文献类型：期刊文献

英文题名：Molecular dynamics simulations of CaCl2-NaCl molten salt based on the machine learning potentials

作者：Xie, Yun[1,2];Bu, Min[1,2];Zou, Guiming[3];Zhang, Ye[1,2,4];Lu, Guimin[1,2,4]

机构：[1]East China Univ Sci & Technol, Joint Int Lab Potassium & Lithium Strateg Resource, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Natl Engn Res Ctr Integrated Utilizat Salt Lake Re, Shanghai 200237, Peoples R China;[3]Fengxin Ganfeng Lithium CO Ltd, Yichun, Peoples R China;[4]Joint Int Lab Potassium & Lithium Strateg Resource, Shanghai, Peoples R China

年份：2023

卷号：254

外文期刊名：SOLAR ENERGY MATERIALS AND SOLAR CELLS

收录：;EI(收录号：20231213744858);WOS:【SCI-EXPANDED(收录号:WOS:000954384400001)】；

基金：The authors acknowledge the financial support from the National Natural Science Foundation of China (Grant U20A20147) .

语种：英文

外文关键词：CaCl2-NaCl molten salt; Machine learning potential molecular; dynamics simulations; Local structure; Properties

摘要：CaCl2-NaCl molten salt is a promising material for energy transport and storage. The microstructure evolution and properties of CaCl2-NaCl molten salt are significantly important for improving energy conversion efficiency. In this study, machine learning potentials were applied to explore the effect of composition and temperature on the structure and properties of CaCl2-NaCl molten salt. It showed great merits, such as considerable economic cost, high efficiency and accuracy, and a safe working environment. The structural information of CaCl2-NaCl molten salt was analyzed systematically, including partial radial distribution function, coordination number distribution, and angular distribution function. The distorted octahedral structure of Na-Cl and Ca-Cl ion pairs was observed in CaCl2-NaCl molten salt and the degree of distortion varied with the components. Besides, the influence of composition and temperature on properties were investigated, including density, ion self-diffusion coefficients, shear viscosity, electrical conductivity, thermal expansion coefficient, and specific heat capacity. In summary, the machine learning potential molecular dynamics simulations have a blazing application prospect in theoretical research of molten salt systems and can provide great fundamentals for practical application.