Prediction of properties in homologous series with a shortcut QS2PR method

Cholakov, Georgi St.; Stateva, Roumiana P.; Shacham, Mordechai; Brauner, Neima

doi:10.1002/aic.11059

Cited by 11 publications

(10 citation statements)

References 18 publications

(27 reference statements)

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“…The minimum information required for deriving a structure-structure relation for a target compound in terms of m predictive compounds is the availability of m -1 noncollinear molecular descriptors for both the predictive and the target compounds. 12 For example, for m ) 3 the coefficients of the structure-structure relation x t ) ∑ i)1 3 β i x i are obtained by the solution of the following system of three linear equations:…”

Section: Methodsmentioning

confidence: 99%

“…The choice of the correct method for property estimation, as illustrated by Wakeham et al, 9 can seriously influence process design, so the development of novel, efficient, and accurate property prediction methods is very important. Recently, a new quantitative structure-structure-property-relationship (QS2PR) method [10][11][12][13][14][15] has been developed to complement the existing QSPR methods. It can predict a large number of properties with a single property-property correlation employing the coefficients of the structure-structure correlation of a target compound and the members of its similarity group.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative Structure−Property Relationships for Prediction of Phase Equilibrium Related Properties

Shacham

Cholakov

Stateva

et al. 2009

Ind. Eng. Chem. Res.

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In this work, novel techniques for predicting vapor pressure and binary interaction coefficients for homologous series are developed based on the previously proposed targeted quantitative structure−property relationship (TQSPR) and QS2PR methods. For predicting vapor pressure variation as a function of temperature, a two-reference compound (TRC) QSPR method is suggested. This method uses two, structurally similar predictive compounds with available vapor pressure data to predict point by point the vapor pressure or the saturation temperature of a target compound. For the target compound, only structural information is required. The two variants of the method were applied to several homologous series. They demonstrate prediction of vapor pressure within experimental uncertainty, depending on the level of similarity between the predictive compounds and the target compound. A targeted QSPR method for prediction of the binary interaction coefficients (k ij ) in cubic equations of state for a compound with the members of its homologous series is also presented. The coefficients for the Soave−Redlich−Kwong and Peng−Robinson equations, used to test the method, were reproduced within the deviation of those obtained from regressed experimental data.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative Structure−Property Relationships for Prediction of Phase Equilibrium Related Properties

Shacham

Cholakov

Stateva

et al. 2009

Ind. Eng. Chem. Res.

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“…These two parameters are determined from the results of quantum mechanical (QM) COSMO solvation calculations. This is the most time-consuming step; however, a large database of DFT/COSMO calculation results maintained by Liu’s group at the Virginia Polytechnic Institute and State University (denoted as the VT database − ) is freely available and is used in this work. Once the QM data are available, the calculation of a ( T , x ) and b ( x ) takes a fraction of a second using a modern personal computer.…”

Section: Computational Detailsmentioning

confidence: 99%

First-Principles Prediction of Vapor−Liquid−Liquid Equilibrium from the PR+COSMOSAC Equation of State

Hsieh

Lin

2010

Ind. Eng. Chem. Res.

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We present the prediction of vapor-liquid-liquid equilibrium (VLLE) from a newly developed PR+COSMOSAC equation of state (EOS). Unlike semiempirical thermodynamic models whose parameters vary with the data used in regression and reparameterization are usually necessary for describing VLLE, this theoretically sound model with its parameters determined from first-principles calculations is not biased against descriptions of either VLE or LLE and can thus be used to predict the occurrence conditions of VLLE. In this model, the molecular interaction and size parameters in the Peng-Robinson EOS are determined directly from a solvation model based on first principle solvation calculations. This new EOS contains neither species dependent parameter nor binary interaction parameters and can be used to predict vapor pressure, liquid density, critical properties of pure substances, and VLE and LLE of mixtures. Without adjustment to the model parameters, this method can predict VLLE with accuracy similar to that of the method modified UNIFAC model. This model is particular useful for the design of new processes involving chemicals whose interaction parameters are not available due to the lack of experimental data.

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“…Over the years, attempts have been made to estimate the T m of new solid substances prior to the synthesis, e.g., by quantitative structure–property relationships (QSAR) − and by the commercially available software programs based on different molecular descriptors . However, better results were reported for structurally related components, i.e., homologous series of components rather than nonhomologous series of components. − Hence, those methods are yet not attractive for potential practical applications, particularly not for nonhomologous series of components.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Solid State Properties of Cocrystals Using Artificial Neural Network Modeling

Krishna

Ukrainczyk

Żegliński

et al. 2017

Crystal Growth & Design

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Using Artificial Neural Networks (ANNs), four distinct models have been developed for the prediction of solid-state properties of cocrystals: melting point, lattice energy, and crystal density. The models use three input parameters for the pure model compound (MC) and three for the pure coformer. In addition, as an input parameter the model uses the pK a difference between the MC and the coformer, and a 1:1 MC−conformer binding energy as calculated by a force field method. Notably, the models require no data for the actual cocrystals. In total, 61 CCs (two-component molecular cocrystals) were used to construct the models, and melting temperatures and crystal densities were extracted from the literature for four MCs: caffeine, theophylline, nicotinamide, and isonicotinamide. The data set includes 14 caffeine cocrystals, 9 theophylline cocrystals, 9 nicotinamide cocrystals, and 29 isonicotinamide cocrystals. Model-I is trained using known cocrystal melting temperatures, lattice energies, and crystal densities, to predict all three solid-state properties simultaneously. The average relative deviation for the training set is 2.49%, 6.21%, and 1.88% for the melting temperature, lattice energy, and crystal density, respectively, and correspondingly 6.26%, 4.58%, and 0.99% for the valdation set. Model-II, model-III, and model-IV were built using the same input neurons as in model-I, for separate prediction of each respective output solid−state property. For these models the average relative deviation for the training sets becomes 1.93% for the melting temperature model-II, 1.29% for the lattice energy model-III, and 1.03% for the crystal density model-IV, and correspondingly 2.23%, 2.40%, and 1.77% for the respective validation sets.

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Prediction of properties in homologous series with a shortcut QS2PR method

Cited by 11 publications

References 18 publications

Quantitative Structure−Property Relationships for Prediction of Phase Equilibrium Related Properties

Quantitative Structure−Property Relationships for Prediction of Phase Equilibrium Related Properties

First-Principles Prediction of Vapor−Liquid−Liquid Equilibrium from the PR+COSMOSAC Equation of State

Prediction of Solid State Properties of Cocrystals Using Artificial Neural Network Modeling

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