  We present a general procedure to introduce electronic polarization into
classical Molecular Dynamics (MD) force-fields using a Neural Network (NN)
model. We apply this framework to the simulation of a solid-liquid interface
where the polarization of the surface is essential to correctly capture the
main features of the system. By introducing a multi-input, multi-output NN and
treating the surface polarization as a discrete classification problem, for
which NNs are known to excel, we are able to obtain very good accuracy in terms
of quality of predictions. Through the definition of a custom loss function we
are able to impose a physically motivated constraint within the NN itself
making this model extremely versatile, especially in the modelling of different
surface charge states. The NN is validated considering the redistribution of
electronic charge density within a graphene based electrode in contact with
aqueous electrolyte solution, a system highly relevant to the development of
next generation low-cost supercapacitors. We compare the performances of our
NN/MD model against Quantum Mechanics/Molecular dynamics simulations where we
obtain a most satisfactorily agreement.
