High-entropy alloy catalysts: Fundamental aspects, promises towards electrochemical NH3 production, and lessons to learn from deep neural networks

A computational approach to judiciously predict high-entropy alloys (HEAs) as an efficient and sustainable material class for the electrochemical reduction of nitrogen is here presented. The employs density functional theory (DFT), adsorption energies N atoms N2 molecules descriptors catalytic activity deep neural networks. probabilistic quantifying HEA catalysts reaction (NRR) described, where catalyst elements concentration are optimized increase probability specific atomic arrangements on surfaces. provides key features effective filtering candidates without need time-consuming calculations. relationships between selectivity, which correlate with averaged valence electron electronegativity reference catalyst, analyzed in terms sufficient interaction sustained reactions and, at same time, release active site. As a result, complete list 3000 HEAs consisting quinary components Mo, Cr, Mn, Fe, Co, Ni, Cu, Zn reported together their metrics rank them from most likely least NRR gas diffusion electrodes, or case non-aqueous electrolytes utilized suppress competing hydrogen evolution reaction. Moreover, energetic landscape transformations computed compared Fe. study also analyses discusses how results would translate liquid-solid aqueous cells, further affected by changes properties upon hydroxylation, oxygen, hydrogen, water coverages.