In homogeneous catalysis, the turnover frequency (TOF) and turnover number (TON) are the most commonly used quantities that experimentally describe catalytic activity. Computational studies, on the other hand, generally yield the ubiquitous free energy profile, which only provides the relative heights of different intermediates and transition states for a given reaction mechanism. This information, however, can be converted into a theoretical TOF through use of the energy span model. Clearly, directly computing turnover frequencies not only allows easy comparison of the activity of different catalysts, but also provides a means of directly comparing theory and experiment. Nonetheless, obtaining detailed free energy profiles for many catalysts is computationally costly. To overcome this and accelerate the rate at which prospective catalysts can be screened, here we use linear scaling relationships in tandem with the energy span model to create volcano plots that relate an easily and quickly computed energetic descriptor variable with a catalyst’s turnover frequency. As a demonstration of their ability, we use these “TOF volcanoes” to rapidly screen prospective transition metal/pincer-ligand catalysts based on activity in facilitating the hydrogenation of CO<sub>2</sub>to formate.
Data-powered augmented volcano plots for homogeneous catalysis
