Angelis Dimitrios, Sofos Filippos, Karakasidis Theodoros E
Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly, Lamia 35100, Greece.
Phys Rev E. 2024 Jan;109(1-2):015105. doi: 10.1103/PhysRevE.109.015105.
The viscosity and thermal conductivity coefficients of the Lennard-Jones fluid are extracted through symbolic regression (SR) techniques from data derived from simulations at the atomic scale. This data-oriented approach provides closed form relations that achieve fine accuracy when compared to well-established theoretical, empirical, or approximate equations, fully transparent, with small complexity and high interpretability. The novelty is further outlined by suggesting analytical expressions for estimating fluid transport properties across the whole phase space, from a dilute gas to a dense liquid, by considering only two macroscopic properties (density and temperature). In such expressions, the underlying physical mechanisms are reflected, while, at the same time, it can be a computationally efficient alternative to costly in time and size first principle and/or molecular dynamics simulations.
通过符号回归（SR）技术，从原子尺度模拟得到的数据中提取了 Lennard-Jones 流体的粘度和热导率系数。这种面向数据的方法提供了封闭形式的关系，与成熟的理论、经验或近似方程相比，具有高精度、完全透明、复杂度低和可解释性强的特点。通过仅考虑两个宏观性质（密度和温度），提出了用于估计从稀薄气体到稠密液体整个相空间中流体传输性质的解析表达式，进一步突出了其新颖性。在这些表达式中，反映了潜在的物理机制，同时，它可以作为一种计算效率高的替代方法，以替代耗时且规模大的第一原理和/或分子动力学模拟。
