(38c) Benchmarking Modern Range Separated DFT Functionals and Ab Initio Wavefunction Theory-in-DFT Embedding for Computational Catalysis Applications

Computational quantum chemistry theories facilitate scientific discovery by enabling first-principles predictions of physical phenomena. Of course, the quality of these predictions hinges on the theory capturing the system's underlying physics. Conventional GGA- and (to a lesser extent) hybrid exchange-correlation (XC) functionals remain the workhorse approaches for elucidating catalytic properties thanks to their favorable balance of accuracy and computational cost. While commonly used XC functionals have been developed about two or more decades ago, recent developments in quantum chemistry have afforded new methods that may be of interest to the catalysis community but have not yet been widely adapted. We present a comparative benchmark study to show how different methods treat energy profiles of common reaction steps in catalysis (beta-hydride eliminations, reductive coupling, etc.)  This presentation shows the relative importance of different treatments of dispersion and self-interaction error found in XC functionals. We also illustrate the utility of Manby and Miller’s ab initio Wavefunction Theory-in-DFT Embedding calculation schemes for benchmarking DFT methods and future outlook for first-principles modeling of homogeneous and heterogeneous catalysis.