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关于在使用周期性扰动方法预测剪切粘度时选择加速幅度的问题
Lingfeng Gui1, Evangelos Tsochantaris1, Kieran Nehil-Puleo2
1School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, Scotland EH14 4AS, United Kingdom.
The Journal of chemical physics
|October 10, 2025
概括
这项研究引入了一种使用局部顺序理论和数据驱动模型的新方法,以准确预测非平衡分子动态的剪切粘度. 这种方法优化了工业流体的粘度计算,减少了计算工作量.
科学领域:
- 计算化学是一种计算化学.
- 材料科学 是一种材料科学.
- 化学工程是化学工程的组成部分.
背景情况:
- 剪切粘度对于工业过程至关重要,但实验数据在各种温度和压力下往往是有限的.
- 分子模拟为粘度预测提供了详细的方法,超越了经验相关性.
- 非平衡分子动力学周期性扰动方法 (PPM) 是有效的,但需要广泛的试验和错误,以获得准确的零扰动粘度.
研究的目的:
- 用分子模拟来提高粘度预测的准确性和效率.
- 为了克服周期性扰动方法 (PPM) 在确定零扰动粘度方面的局限性.
- 为了能够对各种有机溶剂进行可靠的粘度估计.
主要方法:
- 应用Quentrec的局部顺序理论来建模粘度对加速幅度的依赖性,以实现准确的推断.
- 利用数据驱动模型来估计最佳加速幅度,最大限度地减少统计噪声.
- 对144种有机溶剂的实验数据和平衡分子动力学模拟进行了验证.
主要成果:
- 昆特雷克的局部秩序理论准确地预测了零扰动粘度,减少了试错.
- 数据驱动模型有效地识别加速幅度,用于精确的粘度计算.
- 该方法与各种溶剂的实验和平衡模拟结果有很好的一致性.
结论:
- 开发的方法增强了PPM的实施,以准确有效地预测剪切粘度.
- 这种方法提供了有价值的分子见解,并为复杂系统的粘度估计提供了便利.
- 优化的PPM实施有助于工业过程设计和材料属性预测.
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