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.