Improving Molecule-Metal Surface Reaction Networks Using the Meta-Generalized Gradient Approximation: CO2 Hydrogenation

Density functional theory (DFT) is widely used to gain insight in molecule-metal surface reaction networks, which is important for a better understanding of catalysis. However, it is well known that generalized gradient approximation (GGA) density functionals (DF), most often used for the study of reaction networks, struggle to correctly describe both gas-phase molecules and metal surfaces. Also, GGA DFs typically underestimate reaction barriers due to an underestimation of the self-interaction energy. Screened hybrid GGA DFs have been shown to reduce this problem, but are currently intractable for wide usage. In this work we use a more affordable meta-generalized gradient approximation (mGGA) DF in combination with a non-local correlation DF for the first time to study a catalytically important surface reaction network, namely CO2 hydrogenation on Cu. We show that the mGGA DF used, namely rMS-RPBEl-rVV10, outperforms typical GGA DFs by providing similar or better predictions for metals, molecules, as well as molecule-metal surface adsorption and activation energies. Hence, it is a better choice for constructing molecule-metal surface reaction networks.