In this thesis, a proof of concept was established for the use of a novel coupled QM-MD approach to modelling metallic (copper) electrode-electrolyte interfaces. SCC-DFTB calculations of the instantaneous electronic structure of a copper electrode were coupled to a classical MD simulation of an electrode-electrolyte interface. The applied QM-MD method was described rigorously, and used to investigate the compound distribution and dynamics at the interface, relative to a fully classical MD simulation. Polarisation effects were observed to bring about a significant increase in the attraction between cations and the cathode. Moreover, local polarisation of the cathode was found to immobilise adsorbed cations, and induce an increased orientational preference of the nearby water dipoles. The secondary goal of this thesis was to explore to what extent neural networks are able to replicate SCC-DFTB calculations of the electronic charge density on a metallic electrode. Using a computer vision approach, qualitative evidence was obtained indicating that neural networks can be used to replicate SCC-DFTB predictions on periodic metallic surfaces.