The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches
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- @Article{karakasidis:2022:Fluids,
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author = "Theodoros E. Karakasidis and Filippos Sofos and
Christos Tsonos",
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title = "The Electrical Conductivity of Ionic Liquids:
Numerical and Analytical Machine Learning Approaches",
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journal = "Fluids",
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year = "2022",
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volume = "7",
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number = "10",
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pages = "Article No. 321",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2311-5521",
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URL = "https://www.mdpi.com/2311-5521/7/10/321",
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DOI = "doi:10.3390/fluids7100321",
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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.",
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notes = "also known as \cite{fluids7100321}",
- }
Genetic Programming entries for
Theodoros E Karakasidis
Filippos Sofos
Christos Tsonos
Citations