Therefore molecular dynamics (MD) simulations have been exploited to calculate molten salt properties. Classical simulations are efficient for modelling systems over timescales on the order of nanoseconds in order to make viable predictions. Empirical force-field molecular dynamics (FFMD) simulations with classical potentials such as the Born-Mayer-Huggins-Tosi-Fumi (BMHTF) rigid-ion potential have been demonstrated to lack full predictive capabilities due to the lack of many-body interactions that influence local structure. Polarizable ion models (PIM) developed in conjunction with quantum mechanical calculations have led to significant improvements in modeling structure, thermodynamics, and dynamic properties. Ab initio molecular dynamics (AIMD) simulations have been shown to be capable of accurately capturing local structure and solute chemistry in comparison with experiments. AIMD is computationally expensive, however, and therefore it is difficult to access long time and large length scales to predict the dynamic and thermal properties. Recently, state-of-the-art artificial neural network-based interatomic potential molecular dynamics (NNIP-MD) has been demonstrated as a promising computational tool to explore the underlying physics of the high-dimensional molten salt compositions by simulating 10 4 atoms on the timescale of nanoseconds with an accuracy at the level of density functional theory (DFT). It has been demonstrated that the NNIP-MD studies are capable of accurately predicting molten salt struc-ture, heat capacity, self-diffusion coefficients, thermal conductivity, electrical conductivity, viscosity and the melting/freezing point in comparison with experimental measurements.