机器学习驱动的核应用 LiF-NaF-KF 盐的组成和温度依赖的传输和热力学特性
Guang-Ying Li1,2, Yu-Han Lv1,2, Li Zhang3
1College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China.
The journal of physical chemistry. B
|September 5, 2025
概括
这项研究创建了一个准确的深度潜力模型来模拟盐的特性. 该模型揭示了成分和温度如何影响FLiNaK盐的结构和热物理特性.
科学领域:
- 计算材料科学
- 溶盐的热物理特性
- 核反应堆材料
背景情况:
- 在核反应堆等先进的应用中,盐,特别是LiF-NaF-KF (FLiNAK) 系统至关重要.
- 精确模拟它们的微观结构和热物理特性对于材料设计和优化至关重要.
- 现有的模拟方法往往缺乏对复杂盐混合物的全面研究所需的效率和精度.
研究的目的:
- 开发一个高精度的深潜力 (DP) 模型来模拟FLiNAK盐系统.
- 系统地研究FLiNAK盐在各种成分和温度中的结构性质关系.
- 扩大非水性FLiNAK系统的属性数据库,并为材料开发提供理论指导.
主要方法:
- 使用密度函数理论 (DFT) 和DP-GEN工作流程来训练深度潜力模型.
- 采用积极学习策略来代优化培训数据集.
- 使用已验证的DP模型对22种FLiNAK组合进行大规模分子动力学模拟.
主要成果:
- 该DP模型准确地预测了能量,力和应力,显示出与DFT的良好一致性.
- 识别出具有最强局部协调的Li-F离子对,与LiF含量增加的阴离子协调数.
- 已经证明,NaF度主要决定密度,而粘度则严重依赖温度和KF含量,随着温度和KF度的提高而下降.
- 观察到高LiF含量的非乳化成分的热容量 (Cp) 与乳化系统相比显著更高.
结论:
- 开发的DP模型为模拟FLiNAK盐提供了高效和准确的工具.
- 阐明了组成 (LiF,NaF,KF含量) 和温度在调节FLiNAK系统的结构和热物理性质中的关键作用.
- 这些发现为优化特定应用的盐组成提供了坚实的理论基础,特别是在先进的反应器设计中.
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