A theoretical method for finding active alloy electrocatalysts is proposed, and the method is applied to the electrochemical half-cell reaction of reducing oxygen to water, which is vital for improving the efficiency of, for example, hydrogen fuel cells. Our method predicts adsorption energies between reaction intermediates and the alloy surface to discover which sites on the surface are the most active. Starting from the multicomponent alloy IrPdPtRhRu, the alloy composition with best predicted catalytic activity is found.

We present an approach for a probabilistic and unbiased discovery of selective and active catalysts for the carbon dioxide (CO 2 ) and carbon monoxide (CO) reduction reactions on high-entropy alloys (HEAs). By combining density functional theory (DFT) with supervised machine learning, we predict the CO and hydrogen (H) adsorption energies of all surface sites on the (111) surfaces of the disordered CoCuGaNiZn and AgAuCuPdPt HEAs. This allows an optimization for the HEA compositions with increased likelihood for sites with weak hydrogen adsorption to suppress the formation of molecular hydrogen and with strong CO adsorption to favor the reduction of CO. As opposed to the construction of specific arrangements of surface atoms, our approach makes the desired surface sites more frequent through an increase in their probability. This leads to the unbiased discovery of several catalyst candidates for which selectivity toward highly reduced carbon compounds is expected and of which some have been verified in the literature.

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 datadriven 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.

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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.

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.

Using the high-entropy alloys (HEAs) CoCuGaNiZn and AgAuCuPdPt as starting points we provide a framework for tuning the composition of disordered multi-metallic alloys to control the selectivity and activity of the reduction of carbon dioxide (CO2) to highly reduced compounds. By combining density functional theory (DFT) with supervised machine learning we predicted the CO and hydrogen (H) adsorption energies of all surface sites on the (111) surface of the two HEAs. This allowed an optimization for the HEA compositions with increased likelihood for sites with weak hydrogen adsorption{to suppress the formation of molecular hydrogen (H2) and with strong CO adsorption to favor the reduction of CO. This led to the discovery of several disordered alloy catalyst candidates for which selectivity towards highly reduced carbon compounds is expected, as well as insights into the rational design of disordered alloy catalysts for the CO2 and CO reduction reaction.

Ligand and strain effects can tune the adsorption energy of key reaction intermediates on a catalyst surface to speed up rate‐limiting steps of the reaction. As novel fields like high‐entropy alloys emerge, understanding these effects on the atomic structure level is paramount: What atoms near the binding site determine the reactivity of the alloy surface? By statistical analysis of 2000 density functional theory calculations and subsequent host/guest calculations, it is shown that three atomic positions in the third layer of an fcc(111) metallic structure fourth‐nearest to the adsorption site display significantly increased influence on reactivity over any second or third nearest atomic positions. Subsequently observed in multiple facets and host metals, the effect cannot be explained simply through the d‐band model or a valence configuration model but rather by favorable directions of interaction determined by lattice geometry and the valence difference between host and guest elements. These results advance the general understanding of how the electronic interaction of different elements affect adsorbate–surface interactions and will contribute to design principles for rational catalyst discovery of better, more stable and energy efficient catalysts to be employed in energy conversion, fuel cell technologies, and industrial processes.