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The unsatisfactory electrocatalysis of the oxygen evolution reaction (OER) is a major hurdle for the sustainable production of hydrogen using water electrolyzers. Besides, most state-of-the-art catalysts are based on expensive...

Electrocatalytic reduction of oxidized nitrogen compounds (NO x ) promises to help rebalance the nitrogen cycle. It is widely accepted that nitrate reduction to NH4 +/NH3 involves NO as an intermediate, and NO hydrogenation is the potential-limiting step of NO reduction. Whether *NO hydrogenates to *NHO or *NOH is still a matter of debate, which makes it difficult to optimize catalysts for NO x electroreduction. Here, “catalytic matrices” are used to swiftly extract features of active transition metal catalysts for NO electroreduction. The matrices show that active catalysts statistically stabilize *NHO over *NOH and have undercoordinated sites. Besides, square-symmetry active sites with Cu and other elements may prove active for NO electroreduction. Finally, multivariate regressions are able to reproduce the main features found by the matrices, which opens the door for more sophisticated machine-learning studies. In sum, catalytic matrices may ease the analysis of complex electrocatalytic reactions on multifaceted materials.

Incorporating solvent−adsorbate interactions is paramount in models of aqueous (electro)catalytic reactions. Although a number of techniques exist, they are either highly demanding in computational terms or inaccurate. Microsolvation offers a trade-off between accuracy and computational expenses. Here, we dissect a method to swiftly outline the first solvation shell of species adsorbed on transition-metal surfaces and assess their corresponding solvation energy. Interestingly, dispersion corrections are generally not needed in the model, but caution is to be exercised when water− water and water−adsorbate interactions are of similar magnitude.