Comparisons to experiments are important when developing kinetic models based on density functional theory (DFT) calculations. The comparisons are, however, often challenging due to the assumed uncertainties in the energies from which the kinetic parameters are calculated. Here, we introduce a genetic algorithm to adjust the DFT-energies to better match experimental XPS data, using CO hydrogenation on Rh(111) as an example. The adjustments are made to adsorption energies, adsorbate–adsorbate interactions, XPS energies, and peak shapes. While these parameters improve the experimental agreement considerably, the required changes to the DFT energies are relatively large, which indicates the need for refined treatments of, for example, possible surface species and reaction steps, surface inhomogeneities, or higher levels of electronic structure calculations. We propose the genetic-algorithm based method as a general tool for assessment of computational models.

We discuss a simple intuitive chemical interpretation of XPS shifts in terms of the Z+1 approximation and the chemical bonding in the final core-ionized state. We show how this applies to vibrational excitations and discuss the effects of metallic screening as well as when the Z+1 approximation can be expected to break down. Approaches to compute XPS shifts are discussed and exemplified with a focus on the case of CO/Ni(100). We discuss new experimental advances to study heterogeneous catalytic reactions - CO oxidation and CO hydrogenation - using XPS at high pressure (~1 bar) where reactants, intermediates, and products all contribute to the XPS signal. To disentangle the spectra, we perform microkinetic modeling of the reactions combined with a genetic algorithm to determine which initial data need to be re-examined due to DFT inaccuracy, adsorbate-adsorbate interaction, side reactions that are not included, or uncontrolled experimental features. Combined with computed XPS positions, the resulting coverages of various species can then be used to build theoretical XPS spectra to be directly compared with the experiment. Here, we describe the first steps towards this goal.

Glycerol is a byproduct of biodiesel production and, as such, it is of limited economic value. By means of electrooxidation, glycerol can be used as a feedstock for scalable hydrogen production, in addition to conversion to value-added products. The development of novel and efficient catalytic electrode materials for the anodic side of the reaction is a key towards a hydrogen-based energy economy. In the present study, a computational screening protocol combining DFT, scaling relations, and microkinetic modeling allows for a rational selection of novel catalysts that can deliver efficient glycerol electrooxidation, low cost of production, and environmental sustainability. Activity and chemical selectivity towards hydrogen production on pure metal catalysts is discussed in terms of volcano-shaped plots. We find that the selectivity in the glycerol oxidation reaction is influenced by a different energy landscape when in the presence of water and best classified by a comparison of O-H and C-H bond-breaking barriers. In addition, we screened 3570 bi-metallic catalysts in the AB (L1(0)) and A(3)B (L1(2)) ordered structures for activity, stability, price, and toxicity. By filtering based on the criteria for toxicity, resistance to oxidation, miscibility, and price, we have identified 5 L1(0) structured catalysts (AgPd, AuPd, PtSb, CuPt, and AgPt) and 20 L1(2) catalysts (Ga3Ta, In3Ta, Ir3W, Ir3Mo, Cu3Pt, Ir3Ta, Ir3Re, Pd3Bi, Pd3Cu, Pd3W, Pd3Co, Pd3Sn, Pd3Mo, Pd3Ag, Pd3Ga, Pd3Ta, Au3Ru, Pd3In, Au3Ir, and Pd3Au) that are all predicted to show high activity. We also identify an additional 37 L1(0) and 92 L1(2) structured electrocatalysts with an anticipated medium-high activity.