Grand Challenges in Computational Catalysis

Catalysis is a cornerstone of modern societies as over 90% of processes in the chemical industry are facilitated by catalysts, with the majority requiring a homogeneous or heterogeneous catalyst (Hagen, 2015). Future renewable energy scenarios also rely heavily on the utilization of electro-catalysts, e.g., for the production of clean hydrogen. This shift towards new feedstocks and benign processes entails the development of new generations of catalysts. While the discovery of catalysts has often relied on trial and error in the first half of the last century, the establishment of (design) rules has significantly improved the speed with which new catalysts are being discovered. To this end, the knowledge-based improvement and design of new catalysts is increasingly supported by quantum chemical calculations of reaction mechanisms and kinetic modeling of corresponding reaction rates. In fact, first examples of catalyst design by means of computational screening have already emerged (Nørskov et al., 2009; Medford et al., 2015; Zhao et al., 2019). The extent to which computational modeling becomes a dominant factor in the catalysis community in the 21st century depends crucially on the accuracy with which predictions can be made, but also on the development of a reductionist approach, where the main contributing factors to the performance of a given class of catalysts are reduced to a few selected key parameters that can be used efficiently for computational screening. Perhaps the most challenging issue in computational catalysis is the fact that the rate constant of a reaction step changes drastically with minor changes of the reaction barrier (e.g., for a reaction occurring at 500 K by a factor of about 120 for typical errors of ±20 kJ/mol, or a factor of approximately 3 for an error ±5 kJ/mol, that is commonly referred to as chemical accuracy) and that only approximate methods are computationally feasible for the calculation of enthalpy and entropy contributions of a transition states free energy as the catalytic systems are often large and complex. In all fields discussed here (homogeneous, heterogeneous and electro-catalysis) density functional theory (DFT) has become the workhorse of computational studies as it exhibits the best compromise between accuracy and computational cost. In homogeneous catalysis for example, the enthalpy related to the active site of a transition-metal complex can be determined quite accurately with advanced hybrid functionals (Jiang et al., 2012). However, homogeneous catalysts often exhibit large ligands raising the issue of conformational complexity that is difficult to model. This is often getting even more problematic with solvation and leads to difficulties in determining the active conformational space and corresponding enthalpy and particularly entropy contributions to the free energy (Harvey et al., 2019). A balanced description of interand intramolecular interactions during solvation is similarly challenging (Schmidt et al., 2013). In large parts of heterogeneous catalysis (e.g., supported transition metals) one needs to approximate the active site with a simple extended periodic surface model, that usually omits the effect of particle size and shape as well as particle-support interactions. Moreover, functionals using the generalized gradient approximation (GGA) are typically the only applicable choice, with the best functionals having errors of ±20 kJ/mol for adsorption energies (Wellendorff et al., 2015) Edited and reviewed by: Frank Hollmann, Delft University of Technology, Netherlands