<br>机器学习驱动熔融 MgCl2-NaCl 和 MgCl2-KCl 盐深层潜力的开发,ACS Applied Materials & Interfaces

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机器学习驱动熔融 MgCl2-NaCl 和 MgCl2-KCl 盐深层潜力的开发
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2023-03-07 , DOI: 10.1021/acsami.2c19272
Tingrui Xu _{1,

2} , Xuejiao Li _{1,

2,

3} , Yang Wang _{1,

3} , Zhongfeng Tang _{1,

2,

3,

4}

Affiliation

由于高热稳定性和较低的成本，熔融MgCl ₂基氯化物已成为潜在的蓄热和传热材料。本工作采用第一原理、经典分子动力学和机器学习相结合的方法进行深势分子动力学（DPMD）模拟，系统研究了熔融MgCl ₂ –NaCl（MN）和MgCl ₂ –KCl (MK) 共晶盐在 800–1000 K 的温度范围内。这两种氯化物的密度、径向分布函数、配位数、潜在平均力、比热容、粘度和热导率在通过具有更大尺寸（5.2 nm）和更长时间尺度（5 ns）的 DPMD 来扩展温度。得出的结论是，熔融 MK 的较高比热容源自 Mg-Cl 键的强潜在平均力，而熔融 MN 由于弱相互作用而具有较大的热导率和较低的粘度，因此在传热方面表现更好Mg 和 Cl 离子之间。创新地，熔融 MN 和 MK 的微观结构和宏观性质的合理性和可靠性验证了这两种深度电势在温度下的可扩展性，这些 DPMD 结果还为其他配方 MN 和 MK 盐的模拟提供了详细的技术参数。

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Development of Deep Potentials of Molten MgCl2–NaCl and MgCl2–KCl Salts Driven by Machine Learning

Molten MgCl₂-based chlorides have emerged as potential thermal storage and heat transfer materials due to high thermal stabilities and lower costs. In this work, deep potential molecular dynamics (DPMD) simulations by a method combination of the first principle, classical molecular dynamics, and machine learning are performed to systemically study the relationships of structures and thermophysical properties of molten MgCl₂–NaCl (MN) and MgCl₂–KCl (MK) eutectic salts at the temperature range of 800–1000 K. The densities, radial distribution functions, coordination numbers, potential mean forces, specific heat capacities, viscosities, and thermal conductivities of these two chlorides are successfully reproduced under extended temperatures by DPMD with a larger size (5.2 nm) and longer timescale (5 ns). It is concluded that the higher specific heat capacity of molten MK is originated from the strong potential mean force of Mg–Cl bonds, whereas the molten MN performs better in heat transfer due to the larger thermal conductivity and lower viscosity, attributed to the weak interaction between Mg and Cl ions. Innovatively, the plausibility and reliability of microscopic structures and macroscopic properties for molten MN and MK verify the extensibilities of these two deep potentials in temperatures, and these DPMD results also provide detailed technical parameters to the simulations of other formulated MN and MK salts.

更新日期：2023-03-07

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