  Understanding electrochemical interfaces at a microscopic level is essential
for elucidating important electrochemical processes in electrocatalysis,
batteries and corrosion. While \textit{ab initio} simulations have provided
valuable insights into model systems, the high computational cost limits their
use in tackling complex systems of relevance to practical applications. Machine
learning potentials offer a solution, but their application in electrochemistry
remains challenging due to the difficulty in treating the dielectric response
of electronic conductors and insulators simultaneously. In this work, we
propose a hybrid framework of machine learning potentials that is capable of
simulating metal/electrolyte interfaces by unifying the interfacial dielectric
response accounting for local electronic polarisation in electrolytes and
non-local charge transfer in metal electrodes. We validate our method by
reproducing the bell-shaped differential Helmholtz capacitance at the
Pt(111)/electrolyte interface. Furthermore, we apply the machine learning
potential to calculate the dielectric profile at the interface, providing new
insights into electronic polarisation effects. Our work lays the foundation for
atomistic modelling of complex, realistic electrochemical interfaces using
machine learning potential at \textit{ab initio} accuracy.
