posted on 2023-11-27, 19:10authored byNicholas
A. Szaro, Mubarak Bello, Charles H. Fricke, Olajide H. Bamidele, Andreas Heyden
The Random Phase Approximation (RPA) is conceptually
the most accurate
Density Functional Approximation method, able to simultaneously predict
both adsorbate and surface energies accurately; however, this work
questions its superiority over DFT for catalytic application on hydrocarbon
systems. This work uses microkinetic modeling to benchmark the accuracy
of DFT functionals against that of RPA for the ethane dehydrogenation
reaction on Pt(111). Eight different functionals, with and without
dispersion corrections, across the GGA, meta-GGA and hybrid classes
are evaluated: PBE, PBE-D3, RPBE, RPBE-D3, BEEF-vdW, SCAN, SCAN-rVV10,
and HSE06. We show that PBE and RPBE, without dispersion correction,
closely model RPA energies for adsorption, transition states, reaction,
and activation energies. Next, RPA fails to describe the gas phase
energy as unsaturation and chain-length increases in the hydrocarbon.
Finally, we show that RPBE has the best accuracy-to-cost ratio, and
RPA is likely not superior to RPBE or BEEF-vdW, which also gives a
measure of uncertainty.