Quantitative structure-property relationship for predicting the diffusion coefficient of ionic liquids

Xiao, Yongjun; Song, Fan; An, Shuhao; Zeng, Fazhan; Xu, Yingjie; Peng, Changjun; Liu, Honglai

doi:10.1016/j.molliq.2022.118476

Cited by 9 publications

(19 citation statements)

References 49 publications

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“…In a previous study performed by our group, S σ i and V COSMO were chosen as the structural features of substances to describe the infinite dilution molar conductivity, self-diffusion coefficients, infinite dilution mutual diffusion coefficients, and thermal conductivity of ionic liquids. ,, In this study, these descriptors were also used to construct the model. The charge density of the compounds involved in this paper extended from −2.5 to 3.0 e·nm –2 .…”

Section: Data Set and Methodologymentioning

confidence: 99%

“…The COSMO-SAC model can be used to obtain the charge density distribution (σ-profile) and cavity volume ( V COSMO ). The charge density distribution area of molecules in a specific interval ( S σ i ) obtained from the σ-profile can be used as a structural descriptor reflecting the interactions, and the obtained V COSMO can be used as a structural descriptor reflecting the size of the molecular volume. ,, Consequently, a unique set of descriptors for the molecular structure can be obtained directly from the molecular structure.…”

Section: Introductionmentioning

confidence: 99%

“…Our group has previously established three linear models based on the segment area and cavity volume descriptors obtained from the conductor-like screening model for the segment activity coefficient (COSMO-SAC) theory, 37 and the results revealed that the models satisfactorily predicted the self-diffusion coefficients of ionic liquids as well as the infinite dilution interdiffusion coefficient of ionic liquids in water. 38 Machine learning methods have been widely used to construct nonlinear models for predicting the diffusion coefficients of compounds, 39−41 such as the diffusion coefficients of acids in water at infinite dilution, 42 self-diffusion coefficients of Lennard-Jones fluids, 43 and self-diffusion coefficients of Lennard-Jones fluids in pores. 44 Allers et al 45 developed artificial neural network (ANN) models for liquid, supercritical, and gaseous states to predict the self-diffusion coefficients of 118 pure compounds using 19 molecular descriptors, such as density, critical temperature, critical pressure, and critical volume.…”

Section: Introductionmentioning

confidence: 99%

“…Mirkhani et al reported the application of Dragon software combined with genetic function approximation to filter five descriptors from multiple descriptors of 15 different classes to develop a linear QSPR model and used it to predict the diffusion coefficients of 4579 different organic compounds in air at 298.15 K and atmospheric pressure. Our group has previously established three linear models based on the segment area and cavity volume descriptors obtained from the conductor-like screening model for the segment activity coefficient (COSMO-SAC) theory, and the results revealed that the models satisfactorily predicted the self-diffusion coefficients of ionic liquids as well as the infinite dilution interdiffusion coefficient of ionic liquids in water …”

Section: Introductionmentioning

confidence: 99%

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Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study

Zeng

Ren

Xiao

et al. 2022

Ind. Eng. Chem. Res.

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The self-diffusion coefficient of pure liquids, a fundamental transport property, is involved in a wide range of applications. Many methods have been employed to study the self-diffusion coefficient, with the most popular being semiempirical models. The quantitative structure–property relationship (QSPR) has been widely used to predict various physicochemical properties of substances, but the appropriate molecular descriptors must be selected first. In this study, the charge density distribution area of molecules at a specific interval (S σi ) and cavity volume (V COSMO) was determined based on the conductor-like screening model for the segment activity coefficient (COSMO-SAC). Using these molecular descriptors, a backpropagation artificial neural network (BP-ANN) method was employed to construct a nonlinear QSPR model that can predict the self-diffusion coefficients of pure liquids under normal pressure. The data set used included 2596 data points for 238 compounds, covering a self-diffusion coefficient range of 8.74 × 10–13 to 8.66 × 10–9 m2·s–1 and a temperature range of 90.5–475.1 K. The coefficients of determination (R 2) of the BP-ANN model on the training, validation, and testing sets were all greater than 0.99. For the entire data set, the R 2, absolute average relative deviation (AARD), and root mean square error (RMSE) were 0.9940, 7.09%, and 0.1106, respectively. In an application domain (AD) analysis, 94.67% of the data were within the AD range of the model. Consequently, the model developed in this study can satisfactorily predict the self-diffusion coefficients of liquids.

