Chemical structure-based models for prediction of density of ammonium and phosphonium-based deep eutectic solvents

Abstract

In this work, linear and nonlinear quantitative structure–property relationship (QSPR) models are proposed to predict the density of deep eutectic solvents (DESs). A large experimental database of 2005 density data belonging to chloride and bromide-based DESs with ammonium and phosphonium-based cations and various hydrogen bond donors (HBDs) was collected from literatures. Using a modified particle swarm optimization (MPSO) variable-selection approach, subsets of relevant descriptors were selected to relate the density of DESs to their corresponding cation and HBD structures. To develop the nonlinear models, an adaptive neuro-fuzzy inference system (ANFIS) method was used with the same inputs and outputs of linear models. The squared correlation coefficient (R2) of linear models for chloride and bromide-based DESs are 0.897 and 0.960, while the density of DESs with anion part of chloride and bromide is predicted by ANFIS with the R2 of 0.956 and 0.980, respectively. The internal and external validations of the proposed models indicate that these models can be used for prediction of DESs densities with high accuracy and reliability.