基于物理学的多忠实度高斯过程:模拟水和温度对深层欧特克溶剂粘度的影响
Maximilian Fleck1, Samir Darouich1, Jürgen Pleiss2
1Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany.
Journal of chemical information and modeling
|April 16, 2025
概括
本研究介绍了一种具有成本效益的方法,可以使用有限的实验和模拟数据来预测深层环氧溶剂混合物的粘度. 该方法通过减少对广泛测量的需求来增强过程设计.
科学领域:
- 物理化学 物理化学
- 化学工程是化学工程的重要组成部分.
- 材料科学 材料科学 材料科学
背景情况:
- 准确地预测水性深溶解剂 (DES) 混合物的剪切粘度对于工艺设计至关重要.
- 基于实验和模拟的粘度测量可能是昂贵和耗时的.
研究的目的:
- 开发一种具有成本效益和高效的方法,用于预测DES混合物的剪切粘度作为温度和成分的函数.
- 为了减少系统表征所需的实验和模拟的数量.
主要方法:
- 一种线性多真实性方法,结合了有限的实验和模拟粘度数据.
- 在机器学习模型训练之前,基于物理的粘度数据转换.
- 在Eyring绝对速率理论中利用多余的自由能量作为数据驱动组件.
- 基于内核的机器学习用于数据稀缺的场景.
主要成果:
- 通过使用最小的数据集,成功预测了剪切粘度对温度和组成的依赖性.
- 实验和模拟成本的明显降低.
- 对数据可用性有限的系统的方法的验证.
结论:
- 拟议的多忠实性方法为特征DES混合物粘度提供了简化和经济的解决方案.
- 这种方法对于数据有限的研究问题尤为有益,使得有效的系统分析成为可能.
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