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.