The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches

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@Article{karakasidis:2022:Fluids,

• author = "Theodoros E. Karakasidis and Filippos Sofos and Christos Tsonos",
• title = "The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches",
• journal = "Fluids",
• year = "2022",
• volume = "7",
• number = "10",
• pages = "Article No. 321",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2311-5521",
• URL = "https://www.mdpi.com/2311-5521/7/10/321",
• DOI = "doi:10.3390/fluids7100321",
• abstract = "In this paper, we incorporate experimental measurements from high-quality databases to construct a machine learning model that is capable of reproducing and predicting the properties of ionic liquids, such as electrical conductivity. Empirical relations traditionally determine the electrical conductivity with the temperature as the main component, and investigations only focus on specific ionic liquids every time. In addition to this, our proposed method takes into account environmental conditions, such as temperature and pressure, and supports generalisation by further considering the liquid atomic weight in the prediction procedure. The electrical conductivity parameter is extracted through both numerical machine learning methods and symbolic regression, which provides an analytical equation with the aid of genetic programming techniques. The suggested platform is capable of providing either a fast, numerical prediction mechanism or an analytical expression, both purely data-driven, that can be generalised and exploited in similar property prediction projects, overcoming expensive experimental procedures and computationally intensive molecular simulations.",
• notes = "also known as \cite{fluids7100321}",

}

Genetic Programming entries for Theodoros E Karakasidis Filippos Sofos Christos Tsonos

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