化 (AlCl3) 中的液体-蒸汽相平衡是通过机器学习实现的原子间潜力
Rajni Chahal1, Luke D Gibson2, Santanu Roy1
1Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.
The journal of physical chemistry. B
|January 13, 2025
概括
机器学习潜力可以准确预测融盐的特性,这对于清洁能源应用至关重要. 这种模拟方法通过了解各种操作条件的热物理数据来提高安全性.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 盐对于清洁能源技术至关重要,但对其热物理性质的实验性确定具有挑战性.
- 对蒸汽压力等属性的准确知识对于盐系统的安全运行至关重要.
- 分子模拟为预测这些属性的实验方法提供了一个可扩展的替代方案.
研究的目的:
- 开发和验证机器学习原子间潜力 (MLIP) 模拟化 (AlCl3) 盐.
- 预测热物理性质,包括温度-表面张力相关性,液体-蒸汽相位图和粘度.
- 评估不同MLIP架构和盐系统培训策略的性能.
主要方法:
- 开发了两个MLIP架构:基于内核的潜力和神经网络原子间潜力 (NNIP).
- 使用初始分子动力学 (AIMD) 数据培训MLIP,包括低密度配置和PBE-D3功能.
- 进行两相共存模拟,以确定相图和关键性质.
- 对盐结构的实验数据进行验证,包括拉曼光谱和中子结构因子.
主要成果:
- 该NNIP准确地预测了AlCl3的临界温度和密度,分别为实验值的3%和7%.
- 确定了温度-表面张力和温度-粘度的准确相关性.
- 经过PBE-D3功能训练的MLIP与实验盐结构 (Al2Cl6二次体) 和高密度-温度相关性有很好的一致性.
结论:
- 经过适当数据培训的MLIP,特别是NNIP,是预测盐特性的有效工具.
- 在训练中包括低密度配置对于准确地表示宽相空间行为至关重要.
- 这种模拟方法可以加速核反应堆化盐的选,并改善安全评估.
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