A new group-interaction contribution method to predict the thermal decomposition temperature of ionic liquids

In this study, a new approach based on group-interaction contribution (GIC) is proposed for predicting the thermal decomposition temperature (Td) of ionic liquids (ILs). It was developed using a large database which consisted of 639 experimental data points associated with a wide variety of cations and anions. The whole dataset was split randomly into a training set having 499 data points and a validation set with 140 data points. From average absolute relative deviation (%AARD) and correlation coefficient (R2) values calculated as 4.22% and 0.866 respectively, it was concluded that the model was accurate enough for reliable predictions. More importantly, the new model accounts for isomers distinguishing, which represents an advantage over the conventional group contribution methods reported in the literature.