Liang Wenshuo, Lu Guimin, Yu Jianguo
School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China.
National Engineering Research Center for Integrated Utilization of Salt Lake Resource, East China University of Science and Technology, Shanghai 200237, China.
ACS Appl Mater Interfaces. 2021 Jan 27;13(3):4034-4042. doi: 10.1021/acsami.0c20665. Epub 2021 Jan 12.
Theoretical studies on the MgCl-KCl eutectic heavily rely on ab initio calculations based on density functional theory (DFT). However, neither large-scale nor long-time calculations are feasible in the framework of the ab initio method, which makes it challenging to accurately predict some properties. To address this issue, a scheme based on ab initio calculation, deep neural networks, and machine learning is introduced. By training on high-quality data sets generated by ab initio calculations, a deep potential (DP) is constructed to describe the interaction between atoms. This work shows that the DP enables higher efficiency and similar accuracy relative to DFT. By performing molecular dynamics simulations with DP, the microstructure and thermophysical properties of the MgCl-KCl eutectic (32:68 mol %) are investigated. The structural evolution with temperature is analyzed through partial radial distribution functions, coordination numbers, angular distribution functions, and structural factors. Meanwhile, the estimated thermophysical properties are discussed, including density, thermal expansion coefficient, shear viscosity, self-diffusion coefficient, and specific heat capacity. It reveals that the Mg ions in this system have a distorted tetrahedral geometry rather than an octahedral one (with vacancies). The microstructure of the MgCl-KCl eutectic shows the feature of medium-range order, and this feature will be enhanced at a higher temperature. All predicted thermophysical properties are in good agreement with the experimental results. The hydrodynamic radius determined from the shear viscosity and self-diffusion coefficient shows that the Mg ions have a strong local structure and diffuse as if with an intact coordination shell. Overall, this work provides a thorough understanding of the microstructure and enriches the data of the thermophysical properties of the MgCl-KCl eutectic.
关于MgCl-KCl共晶的理论研究严重依赖基于密度泛函理论（DFT）的从头算计算。然而，在从头算方法的框架内，大规模和长时间的计算都是不可行的，这使得准确预测某些性质具有挑战性。为了解决这个问题，引入了一种基于从头算计算、深度神经网络和机器学习的方案。通过对从头算计算生成的高质量数据集进行训练，构建了一个深度势（DP）来描述原子间的相互作用。这项工作表明，相对于DFT，DP具有更高的效率和相似的精度。通过使用DP进行分子动力学模拟，研究了MgCl-KCl共晶（32:68摩尔%）的微观结构和热物理性质。通过部分径向分布函数、配位数、角分布函数和结构因子分析了随温度的结构演变。同时，讨论了估计的热物理性质，包括密度、热膨胀系数、剪切粘度、自扩散系数和比热容。结果表明，该体系中的Mg离子具有扭曲的四面体几何结构，而不是八面体结构（有空位）。MgCl-KCl共晶的微观结构显示出中程有序的特征，并且在较高温度下该特征会增强。所有预测的热物理性质与实验结果都很好地吻合。由剪切粘度和自扩散系数确定的流体动力学半径表明，Mg离子具有很强的局部结构，并且扩散时好像带有完整的配位壳。总的来说，这项工作对微观结构提供了全面的理解，并丰富了MgCl-KCl共晶热物理性质的数据。
