Reliable and robust f(T,P,I)-QSPR models for ionic liquids enabled by
balancing data distribution and LOIO-CV
Abstract
The thermodynamic properties at variable temperature and pressure, such
as density (ρ) and viscosity (η) are necessary in chemical process
design. The quantitative structure-property relationship (QSPR) is a
quick and accurate method to obtain the properties from a large number
of potential ionic liquids (ILs). The QSPR models for ρ and η may have
“pseudo-high” robustness validated by leave-one-out cross-validation
(LOO-CV) and weakened stability with the unbalanced data point
distribution. A rigorous model evaluation method named the
leave-one-ion-out cross-validation (LOIO-CV) was proposed to evaluate
robustness of ILs QSPR models. Balancing the distribution of data points
in ILs, two f(T,P,I)-QSPR models were developed with norm index (I) to
predict ρ and η of ILs at variable temperature and pressure. LOIO-CV
method can enhance the stability QSPR model in predicting the properties
of ILs with new cations and anions, which is essential for data driven
design of ILs.