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

Ren, Wan; Li, Mingyan; Song, Fan; Xiao, Yongjun; Zeng, Fazhan; Peng, Changjun; Liu, Honglai

doi:10.1021/acs.iecr.2c01964

Cited by 7 publications

(7 citation statements)

References 64 publications

(76 reference statements)

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“…The σ-profile binning integral interval length of 0.5 e/nm 2 is standard in the literature, and we have merged all zero sigma fractions into a single fraction (for example: S1, S6, S7, and S12). Similar σ-profile integration intervals were clearly described elsewhere. − The explicit interactions between cations and anions are not calculated due to the high computational cost. We calculated a binned probability of polarized charge at the molecular surface (i.e., the COSMO-RS-derived sigma profile) that we hypothesized is likely to implicitly capture the propensity for certain intermolecular interactions, either among anion or cation molecules or between an anion and cation.…”

Section: Methodsmentioning

confidence: 98%

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Mohan

Smith

Demerdash

et al. 2023

ACS Sustainable Chem. Eng.

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Ionic liquids (ILs) have unique solvent properties and have thus garnered significant interest. However, exhaustive experimental determination of the physicochemical properties of ILs is unrealistic due to the large structural diversity of anions and cations, their high cost, the requirements of elevated temperature and pressure, and the time required. To circumvent these experimental costs, computational approaches to accurately calculate these properties have emerged. In the present study, we present a demonstration of two machine learning (ML) models for the prediction of two critical IL physical properties, the surface tension and the speed of sound, across a wide range of temperatures and pressures. The models make use of molecular descriptors derived from the COSMO-RS, a quantum chemical-based model. The ML models show excellent agreement with experimental observations, with an R 2 value of 0.96–0.99 and RMSE of 1.71 mN/m and 16.12 m/s for the surface tension and speed of sound, respectively. This work paves the way for the development of COSMO-RS-informed ML models for the prediction of IL properties which can help to further optimize and accelerate technology development for ILs.

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Section: Methodsmentioning

confidence: 98%

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Mohan

Smith

Demerdash

et al. 2023

ACS Sustainable Chem. Eng.

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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%

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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“…Although the QSPR and QSPR-like ML methods have significantly evolved in estimating the properties of ILs, the developed models are either limited to certain types of ILs or the source of the descriptors in the models is complex. Thus, we previously developed a series of QSPR models to predict the thermal conductivity, self-diffusion coefficient, and infinite dilution molar electrical conductivity of ILs using the charge-density-distribution area of a molecule at a specific interval ( S σ i ) and cavity volume ( V COSMO ) as descriptors. These descriptors were derived from the segment area and cavity volume obtained from the 2007 version of the conductor-like screening model for segment activity coefficient (COSMO-SAC) .…”

Section: Introductionmentioning

confidence: 99%

“…These descriptors were derived from the segment area and cavity volume obtained from the 2007 version of the conductor-like screening model for segment activity coefficient (COSMO-SAC) . The introduced descriptors effectively differentiated among various ILs, with the V COSMO descriptor highlighting the significant impact of IL size on their characteristics. − Boublian et al also developed a conductivity-prediction model for deep eutectic solvents using S σ i descriptors derived from COSMO-RS. The model utilized a back-propagation artificial neural network (BP-ANN) with two hidden layers and achieved R 2 values of 0.993 and 0.984 for the training and testing sets, respectively.…”

Section: Introductionmentioning

confidence: 99%

QSPR Models Based on Multiple Linear Regression and Back-Propagation Artificial Neural Network for Predicting Electrical Conductivity of Ionic Liquids

Shi,

Song,

et al. 2024

Ind. Eng. Chem. Res.

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Electrical conductivity of ionic liquids (ILs), a crucial transfer property, has been investigated using various methods, including experimental measurements, semiempirical models, and molecular simulations. Among these methods, the quantitative structure−property relationship (QSPR) model is extensively utilized in diverse applications. However, constructing an effective QSPR model depends on the selection of the appropriate molecular descriptors. In this study, linear and nonlinear QSPR models were developed to predict the conductivity of the ILs. These models incorporated temperature as well as the molecular descriptors of cavity volume (V COSMO ) and charge-density-distribution area at specific intervals (S σi ) obtained from the conductor-like screening model for segment activity coefficient (COSMO-SAC). The evaluated data set comprised 254 ILs with 3102 data points; the ILs were considered to consist of "pure ions" or "ion pairs". Three distinct models were developed in this study: model I represented the pure ion model combined with multiple linear regression (MLR), model II referred to the ion pair model combined with MLR, and model III was constructed by considering ILs as ion pairs and using the back-propagation artificial neural network (BP-ANN). For the entire data set, models I and II exhibited coefficients of determination (R 2 ) of 0.9478 and 0.9642, respectively. Furthermore, the calculated average absolute relative deviations (AARDs) were 25.38 and 21.37% with root mean square errors (RMSEs) of 0.3291 and 0.2724, respectively. By contrast, model III demonstrated R 2 values of 0.9939, 0.9911, and 0.9887 for the training, validation, and testing sets, respectively. The corresponding AARD values were 6.96, 8.13, and 8.97%, while the RMSE values were 0.1132, 0.1214, and 0.1569, respectively. Application domain (AD) analysis revealed that 91.1% of the data points lie within the AD of model II and only 0.16% of the data showed response outliers. In contrast, 93.42% of the data were bound within the AD of model III, with no response outliers. These findings indicate that the QSPR model, which treats ILs as "ion pairs" and combines them with the BP-ANN, can predict the conductivity of diverse ILs at various temperatures with a high accuracy.

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

Cited by 7 publications

References 64 publications

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

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

QSPR Models Based on Multiple Linear Regression and Back-Propagation Artificial Neural Network for Predicting Electrical Conductivity of Ionic Liquids

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