Bayesian Optimization of High-Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction*.

Active, selective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropy alloys (HEAs) offer a vast compositional space for tuning such properties. Too vast, however, to traverse without the proper tools.

What Atomic Positions Determines Reactivity of a Surface? Long-Range, Directional Ligand Effects in Metallic Alloys.

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.

Complex-Solid-Solution Electrocatalyst Discovery by Computational Prediction and High-Throughput Experimentation*.

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.