Symbolic regression development of empirical equations for diffusion in Lennard-Jones fluids

被引：23

作者：

Alam, Todd M. ^{[1
,2
]}

Allers, Joshua P. ^{[2
]}

Leverant, Calen J. ^{[3
,4
]}

Harvey, Jacob A. ^{[5
]}

机构：

[1] ACC Consulting New Mexico, Cedar Crest, NM 87008 USA

[2] Sandia Natl Labs, Dept Organ Mat Sci, Albuquerque, NM 87185 USA

[3] Sandia Natl Labs, Dept WMD Threats & Aerosol Sci, Albuquerque, NM 87185 USA

[4] Univ Florida, Dept Chem Engn, Gainesville, FL 32611 USA

[5] Sandia Natl Labs, Geochem Dept, Albuquerque, NM 87185 USA

来源：

JOURNAL OF CHEMICAL PHYSICS | 2022年 / 157卷 / 01期

关键词：

MOLECULAR-DYNAMICS SIMULATIONS; SELF-DIFFUSION; HARD-SPHERE; TRANSPORT-COEFFICIENTS; SYSTEM-SIZE; REAL FLUIDS; VISCOSITY; MODEL; PREDICTION; MIXTURES;

D O I：

10.1063/5.0093658

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

Symbolic regression (SR) with a multi-gene genetic program has been used to elucidate new empirical equations describing diffusion in Lennard-Jones (LJ) fluids. Examples include equations to predict self-diffusion in pure LJ fluids and equations describing the finite-size correction for self-diffusion in binary LJ fluids. The performance of the SR-obtained equations was compared to that of both the existing empirical equations in the literature and to the results from artificial neural net (ANN) models recently reported. It is found that the SR equations have improved predictive performance in comparison to the existing empirical equations, even though employing a smaller number of adjustable parameters, but show an overall reduced performance in comparison to more extensive ANNs. Published under an exclusive license by AIP Publishing.

引用

页数：13

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