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Section: Data Set and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study

Zeng

Ren

Xiao

et al. 2022

Ind. Eng. Chem. Res.

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“…The second is that the fragment area descriptor obtained by COSMO-SAC in the work can distinguish the isomers of substances. In recent studies, QSPR models showed accurate predictions for the infinite dilution molar conductivity and infinite dilution diffusion coefficient of ILs developed based on fragment charge density distributions obtained using the COSMO-SAC model. , The reason for selecting the COSMO-SAC model is that it can generate the charge density distribution’s profile, namely, σ profiles that are called molecular fingerprints, which are identical to the human fingerprints and are unique . Moreover, the fragment charge density distributions separated by various intervals are unique.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Thermal Conductivity of Ionic Liquids Using a Quantitative Structure–Property Relationship

Ren

Song

et al. 2022

Ind. Eng. Chem. Res.

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Thermal conductivity (λ) is an extremely crucial indicator of the heat transfer capability of ionic liquids (ILs) and plays a critical function in their industrial applications. In this study, there are two descriptors for model construction, namely, the charge density distribution area of ions at a specific interval (S σi) obtained using the conductor-like screening model for the segment activity coefficient (COSMO-SAC) and the cavity volume of ILs (V cosmo). Using the multiple linear regression (MLR) approach, a quantitative structure–property relationship (QSPR) model was proposed to describe the thermal conductivity of ILs. Furthermore, 606 experiment data points for 44 ILs at different temperatures and pressures were collected from the literature, which were randomly divided into a training set and a testing set for feasibility analysis. For the model built by the total data, its determination coefficients (R 2), root mean square error (RMSE), and average absolute relative deviation (AARD) are 0.9713, 0.004304, and 2.18%, respectively; thus, the developed λ-QSPR model offers a relatively good prediction of λ for ILs. Meanwhile, the percentage of extraterritorial points in the model’s application domain (AD) analysis is only 3.80% and the double extraterritorial region is blank. Overall, the proposed model reproduces the change of λ with temperature (T) and pressure (p) well and outperforms other models of similar type. Moreover, it provides an effective approach to predicting the thermal conductivity of ILs.

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Modeling self‐diffusion coefficients of ionic liquids using ePC‐SAFT and FVT combined with Einstein relation

Zuo,

Lu,

2024

AIChE Journal

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The electrolyte perturbed‐chain statistical associating fluids theory (ePC‐SAFT) coupled with free volume theory (FVT) was combined with Einstein relation, that is, ePC‐SAFT‐FVT‐E, to describe self‐diffusion coefficients (SDCs) of ionic liquids (ILs). In modeling, ePC‐SAFT was used to calculate density, while FVT parameters, determined from viscosity data, were utilized to calculate the summation of ionic SDCs through the Einstein relation with one parameter. Two strategies were employed to determine this parameter: fitting experimental data for each IL or estimating a universal parameter from van der Waals volume. Comparative analysis reveals good agreement with experimental data, with average absolute relative deviations (ARDs) of 8.14% (strategy 1) and 10.29% (strategy 2). Subsequently, cationic and anionic SDCs were reliably determined from the summation of ionic SDCs, with average ARDs of 10.80% and 10.21%, respectively. This study indicates the ePC‐SAFT‐FVT‐E model, employing viscosity‐derived parameters and three universal parameters, reliably predicts SDCs of ILs in wide temperature and pressure ranges.

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Quantitative structure-property relationship for predicting the diffusion coefficient of ionic liquids

Cited by 9 publications

References 49 publications

Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study

Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study

Predicting the Thermal Conductivity of Ionic Liquids Using a Quantitative Structure–Property Relationship

Modeling self‐diffusion coefficients of ionic liquids using ePC‐SAFT and FVT combined with Einstein relation

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