Determination of the normal boiling point of chemical compounds using a quantitative structure–property relationship strategy: Application to a very large dataset

Gharagheizi, Farhad; Mirkhani, Seyyed Alireza; Ilani-Kashkouli, Poorandokht; Mohammadi, Amir H.; Ramjugernath, Deresh; Richon, Dominique

doi:10.1016/j.fluid.2013.06.034

Cited by 24 publications

(8 citation statements)

References 37 publications

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“…27 The heterogeneity of the 130 molecules selected after the preliminary screening was evaluated by the APfp fingerprint 28 in WebMolCS, and the molecules were grouped into 18 clusters. Two near-azeotropic molecules were selected in each cluster, where molecule pairs were considered near-azeotropic if their predicted boiling points 29 were within 5 K. If there was no near-azeotropic pair in the same cluster, a second near-azeotropic molecule was selected from a different cluster. Using near-azeotropic molecules provides a strong test of the capability of any resulting model to predict the adsorption-based separation of challenging pairs of molecules.…”

Section: Methodsmentioning

confidence: 99%

Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks

Choi

Tang

et al. 2021

J. Phys. Chem. C

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Adsorption-based separations using metal–organic frameworks (MOFs) are a promising alternative to traditional energy-intensive separation process. Machine learning (ML) methods have been applied to predict large collections of adsorption isotherms in MOFs. Previous ML models, however, focus only on predicting single-component adsorption isotherms of a small number of molecules at a single temperature and lack accuracy in the dilute limit. Here we describe a useful strategy for predicting Henry’s constants and heats of adsorption for a diverse set of molecules in large collections of MOFs. To achieve this, a data set containing 21,195 MOF–molecule pairs with 45 adsorbates in 471 MOFs is generated, and a set of 135 descriptors combining energy and chemical information is developed. Robust ML models are developed to predict Henry’s constants and heats of adsorption after removing physically unfavorable adsorption pairs. The adsorption selectivity of near-azeotropic mixtures at two temperatures (300 and 373 K) is predicted with acceptable accuracy by using the predicted Henry’s constants and heats of adsorption. The ability to make temperature-dependent predictions is important for many practical separation applications. Our work sheds light on important challenges and opportunities for developing accurate models predicting adsorption properties for diverse collection of adsorbates and adsorbents.

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Section: Methodsmentioning

confidence: 99%

Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks

Choi

Tang

et al. 2021

J. Phys. Chem. C

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“…This methodology has been applied to a very wide range of properties, from which we can only name a few. Surfactant cloud points [13], pKa [14], normal boiling points [15] and another one in which the impressive number of 17768 pure chemical compounds were considered and which includes an overview of previous models [16], octanol-water partition coefficients [17] and homogeneous catalysis [18]. The observation though is that accuracy and reliability are often still too limited, something we will further comment on after the next paragraph on the Group Contribution methodology.…”

Section: Current Methodsmentioning

confidence: 99%

“…Still, meanwhile neural networks and other artificial intelligence methods are gaining exposure, not only for physicochemical properties but in a much wider area including describing the relation between (chemical plant) process parameters like catalyst type, catalyst concentration, flow, feed characteristics and so forth. Boiling points of organics were modelled using neural networks using 17,768 pure chemical compounds as available input data for constructing the model [16]. The average absolute relative deviations of the predicted properties from existing literature values: 3.2% and squared correlation coefficient R 2 was 0.94.…”

Section: Qspr Methods Using Molecular Descriptorsmentioning

confidence: 99%

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

Meier

2019

Applied Sciences

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Physicochemical properties of chemicals as referred to in this review include, for example, thermodynamic properties such as heat of formation, boiling point, toxicity of molecules and the fate of molecules whenever undergoing or accelerating (catalytic) a chemical reaction and therewith about chemical equilibrium, that is, the equilibrium in chemical reactions. All such properties have been predicted in literature by a variety of methods. However, for the experimental scientist for whom such predictions are of relevance, the accuracies are often far from sufficient for reliable application We discuss current practices and suggest how one could arrive at better, that is sufficiently accurate and reliable, predictive methods. Some recently published examples have shown this to be possible in practical cases. In summary, this review focuses on methodologies to obtain the required accuracies for the chemical practitioner and process technologist designing chemical processes. Finally, something almost never explicitly mentioned is the fact that whereas for some practical cases very accurate predictions are required, for other cases a qualitatively correct picture with relatively low correlation coefficients can be sufficient as a valuable predictive tool. Requirements for acceptable predictive methods can therefore be significantly different depending on the actual application, which are illustrated using real-life examples, primarily with industrial relevance. Furthermore, for specific properties such as the octanol-water partition coefficient more close collaboration between research groups using different methods would greatly facilitate progress in the field of predictive modelling.

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“…Details are given in the Supporting Information. We scanned the molecules' boiling temperatures as predicted by the model of Gharagheizi et al 56 to find pairs of near-azeotropic molecules. From these pairs, we picked the set of 12 molecules listed in Table 1 for further study.…”

Section: ■ Introductionmentioning

confidence: 99%

Adsorption-Based Separation of Near-Azeotropic Mixtures—A Challenging Example for High-Throughput Development of Adsorbents

2021

Self Cite

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Adsorption of gas mixtures is central to adsorption-based gas separations, and the number of adsorbate mixture/adsorbent systems that exist is staggering. Because examples of machine learning (ML) models predicting single-component adsorption of arbitrary molecules in large libraries of crystalline adsorbents have been developed, it is interesting to determine whether these models can accurately predict mixture adsorption. Here, we use molecular simulations to generate mixture adsorption data with a set of 12 near-azeotropic molecules in a diverse set of MOFs. These data provide a challenging example for any method to rapidly predict mixture adsorption in MOFs. We combine a previous ML single-component isotherm model with ideal adsorbed solution theory (IAST) to make predictions that can be compared directly with molecular simulation data for these adsorbed mixtures. This combination of ML and IAST illustrates the scope that is available with these methods, but the accuracy of the resulting predictions is disappointing. By examining the same examples with IAST based on minimal molecular simulation data for single-component isotherms, we show that having an accurate description of adsorption in the dilute loading limit is critical to being able to accurately predict mixture adsorption. This observation points to a useful direction for future work developing robust ML models of adsorption isotherms for diverse collections of molecules and adsorbents.

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Determination of the normal boiling point of chemical compounds using a quantitative structure–property relationship strategy: Application to a very large dataset

Cited by 24 publications

References 37 publications

Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks

Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

Adsorption-Based Separation of Near-Azeotropic Mixtures—A Challenging Example for High-Throughput Development of Adsorbents

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