We consider that the use of inner potentials rather than Fermi levels to describe electrode potentials is conceptually appealing because this can bridge the concepts and approaches used in DFT and classical force field -based simulations of electrochemical interfaces as discussed in Section 1. Because both CIP-DFT and classical simulations model the applied potential using only the electrode and solvent inner potentials, CIP presents a unified scheme for constant potential simulations. Furthermore, ϕ M rigorously contains all information needed to control the applied electrode potential and there is in principle no need to have access to the electronic structure or Fermi level; ϕ M can be extracted from charge distributions obtained with either classical or quantum mechanical simulations. We consider that these features make CIP a very promising approach to include the electrode potential description in modern machine learning potentials which can take long-range interactions and atomic charges into account. In the future, incorporating CIP in machine learning potentials may enable going beyond current QM/MM DFT/reactive force field methods and facilitate computational electrochemistry without explicit simulation of electrons.