Abstract ZnCl2-NaCl-KCl ternary salts are promising thermal storage and heat transfer fluid materials with a freezing point below 250 °C, thermal stability up to 800 °C, and other favorable properties that fit the use in the next generation concentrated solar thermal power. This work for the first time developed a machine learning-based interatomic potential for ZnCl2-NaCl-KCl ternary salt (0.6:0.2:0.2 in mole fraction) on the basis of energies and forces estimated by ab initio molecular dynamics calculations. The proposed machine learning potential was validated with the obtained partial radial distribution functions and the coordination numbers with the AIMD. The structural and thermophysical evolutions with temperature over the entire operating temperature range were documented. Adding Na+ and K+ ions deteriorated the network by corner-sharing and edge-sharing ZnCl4 tetrahedra, and apparently affected self-diffusion coefficient, thermal conductivity, and viscosity of the melt. The calculated thermophysical properties agreed with experimental data. A negative temperature dependence of thermal conductivity was noted and discussed. Based on the experimental data, viscosity data by Li et al. and those of this work, yielded reliable experimental values in the Vogel-Tamman-Fulcher form.

中文翻译：

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熔融 ZnCl2 及其混合物的 DFT 精确机器学习描述：2. 用于 CSP 的 ZnCl2-NaCl-KCl 三元盐的潜力开发和性能预测

摘要 ZnCl2-NaCl-KCl三元盐是一种很有前途的蓄热传热流体材料，其凝固点低于250°C，热稳定性高达800°C，具有适合下一代聚光光热发电的优良性能。 . 这项工作首次基于从头分子动力学计算估计的能量和力，开发了基于机器学习的 ZnCl2-NaCl-KCl 三元盐（摩尔分数为 0.6:0.2:0.2）的原子间势。所提出的机器学习潜力通过获得的部分径向分布函数和 AIMD 的协调数进行了验证。记录了在整个工作温度范围内随温度的结构和热物理演变。添加 Na+ 和 K+ 离子会破坏共角和共边 ZnCl4 四面体的网络，并明显影响熔体的自扩散系数、热导率和粘度。计算出的热物理性质与实验数据一致。注意到并讨论了热导率的负温度依赖性。基于实验数据，Li 等人的粘度数据。和这项工作的那些，以 Vogel-Tamman-Fulcher 形式产生了可靠的实验值。Li等人的粘度数据。和这项工作的那些，以 Vogel-Tamman-Fulcher 形式产生了可靠的实验值。Li等人的粘度数据。和这项工作的那些，以 Vogel-Tamman-Fulcher 形式产生了可靠的实验值。