Machine Learning-Driven Exploration of Composition- and Temperature- Dependent Transport and Thermodynamic Properties in LiF-NaF-KF Molten Salts for Nuclear Applications
GY Li and YH Lv and L Zhang and KW Jiang and L Zhang and YH Liu and XL Tan and T Bo, JOURNAL OF PHYSICAL CHEMISTRY B, 129, 9418-9429 (2025).
DOI: 10.1021/acs.jpcb.5c03444
This study developed a high-precision deep potential (DP) model based on
density functional theory (DFT) and the DP-GEN workflow to efficiently
simulate the microscopic structures and thermophysical properties of
LiF-NaF-KF molten salt systems with varying compositions. Through
iterative optimization of the training data set using the DP-GEN active
learning strategy, our DP model demonstrated excellent agreement with
DFT calculations in predicting energies, forces, and stresses.
Leveraging this model, we systematically investigated the local
structures and properties of 22 FLiNaK molten salt compositions,
including radial distribution functions (RDFs), coordination numbers
(CNs), density (rho), heat capacity (C p), self-diffusion coefficients
(SDCs), electrical conductivity, and shear viscosity. The analysis
revealed that Li-F ion pairs exhibit the strongest localized
coordination, with the coordination numbers of all cations increasing
with higher LiF content. Density was found to be primarily governed by
NaF concentration, showing a positive correlation with NaF content.
Viscosity was significantly influenced by both temperature and
composition, decreasing notably with increasing temperature - the
viscosity of the eutectic composition decreased from 3.933 mPa
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