Rodriguez Alejandro, Lam Stephen, Hu Ming
Department of Mechanical Engineering, University of South Carolina, Columbia, South Carolina 29208, United States.
Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts 01854, United States.
ACS Appl Mater Interfaces. 2021 Nov 24;13(46):55367-55379. doi: 10.1021/acsami.1c17942. Epub 2021 Nov 12.
Molten salts have attracted interest as potential heat carriers and/or fuel solvents in the development of new Gen IV nuclear reactor designs, high-temperature batteries, and thermal energy storage. In nuclear engineering, salts containing lithium fluoride-based compounds are of particular interest due to their ability to lower the melting points of mixtures and their compatibility with alloys. A machine learning potential (MLP) combined with a molecular dynamics study is performed on two popular molten salts, namely, LiF (50% Li) and FLiBe (66% LiF and 33% BeF), to predict the thermodynamic and transport properties, such as density, diffusion coefficients, thermal conductivity, electrical conductivity, and shear viscosity. Due to the large possibilities of atomic environments, we employ training using Deep Potential Smooth Edition (DPSE) neural networks to learn from large datasets of 141,278 structures with 70 atoms for LiF and 238,610 structures with 91 atoms for FLiBe molten salts. These networks are then deployed in fast molecular dynamics to predict the thermodynamic and transport properties that are only accessible at longer time scales and are otherwise difficult to calculate with classical potentials, molecular dynamics, or experiments. The prospect of this work is to provide guidance for future works to develop general MLPs for high-throughput thermophysical database generation for a wide spectrum of molten salts.
在新型第四代核反应堆设计、高温电池和热能存储的发展中，熔盐作为潜在的热载体和/或燃料溶剂引起了人们的关注。在核工程中，含氟化锂基化合物的盐因其能够降低混合物的熔点以及与合金的兼容性而备受关注。对两种常见的熔盐，即LiF（50% Li）和FLiBe（66% LiF和33% BeF）进行了结合分子动力学研究的机器学习势（MLP），以预测热力学和传输性质，如密度、扩散系数、热导率、电导率和剪切粘度。由于原子环境的可能性很大，我们采用深度势平滑版（DPSE）神经网络进行训练，从LiF的141278个含70个原子的结构和FLiBe熔盐的238610个含91个原子的结构的大型数据集中学习。然后将这些网络应用于快速分子动力学，以预测仅在较长时间尺度上可获得且用经典势、分子动力学或实验难以计算的热力学和传输性质。这项工作的前景是为未来开发用于生成广泛熔盐高通量热物理数据库的通用MLP的工作提供指导。
