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

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 insilico 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.