三元运算
热力学
热导率
粘度
熔盐
原子间势
热膨胀
大气温度范围
工作(物理)
热扩散率
熔点
扩散
材料科学
化学
分子动力学
计算化学
计算机科学
物理
复合材料
程序设计语言
作者
Gechuanqi Pan,Jing Ding,Yunfei Du,Dong Hoon Lee,Yutong Lu
标识
DOI:10.1016/j.commatsci.2020.110055
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI