Prediction of Electrical Conductivity of Deep Eutectic Solvents Using COSMO-RS Sigma Profiles as Molecular Descriptors: A Quantitative Structure–Property Relationship Study

Abstract

This work presents the development of molecular-based mathematical models for the prediction of electrical conductivity of deep eutectic solvents (DESs). Two new quantitative structure–property relationship (QSPR) models based on conductor-like screening model for real solvent (COSMO-RS) molecular charge density distributions (S σ-profiles) were developed using the data obtained from the literature. The data comprise 236 experimental electrical conductivity measurements for 21 ammonium- and phosphonium-based DESs, covering a wide range of temperatures and molar ratios. First, the hydrogen-bond acceptors (HBAs) and hydrogen-bond donors (HBDs) of each DES were successfully modeled using COSMO-RS. Then, the calculated S σ-profiles were used as molecular descriptors. The relation between the conductivity and the descriptors in both models has been expressed via multiple linear regression. The first model accounted for the structure of the HBA, the HBD, the molar ratio, and temperature, whereas the second model additionally incorporated the interactions between the molecular descriptors. The results showed that by accounting for the interactions, the regression coefficient (R 2) of the predictive model can be increased from 0.801 to 0.985. Additionally, the scope and reliability of the models were further assessed using the applicability domain analysis. The findings showed that QSPR models based on S σ-profiles as molecular descriptors are excellent at describing the properties of DESs. Accordingly, the obtained model in this work can be used as a useful guideline in selecting DESs with the desired electrical conductivity for industrial applications.

A review on machine learning algorithms for the ionic liquid chemical space

“…recently published a very interesting study about the use of ML in chemical reaction networks, which shows that the prediction of new data points using ML methods is performed much faster than with DFT calculations, with equal accuracy. 76 Using COSMO-RS s-proles as data for ML methods, seems promising and has been implemented in various classical property regression models with very promising results, [77][78][79] but so far with only few implementations to ML algorithms. [80][81][82][83][84][85] classication and prediction.…”

Group and group‐interaction contribution method for estimating the melting temperatures of deep eutectic solvents

“…25 These methods included group contribution (GC) methods, molecular dynamics simulation methods, and quantitative structure-property relationship methods. 26 Sonpal 27 adopted different machine learning algorithms, including linear models like LASSO, elastic nets, ridge, and Bayesian ridge regression, to predict melting points of choline chloride-based DESs using 36 data points.…”

Synthesis, molecular dynamics simulation and adsorption study of different pollutants on functionalized mesosilica

“…The σ –profile of the non-H-bonding for NR dye demonstrated sharp peaks in range of 0– + 0.015 e/Å 2 which were related to the hydrogen elements in the methyl groups. Also, σ -profiles of the carbon of methyl groups, or the non–polar nitrogen groups of CV dye were detected between 0 and + 0.01 e/Å 55 . Furthermore, the obtained sigma profile of carbon atoms and the π-faced in the C rings were presented at about 0.0035 e/Å 56 .…”