety of industrial processes for the conversion of petrochemical products to value-added commodities, catalytic performance predictive methods have been slower to evolve in this area. The Odom laboratory has developed the first model to systematize the factors that control the reactivity of titanium-catalysts used for hydroamination. These researchers are now advancing their working model to be able to handle new challenges such as non-symmetrical ligand systems and multicomponent reactions. In addition, the method is being designed to accommodate silica-supported titanium catalysts and vanadium olefin isomerization catalysts that require consideration of new types of catalyst-substrate interactions. These studies are providing training for graduate and undergraduate students in the field of inorganic/organometallic chemistry and chemical experimentation as well as in machine learning with applications to artificial intelligence in molecular design.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.