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应用机器学习算法来预测Mie流体的运输特性

Justinas Šlepavičius¹, Alessandro Patti^1,2, James L McDonagh³

¹Department of Chemical Engineering, School of Engineering, The University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom.

The Journal of chemical physics

|July 12, 2023

概括

机器学习模型准确地预测流体运输特性,如球形粒子的自我扩散和粘度. 人工神经网络和k-最近邻居在将潜在参数与这些属性相关联方面表现最好.

科学领域:

分子建模和流体动力学.
计算化学和统计力学的应用.
对物理属性的预测模型的开发.

背景情况:

预测流体运输特性 (自扩散,粘度) 对复杂系统来说至关重要,但具有挑战性.
现有的理论方法有限,而经验相关性往往缺乏准确性.
机器学习 (ML) 提供了一种有希望的方法来提高预测能力.

研究的目的:

研究ML算法的应用,用于预测流体的运输特性.
开发米电位参数和流体运输特性之间的准确相关性.
评估不同ML模型 (KNN,ANN,SR) 对此任务的性能.

主要方法:

在流体相图中生成了54 Mie电位的自我扩散系数和剪切粘度的数据集.
采用了k-最近邻居 (KNN),人工神经网络 (ANN) 和符号回归 (SR) ML算法.
与不同密度和温度的运输特性相关的潜在参数.

主要成果:

在预测运输特性方面,ANN和KNN模型表现出可比且高准确度.
与ANN和KNN相比,符号回归 (SR) 显示了较大的偏差.
成功地应用了ML模型来预测,甲和二氧化碳的自我扩散系数.

结论:

ML,特别是ANN和KNN,提供了基于粒子间潜力的精确方法来预测流体运输特性.
开发的模型可以使用状态方程的参数扩展到真实分子系统.
这项工作推进了分子建模流体行为的预测能力.

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