机器学习为制冷剂准确力场的开发提供了支持.
Ning Wang1, Montana N Carlozo1, Eliseo Marin-Rimoldi1
1Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States.
Journal of chemical theory and computation
|June 12, 2023
概括
机器学习优化了碳水化合物 (HFC) 制冷剂的力场,改善了对回收和重新利用工作至关重要的热物理性质的预测.
科学领域:
- 热力学是一种热力学.
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 化碳 (HFC) 是广泛使用的制冷剂,取代了破坏臭氧层的物质.
- 一些HFC具有很高的全球变暖潜力,需要逐步淘汰和回收技术.
- 准确的热物理性质数据对于制定有效的HFC管理策略至关重要.
研究的目的:
- 改进基于机器学习的工作流程,以优化HFCs在经典力场中的Lennard-Jones参数.
- 为了提高分子模拟的预测准确度,以提高制冷剂的关键热物理性质.
- 为HFC-143a,HFC-134a,R-50,R-170和R-14开发精确的力场.
主要方法:
- 采用机器学习工作流程,集成分子动力学 (MD) 和吉布斯集体蒙特卡洛 (GEMC) 模拟.
- 利用支持向量机分类器和高斯过程替代模型来有效地选参数集.
- 基于液体密度和蒸汽-液体平衡 (VLE) 数据的Lennard-Jones参数代优化.
主要成果:
- 在模拟和实验热物理性质之间实现了优化HFC力场的优异一致.
- 报告了液体密度 (0.33.4%),蒸汽密度 (1.42.6%),蒸汽压力 (1.32.8%) 和蒸发度 (0.52.7%) 的低平均绝对百分比误差 (MAPEs).
- 开发的力场表现出优越或与现有文献模型相提并论的性能.
结论:
- 机器学习驱动的工作流有效地优化HFC力场,以准确预测热物理性质.
- 精确的力场对于推进制冷剂管理和开发中的分子模拟能力至关重要.
- 这种方法显著加速了优化参数的发现,节省了大量的计算资源.
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