Molten salts represent an iconic use-case for machine learning interatomic potentials in which conventional modeling paradigms (e.g. empirical potentials, ab initio dynamics) cannot obtain the necessary balance of accuracy (high polarizability salts) and accessible spatiotemporal scales (∼ 10's nm, ns) necessary to estimate important physical properties. Molten salts have broad applications in concentrated solar power systems, liquid metal batteries, rare earth element (REE) production and molten salt reactors , but often molten salt mixtures are required to obtain targeted physical chemical properties. Specifically, to lower the working temperature of molten salts, salts are typically combined to create mixtures of eutectic composition with low melting points. The complexity of the molten salt mixtures in technological applications is further increased by the inclusion of multivalent ions and radioactive heavy elements. As modeling systems of this level of complexity requires the explicit inclusion of many-body effects such as polarization which require a fully quantum mechanical treatment, there are at present no chemically generalizable approaches for predicting the liquid phase structure of complex molten salt mixtures at the spatio-temporal scales relevant for industrial applications.