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. In this work we use a 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 GGA- vdW DFs, especially in describing gas-phase molecules and predicting barriers. Hence, it is a better choice for constructing molecule-metal surface reaction networks.