氯化钠-氯化钾混合物的机器学习势：预测多组分盐的热物理性质和相行为
Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts
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中文总结 AI 辅助
研究利用密度泛函理论数据集训练矩张量势，用于预测多组分熔盐性质，能在宽温宽成分范围模拟，虽成功再现相关趋势，但绝对性质有偏差，支持混合建模框架提升准确性
中文摘要 AI 辅助
使用密度泛函理论（DFT）预测多组分熔盐的性质仍然具有挑战性，因为评估传输性质和相行为所需的空间和时间尺度在计算上是 prohibitive 的。在这项工作中，我们开发了一种矩张量势，使用 NaCl、KCl、NaCl-KCl 混合物和 NaK 合金的 DFT 数据集进行训练，能够在广泛的温度和成分范围内进行大规模分子动力学模拟。我们系统地评估了 D3 色散校正的效果，并将所得势应用于预测液体密度、扩散系数、径向分布函数、热容量、热导率以及 NaCl-KCl 相图。该模型成功地再现了许多与温度和成分相关的趋势。然而，几个绝对性质中仍然存在系统偏差，这突出了实验验证和校准的重要性。这些发现支持了一个混合建模框架，其中第一性原理指导的机器学习势提供了可转移的预测能力和机理洞察，而在模型开发或后续工程评估期间纳入实验数据对于提高定量准确性是必要的。
英文摘要
Predicting the properties of multicomponent molten salts using density functional theory (DFT) remains challenging because the spatial and temporal scales required to evaluate transport properties and phase behavior are computationally prohibitive. In this work, we develop a moment tensor potential trained using a a DFT dataset of NaCl, KCl, NaCl-KCl mixtures, and the NaK alloy, enabling large-scale molecular dynamics simulations across wide ranges of temperatures and compositions. We systematically evaluate the effect of D3 dispersion corrections and apply the resulting potential to predict liquid densities, diffusion coefficients, radial distribution functions, heat capacities, thermal conductivities, and the NaCl-KCl phase diagram. The model successfully reproduces many temperature- and composition-dependent trends. However, systematic deviations in several absolute properties persist, highlighting the importance of experimental validation and calibration. These findings support a hybrid modeling framework in which first-principles-informed machine-learning potentials provide transferable predictive capability and mechanistic insight, while experimental data incorporated during model development or subsequent engineering assessments is necessary to improve quantitative accuracy.
