  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, multi-metallic transition metal surfaces and facets. The
descriptors are expressed as non-linear 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 adding also
alloy adsorption energies to the training data.
