Prediction of ionic liquid's heat capacity by means of their in silico principal properties

Abstract

The in silico principal properties (PPs) of ionic liquids (ILs), derived by means of the VolSurf+ approach, were used to develop a Partial Least Squares (PLS) model able to find a quantitative correlation among IL descriptors (accounting for both cationic and anionic structural features) and heat capacity values, providing affordable predictions validated by experimental C_p measurements for an external set of ILs. In silico predictions allowed the selection of a limited number of structurally different ILs with similar C_p values, providing the possibility to select an optimal IL according to efficiency, as well as to environmental and economic sustainability. The present general procedure, using readily available descriptors for above 8000 ILs and adopting an accessible statistical procedure such as PLS, could be extended to other QSPR models.