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Complex solid solutions (“high entropy alloys”), comprising five or more principal elements, promise a paradigm change in electrocatalysis due to the availability of millions of different active sites with unique arrangements of multiple elements directly neighbouring a binding site. Thus, strong electronic and geometric effects are induced, which are known as effective tools to tune activity. With the example of the oxygen reduction reaction, we show that by utilising a data‐driven discovery cycle, the multidimensionality challenge raised by this catalyst class can be mastered. Iteratively refined computational models predict activity trends around which continuous composition‐spread thin‐film libraries are synthesised. High‐throughput characterisation datasets are then used as input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag‐Ir‐Pd‐Pt‐Ru. The method can identify optimal complex‐solid‐solution materials for electrocatalytic reactions in an unprecedented manner., Complex solid solutions (“high‐entropy alloys”) promise a paradigm change in electrocatalysis but expose the challenge of almost unlimited options in adjusting their compositions. We propose the utilisation of computational models, combined with high‐throughput experimentation for the verification of the model assumptions, which allows for model refinement in iterative loops, understanding of binding mechanisms, and discovery of the most active composition.

Complex solid‐solution electrocatalysts (also referred to as high‐entropy alloy) are gaining increasing interest owing to their promising properties which were only recently discovered. With the capability of forming complex single‐phase solid solutions from five or more constituents, they offer unique capabilities of fine‐tuning adsorption energies. However, the elemental complexity within the crystal structure and its effect on electrocatalytic properties is poorly understood. We discuss how addition or replacement of elements affect the adsorption energy distribution pattern and how this impacts the shape and activity of catalytic response curves. We highlight the implications of these conceptual findings on improved screening of new catalyst configurations and illustrate this strategy based on the discovery and experimental evaluation of several highly active complex solid solution nanoparticle catalysts for the oxygen reduction reaction in alkaline media., Elementary: The effect of changing elements within high‐entropy electrocatalysts with respect to changes in adsorption energies and the correlated characteristic activity curves is analyzed. This concept is verified for the oxygen reduction reaction (ORR) in alkaline media. A targeted design of complex solid‐solution electrocatalysts is significantly enhanced by only screening equiatomic alloys without the need to scan the whole composition range.

Complex solid solution electrocatalysts (often called high-entropy alloys) present a new catalyst class with highly promising features due to the interplay of multi-element active sites. One hurdle is the limited knowledge about structure-activity correlations needed for targeted catalyst design. We prepared Cr-Mn-Fe-Co-Ni nanoparticles by magnetron sputtering a high entropy Cantor alloy target simultaneously into an ionic liquid library. The synthesized nanoparticles have a narrow size distribution but different sizes (from 1.3 ± 0.1 nm up to 2.6 ± 0.3 nm), different crystallinity (amorphous, face-centered cubic or body-centered cubic) and composition (i.e. high Mn versus low Mn content). The Cr-Mn-Fe-Co-Ni complex solid solution nanoparticles possess an unprecedented intrinsic electrocatalytic activity for the oxygen reduction reaction in alkaline media, some of them even surpassing that of Pt. The highest intrinsic activity was obtained for body-centered cubic nanoparticles with a low Mn and Fe content which were synthesized using the ionic liquid 1-etyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [Emimi][(Tf)2N].