In Silico Calculation of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids: Critical Review of Current Methods and New Models Based on Three Machine Learning Algorithms

The
aim of the paper is to address all the disadvantages of currently
available models for calculating infinite dilution activity coefficients
(γ∞) of molecular solutes in ionic liquids
(ILs)a relevant property from the point of view of many applications
of ILs, particularly in separations. Three new models are proposed,
each of them based on distinct machine learning algorithm: stepwise
multiple linear regression (SWMLR), feed-forward artificial neural
network (FFANN), and least-squares support vector machine (LSSVM).
The models were established based on the most comprehensive γ∞ data bank reported so far (>34 000 data
points
for 188 ILs and 128 solutes). Following the paper published previously
[J. Chem. Inf. Model 2014, 54, 1311–1324], the ILs were treated in terms of group contributions,
whereas the Abraham solvation parameters were used to quantify an
impact of solute structure. Temperature is also included in the input
data of the models so that they can be utilized to obtain temperature-dependent
data and thus related thermodynamic functions. Both internal and external
validation techniques were applied to assess the statistical significance
and explanatory power of the final correlations. A comparative study
of the overall performance of the investigated SWMLR/FFANN/LSSVM approaches
is presented in terms of root-mean-square error and average absolute
relative deviation between calculated and experimental γ∞, evaluated for different families of ILs and solutes,
as well as between calculated and experimental infinite dilution selectivity
for separation problems benzene from n-hexane and
thiophene from n-heptane. LSSVM is shown to be a
method with the lowest values of both training and generalization
errors. It is finally demonstrated that the established models exhibit
an improved accuracy compared to the state-of-the-art model, namely,
temperature-dependent group contribution linear solvation energy relationship,
published in 2011 [J. Chem. Eng. Data 2011, 56, 3598−3606].