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In order to estimate the reactivity of a large number of potentially complexheterogeneous catalysts while searching for novel and more efficient materials,physical as well as data-centric models have been developed for a fasterevaluation of adsorption energies compared to first-principles calculations.However, global models designed to describe as many materials as possible mightoverlook the very few compounds that have the appropriate adsorption propertiesto be suitable for a given catalytic process. Here, the subgroup-discovery(SGD) local artificial-intelligence approach is used to identify the keydescriptive parameters and constrains on their values, the so-called SG rules,which particularly describe transition-metal surfaces with outstandingadsorption properties for the oxygen reduction and evolution reactions. Westart from a data set of 95 oxygen adsorption energy values evaluated bydensity-functional-theory calculations for several monometallic surfaces alongwith 16 atomic, bulk and surface properties as candidate descriptiveparameters. From this data set, SGD identifies constraints on the most relevantparameters describing materials and adsorption sites that (i) result in Oadsorption energies within the Sabatier-optimal range required for the oxygenreduction reaction and (ii) present the largest deviations from the linearscaling relations between O and OH adsorption energies, which limit theperformance in the oxygen evolution reaction. The SG rules not only reflect thelocal underlying physicochemical phenomena that result in the desiredadsorption properties but also guide the challenging design of alloy catalysts.

Identifying outstanding transition-metal-alloy heterogeneous catalysts for the oxygen reduction and evolution reactions via subgroup discovery