Microkinetic Model Fitted with a Genetic Algorithm to Experimental XPS Coverages at High Pressure-CO Hydrogenation on Rh(111)

Journal article, 2024

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.