Prediction of the Melting Point of Ionic Liquids with Clustering and Noeuroevolution

Authors

  • Juan Frausto Solis Instituto Tecnológico de Ciudad Madero
  • Juan Javier González-Barbosa Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero,Tamaulipas, México https://orcid.org/0000-0002-3699-4436
  • Jorge Alberto Cerecedo-Cordoba 1 Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Cuidad Madero, Tamaulipas, México
  • Juan Paulo Sánchez-Hernández Universidad Politécnica del Estado de Morelos https://orcid.org/0000-0002-9448-1946
  • Ocotlán Díaz-Parra Universidad Politécnica de Pachuca, Pachuca, México https://orcid.org/0000-0002-8740-3747
  • Guadalupe Castilla-Valdez Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero,Tamaulipas, México

DOI:

https://doi.org/10.61467/2007.1558.2023.v14i3.384

Keywords:

Ionic Liquids, Clustering analysis, Neuroevolution, Neural Networks, Machine Learning

Abstract

Ionic liquids (ILs) are salts with a wide liquid temperature range and low melting points and can be fine-tuned to have specific physicochemical properties by the selection of their anion and cation. However, having a physical synthesis of multiple ILs for testing purposes can be expensive. For this reason, an in-silico estimation of physicochemical properties is desired. The selection of these components is limited by the low precision offered by state-of-the-art predictive models. In this paper, we explore the prediction of melting points with clustering algorithms and a novel Neuroevolution approach. We focused our design on simplicity. We concluded that performing clustering analysis in a previous phase of the model generation improves the estimation accuracy of the melting point, which is validated in experimentation made in-silico

Author Biographies

Juan Javier González-Barbosa, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero,Tamaulipas, México

Full Professor at Graduate and Research Division of Instituto Tecnológico de Ciudad Madero from Tecnológico Nacional de México.

Jorge Alberto Cerecedo-Cordoba, 1 Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Cuidad Madero, Tamaulipas, México

Associate Professor at Instituto Tecnológico de Ciudad Madero usually works in computer sciences, especially in machine learning in industry and academic sectors.

   

Juan Paulo Sánchez-Hernández, Universidad Politécnica del Estado de Morelos

Professor at Universidad Politécnica del Estado de Morelos, Information Systems Department

Ocotlán Díaz-Parra, Universidad Politécnica de Pachuca, Pachuca, México

Full professor at Universidad Politécnica de Pachuca, México

Guadalupe Castilla-Valdez, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero,Tamaulipas, México

Full Professor at Graduate and Research Division of Tecnológico de Ciudad Madero.

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Published

2023-12-31

How to Cite

Frausto Solis, J., González-Barbosa, J. J., Cerecedo-Cordoba, J. A., Sánchez-Hernández, J. P., Díaz-Parra, O., & Castilla-Valdez, G. (2023). Prediction of the Melting Point of Ionic Liquids with Clustering and Noeuroevolution. International Journal of Combinatorial Optimization Problems and Informatics, 14(3), 24–30. https://doi.org/10.61467/2007.1558.2023.v14i3.384

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