Electrochemical reactions will underpin the decarbonization of the global economy by enabling the widespread use of fuel cells, sustainable production of hydrogen and ammonia, and reduction of CO 2 . Firstprinciples density functional theory (DFT) modeling of electrochemical reactions has proven invaluable in the discovery of new catalysts for electrochemical and photochemical energy conversion, as well as battery electrode materials. Improving the accuracy of first-principles electrochemical predictions requires better treatment of both electronic structure and solvation by the electrochemical environment. On the electronic structure side, semi-local DFT methods have notorious limitations in correctly predicting the localization of charge, band/HOMO-LUMO gaps, and alignment of frontier states due to self-interaction error (SIE). This shortcoming of DFT is particularly problematic in studies of (photo)electrochemical reactions because accurate prediction of frontier state alignment for (photo)electrocatalysts relative to those of reactants/products is fundamental in the discovery of new catalysts and reaction mechanisms. The incorporation of exact exchange in hybrid functionals partially corrects the SIE but can add empirical parameters that decrease the transferability of such approaches.