The accurate study of electrochemical interfaces calls for simulation techniques that can treat the electronic response of metal electrodes under electrostatic perturbations. Despite recent advancements in atomistic machine-learning (ML) methods applied to electronic-structure properties, predicting the non-local behavior of the charge density in electronic conductors remains a majoropen challenge. [more]