Deep Learning Potential Assisted Prediction of Local Structure and Thermophysical Properties of the SrCl₂-KCl-MgCl₂ Melt.

IF 5.7 1区化学 Q2 CHEMISTRY, PHYSICAL Journal of Chemical Theory and Computation Pub Date : 2024-09-10 Epub Date: 2024-08-28 DOI:10.1021/acs.jctc.4c00824

Jia Zhao, Taixi Feng, Guimin Lu

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Abstract

The local structure and thermophysical properties of SrCl₂-KCl-MgCl₂ melt were revealed by deep potential molecular dynamicsdriven by machine learning to facilitate the development of molten salt electrolytic Mg-Sr alloys. The short- and intermediate-range order of the SrCl₂-KCl-MgCl₂ melts was explored through radial distribution functions and structure factors, respectively, and their component and temperature dependence were discussed comprehensively. In the MgCl₂-rich system, the intermediate-range order is more pronounced, and its evolution with temperature exhibits a non-Debye-Waller behavior. Mg-Cl is dominated by 4,5 coordination and Sr-Cl by 6,7 coordination, and their coordination geometries exhibit distorted octahedra and distorted pentagonal bipyramids, respectively. A database of thermophysical properties of SrCl₂-KCl-MgCl₂ melts, including density, self-diffusion coefficient, viscosity, and ionic conductivity, was thus developed, covering the temperature range from 873 to 1173 K.

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深度学习势能辅助预测 SrCl2-KCl-MgCl2 熔体的局部结构和热物理性质。

通过机器学习驱动的深位势分子动力学揭示了SrCl2-KCl-MgCl2熔体的局部结构和热物理性质，促进了熔盐电解Mg-Sr合金的发展。通过径向分布函数和结构因子分别探讨了SrCl2-KCl-MgCl2熔体的短程和中程有序性，并全面讨论了它们的组分和温度依赖性。在富氯化镁体系中，中程阶更为明显，且随温度的变化呈现出非德比-瓦勒行为。Mg-Cl 以 4,5 配位为主，Sr-Cl 以 6,7 配位为主，它们的配位几何分别表现为扭曲的八面体和扭曲的五角二棱锥。由此建立了一个 SrCl2-KCl-MgCl2 熔体的热物理性质数据库，其中包括密度、自扩散系数、粘度和离子电导率，涵盖了 873 至 1173 K 的温度范围。

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来源期刊

Journal of Chemical Theory and Computation 化学-物理：原子、分子和化学物理

CiteScore

9.90

自引率

16.40%

发文量

568

审稿时长

1 months

期刊介绍： The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.