A group contribution method to predict the glass transition temperature of ionic liquids

A simple group contribution method to predict the glass transition temperature of several ionic liquids is present. Experimental data of 150 ionic liquids were taken from the literature and used to obtain the contributions for the cation-anion groups in a correlation set. The optimum parameters of the method were obtained using a genetic algorithm-based on multivariate linear regression. The capabilities of the designed method were tested in the prediction of the glass point of another 100 ionic liquids not used in the correlation step. The results show that the group contribution method represents an excellent alternative for the estimation of the glass transition temperature of diverse ionic liquids from the knowledge of their molecular structure with an average deviation of 5% and a correlation coefficient of 0.91.

► 150 ionic liquids were used to obtain the contributions for 26 cation groups and 36 anion groups.
► Optimum parameters were obtained by a genetic algorithm-based on multivariate linear regression.
► The results show an average deviation of 5% in the prediction of the glass transition temperature of several ionic liquids.