  Electrochemical energy storage always involves the capacitive process. The
prevailing electrode model used in the molecular simulation of polarizable
electrode-electrolyte systems is the Siepmann-Sprik model developed for perfect
metal electrodes. This model has been recently extended to study the
metallicity in the electrode by including the Thomas-Fermi screening length.
Nevertheless, a further extension to heterogeneous electrode models requires
introducing chemical specificity, which does not have any analytical recipes.
Here, we address this challenge by integrating the atomistic machine learning
code (PiNN) for generating the base charge and response kernel and the
classical molecular dynamics code (MetalWalls) dedicated to the modeling of
electrochemical systems, and this leads to the development of the PiNNwall
interface. Apart from the cases of chemically doped graphene and graphene oxide
electrodes as shown in this study, the PiNNwall interface also allows us to
probe polarized oxide surfaces in which both the proton charge and the
electronic charge can coexist. Therefore, this work opens the door for modeling
heterogeneous and complex electrode materials often found in energy storage
systems.
