  A crucial aspect in the simulation of electrochemical interfaces consists in
treating the distribution of electronic charge of electrode materials that are
put in contact with an electrolyte solution. Recently, it has been shown how a
machine-learning method that specifically targets the electronic charge
density, also known as SALTED, can be used to predict the long-range response
of metal electrodes in model electrochemical cells. In this work, we provide a
full integration of SALTED with MetalWalls, a program for performing classical
simulations of electrochemical systems. We do so by deriving a spherical
harmonics extension of the Ewald summation method, which allows us to
efficiently compute the electric field originated by the predicted electrode
charge distribution. We show how to use this method to drive the molecular
dynamics of an aqueous electrolyte solution under the quantum electric field of
a gold electrode, which is matched to the accuracy of density-functional
theory. Notably, we find that the resulting atomic forces present a small error
of the order of 1 meV/{\AA}, demonstrating the great effectiveness of adopting
an electron-density path in predicting the electrostatics of the system. Upon
running the data-driven dynamics over about 3 ns, we observe qualitative
differences in the interfacial distribution of the electrolyte with respect to
the results of a classical simulation. By greatly accelerating
quantum-mechanics/molecular-mechanics approaches applied to electrochemical
systems, our method opens the door to nanoseconds timescales in the accurate
atomistic description of the electrical double layer.
