Modeling from Theory and Modeling from Data: Complementary or Alternative Approaches? The Case of Ionic Liquids

Abstract

In the field of ionic liquids (ILs), theory‐driven modeling approaches aimed at the best fit for all available data by using a unique, and often nonlinear, model have been widely adopted to develop quantitative structure–property relationship (QSPR) models. In this context, we propose chemoinformatic and chemometric data‐driven procedures that lead to QSPR soft models with local validity that are able to predict relevant physicochemical properties of ILs, such as viscosity, density, decomposition temperature, and conductivity. These models, which use readily available and easily interpretable VolSurf+ descriptors, represent an unexploited opportunity for experimentalists to model and predict the physicochemical properties of ILs in industrial R&D design.

Data‐Driven Modelling of Gas Solubility in Ionic Liquids Using Principal Properties as Orthogonal Descriptors

“…VolSurf+ descriptors, used as such or in their compacted form of cation and anion PPs, PP+ and PP‐ respectively, were successfully used to develop Quantitative Structure Activity Relationships (QSARs) to model and predict ILs toxicity,, polarity, heat capacity, density, viscosity, conductivity and decomposition temperature, which are properties of paramount importance for their industrial applications.…”

Gas principal properties as new compact descriptors for data-driven gas solubility modelling

“…These parameters used as such, or compacted into cationic and anionic PPs, PP+ and PP-for ILs, respectively, 17 were applied to develop QSARs relating the chemical structure of ILs to their toxicities, 17,18 or to important physico-chemical properties such as polarity, 19 heat capacity, 20 viscosity, density, decomposition temperature and conductivity. 21 Such an approach has been illustrated in detail and allowed design of ILs for specific applications. 22 The above examples illustrate the use of PPs as molecular descriptors for building blocks to predict the biological or physico-chemical properties of more complex chemical entities.…”

“…30 Modelling and predicting gas solubility adopting theory-driven approaches, such as quantum mechanical calculations, 31 mixed quantum mechanics/molecular mechanics 32 and force field 33 models have been reported. 34 It has recently been proposed 21 that data-driven modelling approaches can complement and usefully integrate theory-driven ones.…”