Abstract
A new group contribution (GC) quantitative structure-property relationship (QSPR) for estimating density (ρ) of pure ionic liquids (ILs) as a function of temperature (T) and pressure (p) is developed on the basis of the most comprehensive collection of volumetric data reported so far (in total 41 250 data points, deposited for 2267 ILs from diverse chemical families). The model was established based on a carefully revised, evaluated, and reduced data set, whereas the adopted GC methodology follows the approach proposed previously [ Ind. Eng. Chem. Res. 2012, 51, 591−604]. However, a novel approach is proposed to model both temperature and pressure dependence. The idea consist of an independent representation of reference density ρ0 at T0 = 298.15 K and ρ0 = 0.1 MPa and dimensionless correction f(T, P) ≡ ρ(T, p)/ρ0 for other conditions of temperature and pressure. Three common machine learning algorithms are employed to represent the quantitative structure–property relationship between the studied property...
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