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High-entropy alloys (HEAs) have the potential to be a paradigm-shift for rational catalyst discovery but this new type of alloy requires a completely new approach to predict the surface reactivity. In addition to the ligand effect perturbing the surface-adsorbate bond, the random configuration of elements in the surface will also induce local strain effects due to the varying radii of neighboring atoms. Accurate modelling of HEA surface reactivity requires an estimate of this effect: To what degree is the adsorption of intermediates on these lattice distorted atomic environments affected by local strain? In this study, more than 3,500 density functional theory (DFT) calculated adsorption energies of *OH and *O adsorbed on the HEAs IrPdPtRhRu and AgAuCuPdPt are statistically analyzed with respect to the lattice constants of the alloys and the surfaces of each individual binding site. It is found that the inherent distortion of the lattice structure in HEAs releases the local strain effect on the adsorption energy as the atomic environment surrounding the binding atom(s) settles into a relaxed structure. This is even observed to be true for clusters of atoms of which the sizes deviate significantly from the atomic environment in which they are embedded. This elucidates an important aspect of binding site interaction with the neighboring atoms and thus constitutes a step towards a more accurate theoretical model of estimating the reactivity of HEA surfaces.

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.

We present an approach for a probabilistic and unbiased discovery of selective and active catalysts for the carbon dioxide (CO2) and carbon monoxide (CO) reduction reactions on high-entropy alloys ...

Summary High-entropy alloys (HEAs) provide a near-continuous distribution of adsorption energies. With a minority of sites having optimal properties that dominate the catalysis, the overall catalytic activity can increase. In this article, we focus on the oxygen reduction reaction (ORR). We present density functional theory (DFT) calculated *OH and *O adsorption energies on a random subset of available binding sites on the surface of the HEA IrPdPtRhRu. Employing a simple machine learning algorithm, we predict remaining adsorption energies, finding good agreement between calculated and predicted values. With a full catalog of available adsorption energies, an appropriate expression for predicting catalytic activity was used to optimize the HEA composition. The HEA then becomes a design platform for the unbiased discovery of new alloys by promoting sites with exceptional catalytic activity. Setting different optimization constraints led to a new HEA composition and binary alloy IrPt, showing significant enhancements over pure Pt(111).

Electrocatalysis on high-entropy alloys (HEAs) is a very young field. There are only a few articles on the theoretical understanding of catalysis on these surfaces. Activity descriptors and a ‘theory of catalysis’ exist for uniform surfaces. In this article, we address scaling relations and Bronsted–Evans–Polanyi relations on HEAs which are fundamental for finding activity descriptors. We find that some relations hold, whereas others disappear on HEAs. This reveals some of the future challenges in modeling electrocatalysis on HEAs.