While several group contribution method (GCM) models have been developed in recent years for the prediction of ionic liquid (IL) properties, some challenges exist in their effective application. Firstly, the models have been developed and tested based on different datasets; therefore, direct comparison based on reported statistical measures is not reliable. Secondly, many of the existing models are limited in the range of ILs for which they can be used due to the lack of functional group parameters. In this paper, we examine two of the most diverse GCMs for the estimation of IL melting point; a key property in the selection and design of ILs for materials and energy applications. A comprehensive database consisting of over 1300 data points for 933 unique ILs, has been compiled and used to critically evaluate the two GCMs. One of the GCMs has been refined by introducing new functional groups and reparametrized to give improved performance for melting point estimation over a wider range of ILs. This work will aid in the targeted design of ILs for materials and energy applications.

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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.

Thermal conductivities of nine different bis­((trifluoromethyl)­sulfonyl)­imide-based ionic liquids (ILs), [C2mim]­[NTf2], [C4mmim]­[NTf2], [C8mim]­[NTf2], [P1,4,4,4]­[NTf2], [N1,1,1,4]­[NTf2], [N1,8,8,8]­[NTf2], [C4 3mpy]­[NTf2], [C4 4mpy]­[NTf2], and [C4mpy]­[NTf2], have been measured using the modified transient plane source method (MTPS) over the temperature range from 298 to 348 K at atmospheric pressure. The thermal conductivities of all the studied ILs are in the range of 0.120–0.150 W·m–1·K–1, with a combined expanded uncertainty of 0.01 W·m–1·K–1 at a 95% confidence level. In all cases, thermal conductivity decreases with increasing temperature and follows a linear trend, as is typical for most liquids and falls within the reported range for ILs. Two existing group contribution models (GCMs) with differing modeling philosophies have also been extended and tested against a database consisting of this new data combined with all existing IL thermal conductivity data in the literature. The extended GCMs yielded average absolute relative deviations of 5.5% and 4.9%, respectively, for the entire dataset.