Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods

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@Article{angelis:2023:Micromachines,

• author = "Dimitrios Angelis and Filippos Sofos and Konstantinos Papastamatiou and Theodoros E. Karakasidis",
• title = "Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods",
• journal = "Micromachines",
• year = "2023",
• volume = "14",
• number = "7",
• pages = "Article No. 1446",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2072-666X",
• URL = "https://www.mdpi.com/2072-666X/14/7/1446",
• DOI = "doi:10.3390/mi14071446",
• abstract = "In this paper, we propose an alternative road to calculate the transport coefficients of fluids and the slip length inside nano-conduits in a Poiseuille-like geometry. These are all computationally demanding properties that depend on dynamic, thermal, and geometrical characteristics of the implied fluid and the wall material. By introducing the genetic programming-based method of symbolic regression, we are able to derive interpretable data-based mathematical expressions based on previous molecular dynamics simulation data. Emphasis is placed on the physical interpretability of the symbolic expressions. The outcome is a set of mathematical equations, with reduced complexity and increased accuracy, that adhere to existing domain knowledge and can be exploited in fluid property interpolation and extrapolation, bypassing timely simulations when possible.",
• notes = "also known as \cite{mi14071446}",

}

Genetic Programming entries for Dimitrios Angelis Filippos Sofos Konstantinos Papastamatiou Theodoros E Karakasidis

Citations