Active,s elective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropya lloys (HEAs) offer av ast compositional space for tuning such properties.T oo vast, however,t ot raverse without the proper tools.H ere,w er eport the use of Bayesiano ptimization on am odel based on density functional theory (DFT) to predict the most active compositions for the electrochemical oxygen reduction reaction (ORR) with the least possible number of sampled compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discoveredo ptima are then scrutinized with DFT and subjected to experimental validation where optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru binary alloys.This study offers insight into the number of experiments needed for optimizing the vast compositional space of multimetallic alloys whichhas been determined to be on the order of 50 for ORR on these HEAs.

chemical complexity of materials used in challenging applications is usually high, as many elements (4-12) are needed to adjust properties to meet frequently contradicting demands. Traditional examples are steels, superalloys, or metallic glasses, while since several years, new types of chemically complex materials are emerging such as high entropy alloys (HEA) or compositionally complex solid solutions (CSS). [1] Whereas HEA can be multi-phase materials, CSS are single-phase materials. CSS were identified recently as a discovery platform for novel electrocatalysts. [2,3] However, the poly-elemental nature of these materials makes the identification of optimal compositions for specific properties a very challenging task. The choice of constituent elements and their relative chemical composition presents an immense search space for finding materials with enhanced properties such as high activity, selectivity, and stability for a given catalytic reaction. CSS-based electrocatalysts were already successfully applied to hydrogen [4--7] and oxygen evolution reactions, [6,[8][9][10] CO, [11,12] CO 2, [11,13] and oxygen reduction reactions, [2,3,10,12,[14][15][16] methanol, [7,17,18] and ethanol oxidation [19] as well as ammonia synthesis [20] and decomposition. [21,22] The special properties of CSS arise from their unique multi-element active High entropy alloys (HEA) comprise a huge search space for new electrocatalysts. Next to element combinations, the optimization of the chemical composition is essential for tuning HEA to specific catalytic processes. Simulations of electrocatalytic activity can guide experimental efforts. Yet, the currently available underlying model assumptions do not necessarily align with experimental evidence. To study deviations of theoretical models and experimental data requires statistically relevant datasets. Here, a combinatorial strategy for acquiring large experimental datasets of multi-dimensional composition spaces is presented. Ru-Rh-Pd-Ir-Pt is studied as an exemplary, highly relevant HEA system. Systematic comparison with computed electrochemical activity enables the study of deviations from theoretical model assumptions for compositionally complex solid solutions in the experiment. The results suggest that the experimentally obtained distribution of surface atoms deviates from the ideal distribution of atoms in the model. Leveraging both advanced simulation and large experimental data enables the estimation of electrocatalytic activity and solid-solution stability trends in the 5D composition space of the HEA system. A perspective on future directions for the development of active and stable HEA catalysts is outlined.

Active,s elective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropya lloys (HEAs) offer av ast compositional space for tuning such properties.T oo vast, however,t ot raverse without the proper tools.H ere,w er eport the use of Bayesiano ptimization on am odel based on density functional theory (DFT) to predict the most active compositions for the electrochemical oxygen reduction reaction (ORR) with the least possible number of sampled compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discoveredo ptima are then scrutinized with DFT and subjected to experimental validation where optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru binary alloys.This study offers insight into the number of experiments needed for optimizing the vast compositional space of multimetallic alloys whichhas been determined to be on the order of 50 for ORR on these HEAs.

No abstract

Complex solid solutions (“high entropy alloys” with a single solid‐solution phase) hold great promise in electrocatalysis because of their nearly unlimited number of different active sites exposed at the surface. It has been shown by theoretical studies that multiple arrangements of different elements directly neighboring a binding site create millions of differently active catalytic sites. We report a zooming‐in approach using scanning electrochemical cell microscopy (SECCM) to distinguish between the averaged electrochemical response of multiple active sites and active site‐specific electrochemical response. Using a thin film complex solid solution electrocatalyst and a range of SECCM single barrel capillaries with diameters from 1.2 µm to 50 nm, we observed an averaged electrochemical response for the oxygen reduction reaction with minor statistical variations for the larger capillary diameters. In contrast, significant statistical heterogeneity among the measured spots is observed for small capillary diameters. This statistical heterogeneity is attributed to the ability of the smaller probe size to address a comparatively smaller number of active sites with high or low activity dominating the measured electrocatalytic currents.