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

• Published in: International Journal of Combinatorial Optimization Problems and Informatics Vol. 14; no. 3; pp. 24 - 30
• Main Authors: Frausto Solis, Juan, González-Barbosa, Juan Javier, Cerecedo-Cordoba, Jorge Alberto, Sánchez-Hernández, Juan Paulo, Díaz-Parra, Ocotlán, Castilla-Valdez, Guadalupe
• Format: Journal Article
• Language: English
• Published: Jiutepec International Journal of Combinatorial Optimization Problems & Informatics 31.12.2023
• Subjects: Algorithms; Cluster analysis; Clustering; Informatics; Ionic liquids; Machine learning; Melting points; Neural networks; Neurons; Optimization; Prediction models; Predictions; Regression analysis; Temperature; Variables
• ISSN: 2007-1558; 2007-1558
• DOI: 10.61467/2007.1558.2023.v14i3.384

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