Machine learning models for phase transition and decomposition temperature of ionic liquids

Abstract

The working temperature range of Ionic Liquids (IL) is determined by their liquid state range, where the IL’s melting point/glass transition and decomposition temperatures define the lower and upper limits of the range, respectively. Computational prediction of the structure of new ILs with required properties, e.g. which can exist in a liquid state at room temperature and are stable up to high temperatures, is a much less time-consuming and less expensive approach than stepwise synthesis and experimental examination of all presupposed ILs. Therefore, in the present work the quantitative structure–property relationship (QSPR) models were developed to predict the glass transition temperature (Tg), melting point (Tm), and decomposition temperatures (Td) of ILs. We showed that a use of component validation protocol provided a better agreement of statistical parameters for the training and test sets. The performance of various modeling algorithms and descriptor sets was discussed and compared and advantages of descriptor-less as well as multi-task modeling were shown. An explanation of the models using statistical analysis of functional groups and Molecular Matched Pairs were provided for the mixtures for the first time. The experimental data and models, which are the first publicly available models for prediction of transition and decomposition temperatures of ILs, are publicly available online at http://ochem.eu/article/140250.