Ionic Liquid Melting Points: Structure-Property Analysis and New Hybrid Group Contribution Model

Melting point (T-m) is one of the defining characteristics of ionic liquids (ILs) and is often one of the most important factors in their selection for applications in separation processes, lubrication, or thermal energy storage. Due to the almost limitless number of theoretically possible ILs, each with incrementally different physiochemical properties, there is significant scope for designing ILs for specific applications. However, the need for extensive synthesis and experimental characterization to find the optimum IL is a major barrier. Therefore, it is essential that predictive tools are developed for estimating the physiochemical properties of ILs. The starting point for any such approach should be the prediction of T-m since most other property models will be based on the assumption that the IL is in the liquid phase at the application temperature. While several attempts have previously been made at developing group contribution methods (GCMs) for estimating IL T-m, the complex relationship between the IL structure and T-m has resulted in only limited success. In this study, an extensive database of IL T-m has been compiled and used as the basis for a top down structure-property analysis. Based on the findings, a new hybrid GCM has been developed, which combines functional group parameters with simple, indirect structural parameters derived from the structure-property analysis. The new hybrid GCM has a mean absolute percentage error (MAPE) of 8.6% over the dataset of around 1700 data points and performs quantitatively and qualitatively better than the standard GCM approach.

Automated summary

Melting point is one of the defining characteristics of ionic liquids (ILs) and plays a crucial role in their selection for various applications. The development of predictive tools is essential to estimate the physiochemical properties of ILs, and the prediction of melting point is the starting point for such approaches. Previous attempts to predict IL melting point using group contribution methods have had limited success due to the complex relationship between IL structure and melting point. In this study, a new hybrid group contribution method is proposed based on an extensive database of IL melting points, combining functional group parameters with simple structural parameters. The new method performs better quantitatively and qualitatively than the standard group contribution method.