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机器学习驱动的分子动力学研究LiF-SmF₃盐

Fei Liu¹, Kailei Sun¹, Xu Wang¹

¹School of Metallurgical Engineering, Jiangxi University of Science and Technology, Ganzhou, China.

Journal of computational chemistry

|October 14, 2025

概括

一个新的机器学习模型准确地预测了LiF-SmF3盐系统的特性,这对于通过电解生产Sm-Fe合金至关重要. 这种方法为昂贵的实验测量提供了有效的替代方案.

科学领域:

材料科学材料科学材料科学
计算化学计算化学
电化学电化学电化学

背景情况:

化-三化 (LiF-SmF3) 系统对于通过盐电解来生产Sm-Fe合金至关重要.
了解它们的特性和局部结构对于流程优化至关重要.
高温化物化的实验测量是耗时且昂贵的.

研究的目的:

为LiF-SmF3系统开发一个准确的机器学习潜力模型.
为了高效地获得这些盐系统的特性和结构特征.
为了优化Sm-Fe合金的电解制备过程.

主要方法:

为LiF-SmF3系统开发机器学习潜力模型.
对密度函数计算的模型准确性的验证.
该模型在分子动力学模拟 (1100-1350 K) 中的应用.

主要成果:

机器学习模型实现了高精度 (96.92%) 的低能量和力偏差.
计算的密度和粘度从实验数据中显示出最小的偏差 (1.03%-2.77%和3.36%-4.58%).
成功计算了离子自我扩散系数和结构特征 (半径分布函数).

结论:

开发的机器学习潜力模型为研究LiF-SmF3系统提供了高效和准确的方法.
这种计算方法显著减少了对广泛实验测量的需求.
这些发现有助于通过更好地了解融盐介质来优化Sm-Fe合金生产.

关键词:

这是一种Sm-Fe合金.化物融盐的化物融盐.机器学习是机器学习.辐射分布的功能是辐射分布的功能.自己扩散系数的自我扩散系数.

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