A promising route to overcome the limitations of local ML models is to include known physical interactions explicitly. For example, a description of long-range electrostatics can be obtained by learning atom-centered charge distributions (e.g atomic charges, dipoles or partitioned electron densities). A prominent recent example of this is the MuML dipole model of Veit et al., which uses atomic charges and atom-centered dipoles to predict molecular dipole moments. This idea takes advantage of the fact that the charge distributions around atoms can be predicted with reasonable accuracy from local environments, even if their interactions are long-ranged. While this solves some of the issues of local interatomic potentials, there are also significant downsides: Firstly, charge conservation of the overall system is generally not ensured, and secondly non-local charge transfer (e.g through conjugated π-systems) is not captured. These issues can be fundamentally addressed by switching the target of the ML model: Instead of predicting the charge distribution directly, one can predict a charge dependent energy expression. The charge distribution is then obtained by minimizing this energy expression under the constraint that the charge is conserved. This idea is closely related to classical charge equilibration approaches like QEq. In this manner, charge conservation is rigorously ensured, the description of non-local charge transfer is enabled and a simple route to analytical derivatives is provided through a Hellmann-Feynman-like approach. The advantage of this approach, compared to directly predicting the charge density, can perhaps be understood in analogy to the choice of initial guess in density functional theory (DFT) calculations: While it is common practice to construct the initial guess from a superposition of atomic densities, it has been found that the superposition of atomic potentials yields a significantly improved starting point. So far only few examples of ML-based charge equilibration models have been reported, however. Most notably, Goedecker and co-workers applied a NN-based QEq model to ionic crystals. The corresponding models were trained to predict the energies and forces of reference DFT calculations, using the predicted partial charges merely as an intermediate quantity. More recently, Behler, Goedecker and co-workers combined this approach with local NN potentials for the description of organic molecules and MgO surfaces. Here, the charge equilibration models were trained on partial charges from reference DFT calculations.