Yadavalli, S; Jones, G; Stamatakis, M; (2021) DFT Benchmark Studies on Representative Species and Poisons of Methane Steam Reforming on Ni(111). Physical Chemistry Chemical Physics 10.1039/d1cp00862e. (In press).

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Abstract

Ni catalysts used in Methane Steam Reforming (MSR) are highly susceptible to poisoning by carbon-based species, which poses a major impediment to the productivity of industrial operations. These graphitic carbon-like formations are typically modelled from first principles as graphene. Although numerous experimental investigations have been carried out, a conclusive mechanistic molecular-level understanding of graphene formation on Ni during the MSR reaction is still elusive. First principles-based approaches, such as Density Functional Theory (DFT) calculations, can provide valuable insight into the mechanism of graphene growth in the MSR reaction. It is, however, critical that a DFT model of this reaction can accurately describe the interactions of Ni(111) with the MSR intermediates as well as graphene. Crucially, these interactions include van der Waals forces, making the choice of a proper DFT functional a subject of debate, as there are several dispersion-inclusive functionals available in the literature. In this work, a systematic benchmark study has been carried out to identify a suitable DFT functional for the graphene and MSR system. The binding energies of graphene and important MSR species were computed on Ni(111) using GGA functionals, DFT-D3 and van der Waals density functionals (vdW-DF). Comparisons of these binding energies with published experimental data reveal that the GGA functionals are inadequate for the graphene-Ni(111) system. Among the vdW-DF, optB88-vdW predicts the binding energy of graphene with high accuracy; however, its predictions significantly deviate from the experimental binding energies of CO and O. Among DFT-D3 functionals, PBE-D3 was found to have a reasonable predictive accuracy for most MSR species (excluding the CO adsorbate). Overall, no single DFT functional could estimate the binding energies of all the species with equally high accuracy. Our benchmarks guide the selection of DFT functionals for simulations of MSR and could aid in the future development of predictive quality functionals.

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