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

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 conduct...

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Bibliographic Details
Published in:Fluids (Basel) 2022-10, Vol.7 (10), p.321
Main Authors: Karakasidis, Theodoros E., Sofos, Filippos, Tsonos, Christos
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Atomic properties
Electrical conductivity
Electrical resistivity
Empirical analysis
Environmental conditions
Experiments
Fluid mechanics
Genetic algorithms
Ionic liquids
Machine learning
Molecular structure
Molecular weight
Numerical methods
Numerical prediction
Observations
Physics
Properties
Research methodology
Simulation
Solvents
symbolic regression
Viscosity
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Summary: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 generalization 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 generalized and exploited in similar property prediction projects, overcoming expensive experimental procedures and computationally intensive molecular simulations.
ISSN:2311-5521
2311-5521
DOI:10.3390/fluids7100321