A group contribution-based prediction method for the electrical conductivity of ionic liquids

Abstract

A group contribution method is developed for estimating the electrical conductivity of ionic liquids (ILs). Based on 1578 collected experimental data covering 57 ILs in a wide range of temperature (248.05–468.15 K) and electrical conductivity (0.0017–9.1670 S m−1), the parameters of each group (cation, anion, substituent) in the method are optimized. A deterministic algorithm-based on multivariate linear regression is used with 1121 data points covering 57 ILs used as training set and the reaming 457 data points covering 20 ILs are used for validation. The prediction results (from test set) expressed as an average absolute relative deviation (AARD %) of 6.8% between the experimental and predicted electrical conductivities of ILs illustrate the good predictive capability of this group contribution method, with a maximum relative deviation (RD %) of 26.6%. This group contribution-based method can be easily extended to new IL groups that are not involved in this study once the experimental data of ILs involving these new groups become available.