Leave-one-ion-out cross-validation for assisting in developing robust QSPR models of ionic liquids

Precisely determining thermodynamic properties such as density (& rho;) and viscosity (& eta;) at variable temperatures and pressures is necessary in the chemical process design of ionic liquids (ILs). Quantitative structure-property relationship (QSPR) models offer a quick and accurate route for obtaining the thermodynamic properties of ILs under variable environmental conditions. However, QSPR models employing the leave-one-out cross-validation (LOO-CV) method are often hindered by a "pseudo-high" robustness, weakened stability, and unbalanced data point distribution when used for determining the & rho; and & eta; of ILs. In this work, we propose a rigorous evaluation method for QSPR models, called leave-one-ion-out cross-validation (LOIO-CV), to evaluate the thermodynamic properties of ILs. By balancing the distribution of data points in ILs, twof(T,P,I)-QSPR models are developed with norm indexes (I) to predict & rho; and & eta; of ILs at variable temperatures and pressures. Our LOIO-CV method enhances the stability of QSPR models when predicting the properties of ILs with novel cations and anions, which is crucial for the data-driven design of new ILs.