The training databases have been summarized in Table . As noted in subsection II A, there is a variation in the input compositions used for different molten salt mixtures. Consequently, the systems with larger input composition spaces exhibit a larger number of active learned training samples. The BO-optimized hyperparameters used to fit the final GAP models on these training databases are listed in Table ). The atomic forces validated for independent test samples drawn at each composition are visualized in Figure . The GAP models provide excellent force prediction accuracy with respect to DFT-SCAN across different molten salt mixture chemistries, with root mean squared errors (RMSE) ranging from 0.12 to 0.17 eV/Å. The larger distribution of atomic force in the LiCl-KCl system can be attributed to smaller chemical species having relatively higher diffusivity. The final GAP models are used to perform MD simulations with a system size over ∼1000 atoms. Detailed information on the simulated systems, their number of atoms, and densities estimated from GAP-MD for each composition and temperature are summarized in Appendix B Table . We performed GAP MD using the LAMMPS software package compiled with the QUIP pair style . Each simulation condition is initially thermalized at 1500K in the (NVT) ensemble , followed by volume relaxation in an isothermal-isobaric (NPT) ensemble with a pressure coupling of 1 bar perature is decreased to the target temperature over 200 ps in the NPT ensemble. At the target temperature the NPT MD is continued for ∼2 ns with a time-step of 0.5 fs and the last 1 ns is used for computing the structure. A representative set of simulated molten salt chemistries are shown in Figure . The results of these simulations are presented in the next two subsections.