Date of Award


Document Type


Degree Name

Master of Science (MS)


College of Science and Mathematics


Chemistry and Biochemistry

Thesis Sponsor/Dissertation Chair/Project Chair

Henk Eshuis

Committee Member

Marc Kasner

Committee Member

Mark Whitener


This study evaluates the performance of a computational method known as the Random Phase Approximation (RPA) for a Nickel catalyst that activates the C-CN bond of benzonitrile. The reaction mechanism contains two distinct processes. The first process describes the cleavage of the C-CN bond of benzonitrile. The latter describes the fluxionality of benzonitrile about the Nickel atom. Many popular computational methods would struggle to properly describe either or both processes, because they may not perform consistently for transition-metal complexes, are computationally expensive, or have a strong dependence on orbital energy gaps. The RPA provides results with an excellent balance of accuracy with a scaling factor small enough to study many chemically relevant systems.

When compared to density functional theory (DFT), the RPA performs slightly better than the dispersion corrected functionals when describing the C-CN activation of benzonitrile, and slightly worse than the dispersion corrected functionals when describing the fluxionality of the mechanism. However, it provides more consistent and reliable results than Mpller-Plesset Perturbation Theory and Coupled-Cluster Theory with Single and Double excitations. Overall, the RPA performs on par or better than the majority of the DFT functionals and certainly performs better than the more expensive MP2. Although RPA is not error free and does not come within chemical accuracy of 1 kcal mol-1, the RPA results are in excellent agreement with both accurate theoretical results and experimental data. The RPA is a step forward toward a systematic, parameter free, all-round method to describe transition-metal chemistry that offers an excellent alternative to the more expensive electron correlated methods, and less consistent DFT functionals.

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