Improving Molecule–Metal Surface Reaction Networks Using the Meta-Generalized Gradient Approximation: CO 2 Hydrogenation

The Journal of Physical Chemistry C, vol. 128, pp. 8611–8620

Abstract

High Resolution Image Download MS PowerPoint Slide Density functional theory is widely used to gain insights into 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 (DFs), 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-GGA (mGGA) DF in combination with a nonlocal correlation DF for the first time to study and gain new insights into a catalytically important surface reaction network, namely, CO 2 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 and molecules, as well as molecule–metal surface adsorption and activation energies. Hence, it is a better choice for constructing molecule–metal surface reaction networks.

Authors 7

1. Yuxiang Cai

   University of Liverpool · University of Antwerp

   Affiliation as printed

   Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, U.K

   Research Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium

2. Roel Michiels

   University of Antwerp

   Affiliation as printed

   Research Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium

3. Federica De Luca

   University of Messina · University of Antwerp · National Interuniversity Consortium of Materials Science and Technology

   Affiliation as printed

   Department of ChiBioFarAM (Industrial Chemistry), ERIC aisbl and INSTM/CASPE, University of Messina, V.le F. Stagno d’Alcontres 31, Messina 98166, Italy

   Research Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium

   Department of ChiBioFarAM (Industrial Chemistry), ERIC aisbl and INSTM/CASPE, University of Messina, V.le F. Stagno d'Alcontres 31, Messina 98166, Italy

4. Erik Cornelis Neyts

   University of Antwerp

   Affiliation as printed

   Research Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium

5. Xin Cheng Tu

   University of Liverpool

   Affiliation as printed

   Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, U.K

6. Annemie Bogaerts

   University of Antwerp

   Affiliation as printed

   Research Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium

7. Nick Gerrits corresponding Aachen Leiden Institute of Chemistry, Gorlaeus Laboratories

   Leiden University · University of Antwerp

   Affiliation as printed

   Leiden Institute of Chemistry, Gorlaeus Laboratories, Leiden University, P.O. Box 9502, Leiden 2300 RA, The Netherlands

   Research Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium