The application of modern machine learning to challenges in atomistic simulation is gaining attraction.

Catalysts for aromatic C-O bond activation can potentially be used for the lignin degradation process. We investigated the mechanisms of C-O bond hydrogenolysis of diphenyl ether (PhOPh) by the nickel N-heterocyclic carbene (Ni-SIPr) complex to produce benzene and phenol as products. Our calculations revealed that diphenyl ether is not only a substrate, but also serves as a ligand to stabilize the Ni-SIPr complex. The Ni(SIPr)(η(6)-PhOPh) complex is initially formed before rearranging to Ni(SIPr)(η(2)-PhOPh), the active species for C-O bond activation. The catalytic reaction has three steps: (i) oxidative addition of Ni(SIPr)(η(2)-PhOPh) to form [Ni(SIPr)(OPh)(Ph)](0), (ii) σ-complex-assisted metathesis, in which H2 binds to the nickel to form [Ni(SIPr)(OPh)(Ph)(H2)](0), and then benzene (or phenol) is eliminated, and (iii) reductive elimination of phenol (or benzene) and the binding of PhOPh to regenerate Ni(SIPr)(η(2)-PhOPh). As the rate determining step is the oxidative addition step (+24 kcal mol(-1)), we also calculated the free energy barriers for the oxidative addition of diaryl ether containing a trifluoromethyl electron withdrawing group (PhOC6H4CF3) and found that C-O bond activation at the carbon adjacent to the aryl ring that contains the electron withdrawing substituent is preferred. This is in agreement with the experimental results, in that the major products are phenol and trifluoromethylbenzene. Moreover, the hydrogenation of benzene via Ni(SIPr)(η(2)-C6H6) requires a high energy barrier (+39 kcal mol(-1)); correspondingly, the hydrogenation products, e.g. cyclohexane and cyclohexadiene, were not observed in the experiment. Understanding the reaction mechanisms of the nickel catalysts for C-O bond hydrogenolysis of diphenyl ether will guide the development of catalytic systems for aromatic C-O bond activation to achieve the highest possible selectivity and efficiency.

Conspectus For the past two decades, linear free energy scaling relationships and volcano plots have seen frequent use as computational tools that aid in understanding and predicting the catalytic behavior of heterogeneous and electrocatalysts. Based on Sabatier’s principle, which states that a catalyst should bind a substrate neither too strongly nor too weakly, volcano plots provide an estimate of catalytic performance (e.g., overpotential, catalytic cycle thermodynamics/kinetics, etc.) through knowledge of a descriptor variable. By the use of linear free energy scaling relationships, the value of this descriptor is employed to estimate the relative energies of other catalytic cycle intermediates/transition states. Postprocessing of these relationships leads to a volcano curve that reveals the anticipated performance of each catalyst, with the best species appearing on or near the peak or plateau. While the origin of volcanoes is undoubtedly rooted in examining heterogeneously catalyzed reactions, only recently has this concept been transferred to the realm of homogeneous catalysis. This Account summarizes the work done by our group in implementing and refining “molecular volcano plots” for use in analyzing and predicting the behavior of homogeneous catalysts. We begin by taking the reader through the initial proof-of-principle study that transferred the model from heterogeneous to homogeneous catalysis by examining thermodynamic aspects of a Suzuki–Miyaura cross-coupling reaction. By establishing linear free energy scaling relationships and reproducing the volcano shape, we definitively showed that volcano plots are also valid for homogeneous systems. On the basis of this key finding, we further illustrate how unified pictures of C–C cross-coupling thermodynamics were created using three-dimensional molecular volcanoes. The second section highlights an important transformation from “thermodynamic” to “kinetic” volcanoes by using the descriptor variable to directly estimate transition state barriers. Taking this idea further, we demonstrate how volcanoes can be used to directly predict an experimental observable, the turnover frequency. Discussion is also provided on how different flavors of molecular volcanoes can be used to analyze aspects of homogeneous catalysis of interest to experimentalists, such as determining the product selectivity and probing the substrate scope. The third section focuses on incorporating machine learning approaches into molecular volcanoes and invoking big-data-type approaches in the analysis of catalytic behavior. Specifically, we illustrate how machine learning can be used to predict the value of the descriptor variable, which facilitates nearly instantaneous screening of thousands of catalysts. With the large amount of data created from the machine learning/volcano plot tandem, we show how the resulting database can be mined to garner an enhanced understanding of catalytic processes. Emphasis is also placed on the latest generation of augmented volcano plots, which differ fundame...

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 CO2 to formate.