Angelis Dimitrios, Georgakopoulos Chrysostomos, Sofos Filippos, Karakasidis Theodoros E
Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly, 35100 Lamia, Greece.
Int J Mol Sci. 2025 Jul 14;26(14):6748. doi: 10.3390/ijms26146748.
Machine Learning methods are exploited to extract a universal approach for self-diffusion coefficient calculation in molecular fluids. Analytical expressions are derived through symbolic regression for fluids both in bulk and confined nanochannels. The symbolic regression framework is trained on simulation data from molecular dynamics and correlates the values of the self-diffusion coefficients with macroscopic properties, such as density, temperature, and the width of confinement. New expressions are derived for nine different molecular fluids, while an all-fluid universal equation is extracted to capture molecular behavior as well. In such a way, a highly computationally demanding property is predicted by easy-to-define macroscopic parameters, bypassing traditional numerical methods based on mean squared displacement and autocorrelation functions at the atomistic level. To achieve generalizability and interpretability, simple symbolic expressions are selected from a pool of genetic programming-derived equations. The obtained expressions present physical consistency, and they are discussed in terms of explainability. The accurate prediction of the self-diffusion coefficient both in bulk and confined systems is important for advancing the fundamental understanding of fluid behavior and leading the design of nanoscale confinement devices containing real molecular fluids.
利用机器学习方法来提取一种用于计算分子流体自扩散系数的通用方法。通过符号回归推导出了适用于本体流体和受限纳米通道中流体的解析表达式。符号回归框架基于分子动力学模拟数据进行训练，并将自扩散系数的值与宏观性质（如密度、温度和受限宽度）相关联。针对九种不同的分子流体推导出了新的表达式，同时还提取了一个通用的全流体方程来捕捉分子行为。通过这种方式，借助易于定义的宏观参数预测了一个计算量很大的性质，绕过了基于原子水平的均方位移和自相关函数的传统数值方法。为了实现通用性和可解释性，从一组遗传编程衍生的方程中选择了简单的符号表达式。所得到的表达式具有物理一致性，并从可解释性方面进行了讨论。准确预测本体和受限系统中的自扩散系数对于推进对流体行为的基本理解以及指导包含真实分子流体的纳米级受限装置的设计具有重要意义。
