Extensive Databases and Group Contribution QSPRs of Ionic Liquids Properties. 1. Density

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 end points, GCs, T, and p, namely, multiple linear regression,
feed-forward artificial neural network, and least-squares support
vector machine. On the basis of detailed statistical analysis of the
resulting models, including both internal and external stability checks
by means of common statistical procedures such as cross-validation, y-scrambling, and “hold-out” testing, the
final model is selected and recommended. An impact of type of cation
and anion of the accuracy of calculations is highlighted and discussed.
Performance of the new model is finally demonstrated by comparing
it with similar methods published recently in the literature.