  LiF-NaF-ZrF4 multicomponent molten salts are promising candidate coolants for
advanced clean energy systems owing to their desirable thermophysical and
transport properties. However, the complex structures enabling these
properties, and their dependence on composition, is scarcely quantified due to
limitations in simulating and interpreting experimental spectra of highly
disordered, intermediate-ranged structures. Specifically, size-limited
ab-initio simulation and accuracy-limited classical models used in the past,
are unable to capture a wide range of fluctuating motifs found in the extended
heterogeneous structures of liquid salt. This greatly inhibits our ability to
design tailored compositions and materials. Here, accurate, efficient and
transferable machine learning potentials are used to predict structures far
beyond the first coordination shell in LiF-NaF-ZrF4. Neural networks trained at
only eutectic compositions with 29% and 37% ZrF4 are shown to accurately
simulate a wide range of compositions (11 to 40% ZrF4) with dramatically
different coordination chemistries, while showing a remarkable agreement with
theoretical and experimental Raman spectra. The theoretical Raman calculations
further uncovered the previously unseen shift and flattening of bending band at
~250 cm-1 which validated the simulated extended-range structures as observed
in compositions with higher than 29% ZrF4 content. In such cases, machine
learning-based simulations capable of accessing larger time- and length-scales
(beyond 17 {\AA}) were critical for accurately predicting both structure and
ionic diffusivities.
