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LiF-NaF-ZrF4 multicomponent molten salts are promising candidate coolants foradvanced clean energy systems owing to their desirable thermophysical andtransport properties. However, the complex structures enabling theseproperties, and their dependence on composition, is scarcely quantified due tolimitations in simulating and interpreting experimental spectra of highlydisordered, intermediate-ranged structures. Specifically, size-limitedab-initio simulation and accuracy-limited classical models used in the past,are unable to capture a wide range of fluctuating motifs found in the extendedheterogeneous structures of liquid salt. This greatly inhibits our ability todesign tailored compositions and materials. Here, accurate, efficient andtransferable machine learning potentials are used to predict structures farbeyond the first coordination shell in LiF-NaF-ZrF4. Neural networks trained atonly eutectic compositions with 29% and 37% ZrF4 are shown to accuratelysimulate a wide range of compositions (11 to 40% ZrF4) with dramaticallydifferent coordination chemistries, while showing a remarkable agreement withtheoretical and experimental Raman spectra. The theoretical Raman calculationsfurther uncovered the previously unseen shift and flattening of bending band at~250 cm-1 which validated the simulated extended-range structures as observedin compositions with higher than 29% ZrF4 content. In such cases, machinelearning-based simulations capable of accessing larger time- and length-scales(beyond 17 {\AA}) were critical for accurately predicting both structure andionic diffusivities.

Transferable Deep Learning Potential Reveals Intermediate-Range Ordering Effects in LiF-NaF-ZrF4 Molten Salt