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Author Cai, Y.; Michiels, R.; De Luca, F.; Neyts, E.; Tu, X.; Bogaerts, A.; Gerrits, N. url  doi
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  Title Improving Molecule–Metal Surface Reaction Networks Using the Meta-Generalized Gradient Approximation: CO2Hydrogenation Type A1 Journal Article
  Year (down) 2024 Publication The Journal of Physical Chemistry C Abbreviated Journal J. Phys. Chem. C  
  Volume 128 Issue 21 Pages 8611-8620  
  Keywords A1 Journal Article; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;  
  Abstract 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 selfinteraction 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, 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 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.
 
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  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Wos Publication Date 2024-05-30  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1932-7447 ISBN Additional Links  
  Impact Factor 3.7 Times cited Open Access  
  Notes H2020 Marie Sklodowska-Curie Actions, 813393 ; Fonds Wetenschappelijk Onderzoek, 1114921N ; H2020 European Research Council, 810182 ; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, 019.202EN.012 ; Approved Most recent IF: 3.7; 2024 IF: 4.536  
  Call Number PLASMANT @ plasmant @ Serial 9248  
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