The quantitative structure-property relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids

Toots, Karl Marti; Sild, Sulev; Leis, Jaan; Acree, William E.; Maran, Uko

doi:10.1016/j.molliq.2021.117573

Cited by 9 publications

(10 citation statements)

References 79 publications

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“…The orthogonal matching pursuit (OMP) algorithm is a bottom-up feature selection algorithm that selects a feature into a linear model on each iteration based on the correlation of the features to the residual of the linear model estimations [ 48 , 113 ]. OMP was used for the selection of features (molecular descriptors) into MLR models.…”

Section: Methodsmentioning

confidence: 99%

“…Examples include the more commonly known Abraham solvation model [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ] and a selection of linear and non-linear QSPR approaches [ 46 , 47 ]. Our recent research effort in this direction concentrated on modeling the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids [ 48 ]. We have previously developed a series of gas-liquid partition coefficient models for a general treatment of solubility in traditional organic solvents [ 49 , 50 , 51 , 52 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Quantitative Structure–Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons

Toots

Sild

Leis

et al. 2022

IJMS

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Ionic liquids (ILs) are known for their unique characteristics as solvents and electrolytes. Therefore, new ILs are being developed and adapted as innovative chemical environments for different applications in which their properties need to be understood on a molecular level. Computational data-driven methods provide means for understanding of properties at molecular level, and quantitative structure–property relationships (QSPRs) provide the framework for this. This framework is commonly used to study the properties of molecules in ILs as an environment. The opposite situation where the property is considered as a function of the ionic liquid does not exist. The aim of the present study was to supplement this perspective with new knowledge and to develop QSPRs that would allow the understanding of molecular interactions in ionic liquids based on the structure of the cationic moiety. A wide range of applications in electrochemistry, separation and extraction chemistry depends on the partitioning of solutes between the ionic liquid and the surrounding environment that is characterized by the gas-ionic liquid partition coefficient. To model this property as a function of the structure of a cationic counterpart, a series of ionic liquids was selected with a common bis-(trifluoromethylsulfonyl)-imide anion, [Tf2N]−, for benzene, hexane and cyclohexane. MLR, SVR and GPR machine learning approaches were used to derive data-driven models and their performance was compared. The cross-validation coefficients of determination in the range 0.71–0.93 along with other performance statistics indicated a strong accuracy of models for all data series and machine learning methods. The analysis and interpretation of descriptors revealed that generally higher lipophilicity and dispersion interaction capability, and lower polarity in the cations induces a higher partition coefficient for benzene, hexane, cyclohexane and hydrocarbons in general. The applicability domain analysis of models concluded that there were no highly influential outliers and the models are applicable to a wide selection of cation families with variable size, polarity and aliphatic or aromatic nature.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Quantitative Structure–Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons

Toots

Sild

Leis

et al. 2022

IJMS

Self Cite

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“…The Abraham model expressions for each of the fore-mentioned processes contain a common set of solute descriptors. The series of mathematical expressions are then solved for the "best" set of descriptor values that minimizes the overall squared-summed difference between the measured properties and the respective back-calculated values based on Equations ( 1) and (2). For the methylated hydrocarbons listed in Table 1, the computational process is greatly simplified in that the E, S, A and B solute descriptors are all equal to zero.…”

Section: Calculation Of Abraham Model Solute Descriptors For Methylat...mentioning

confidence: 99%

“…Over the last 50 years numerous predictive methods have been developed based upon either quantitative structure-property relationships (QSPRs) or group contribution concepts. QSPR methods [2][3][4][5][6][7][8] identify mathematical relationships between the desired property that one wishes to calculate and other physical properties or from compound descriptor values that can be derived from molecular structure and/or quantum mechanical considerations. For some published QSPR studies [2,6,8] more than 1000 different descriptors have been considered before narrowing the descriptor set down to those yielding the desired predictive accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…QSPR methods [2][3][4][5][6][7][8] identify mathematical relationships between the desired property that one wishes to calculate and other physical properties or from compound descriptor values that can be derived from molecular structure and/or quantum mechanical considerations. For some published QSPR studies [2,6,8] more than 1000 different descriptors have been considered before narrowing the descriptor set down to those yielding the desired predictive accuracy. Group contribution methods [9][10][11][12][13][14][15][16], on the other hand, fragment the given molecule into atoms or functional groups.…”

Section: Introductionmentioning

confidence: 99%

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Abraham Solvation Parameter Model: Calculation of L Solute Descriptors for Large C11 to C42 Methylated Alkanes from Measured Gas–Liquid Chromatographic Retention Data

Sinha

Yang

et al. 2022

Liquids

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Abraham model L solute descriptors have been determined for 149 additional C11 to C42 monomethylated and polymethylated alkanes based on published Kovat’s retention indices based upon gas–liquid chromatographic measurements. The calculated solute descriptors, in combination with previously published Abraham model correlations, can be used to predict a number of very important chemical and thermodynamic properties including partition coefficients, molar solubility ratios, gas–liquid chromatographic and HPLC retention data, infinite dilution activity coefficients, molar enthalpies of solvation, standard molar vaporization and sublimation at 298 K, vapor pressures, and limiting diffusion coefficients. The predictive computations are illustrated by estimating both the standard molar enthalpies of sublimation and the enthalpies of solvation in benzene for the monomethylated and polymethylated alkanes considered in the current study.

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Gas‐to‐ionic liquid partition: QSPR modeling and mechanistic interpretation

Zou

Chang

Lou

et al. 2023

Molecular Informatics

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The present work was devoted to explore the quantitative structure‐property relationships for gas‐to‐ionic liquid partition coefficients (log KILA). A series of linear models were first established for the representative dataset (IL01). The optimal model was a four‐parameter equation (1Ed) consisting of two electrostatic potential‐based descriptors ( ΣVs,ind- ${{\rm { \Sigma }}{V}_{s,ind}^{-}}$ and Vs,max), one 2D matrix‐based descriptor (J_D/Dt) and dipole moment (μ). All of the four descriptors introduced in the model can find the corresponding parameters, directly or indirectly, from Abraham's linear solvation energy relationship (LSER) or its theoretical alternatives, which endows the model good interpretability. Gaussian process was utilized to build the nonlinear model. Systematical validations, including 5‐fold cross‐validation for the training set, the validation for test set, as well as a more rigorous Monte Carlo cross‐validation were performed to verify the reliability of the constructed models. Applicability domain of the model was evaluated, and the Williams plot revealed that the model can be used to predict the log KILA values of structurally diverse solutes. The other 13 datasets were also processed in the same way, and all of the linear models with expressions similar to equation 1Ed were obtained. These models, whether linear of nonlinear, represent satisfactory statistical results, which confirms the universality of the method adopted in this study in QSPR modeling of gas‐to‐IL partition.

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The quantitative structure-property relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids

Cited by 9 publications

References 79 publications

Machine Learning Quantitative Structure–Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons

Machine Learning Quantitative Structure–Property Relationships as a Function of Ionic Liquid Cations for the Gas-Ionic Liquid Partition Coefficient of Hydrocarbons

Abraham Solvation Parameter Model: Calculation of L Solute Descriptors for Large C11 to C42 Methylated Alkanes from Measured Gas–Liquid Chromatographic Retention Data

Gas‐to‐ionic liquid partition: QSPR modeling and mechanistic interpretation

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