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Abstract:
Computational screening for new and improved catalyst materials relies on accurate and low-cost predictions of key parameters such as adsorption energies. Here, we use recently developed compressed sensing methods to identify descriptors whose predictive power extends
over a wide range of adsorbates, multimetallic transition metal surfaces, and facets. The descriptors are expressed as nonlinear functions of intrinsic properties of the clean catalyst surface, e.g. coordination numbers, d-band moments, and density of states at the Fermi level. From a single density functional theory calculation of these properties, we predict adsorption
energies at all potential surface sites, and thereby also the most stable geometry. Compared to previous approaches such as scaling relations, we find our approach to be both more general and more accurate for the prediction of adsorption energies on alloys with mixed-metal surfaces, already when based on training data including only pure metals. This accuracy can be systematically improved by also adding alloy adsorption energies to the training data.
