Relationship between Standard Enthalpies/Entropies of Formation and Topological Structural Characteristics for Saturated Hydrocarbons

Dolomatov, M. Yu.; Аубекеров, Т. М.

doi:10.1134/s0036024418030068

Cited by 3 publications

(1 citation statement)

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“…13 Hence, to correlate the molecular structures of hydrocarbon compounds with their standard enthalpies of formation, some researchers tried to develop predictive models for standard enthalpies of formation utilizing the quantitative structure−property relationship (QSPR) methods, 14 in which the molecular structure features are commonly characterized using the derived topological indices (descriptors) from molecular graphs. 15 Dolomatov et al 16 developed a multiple regression model to predict the standard enthalpies of formation for 59 saturated hydrocarbons. By characterizing the carbon bonds and the molecule branching features using the Wiener and Randićindices, this model provided improved accuracy compared with the previously proposed single-factor model.…”

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confidence: 99%

Prediction of Standard Enthalpies of Formation Based on Hydrocarbon Molecular Descriptors and Active Subspace Methodology

Guan

Zeng

et al. 2020

Ind. Eng. Chem. Res.

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Standard enthalpy of formation is an important fuel thermochemical property for energy balance calculation and equilibrium dynamics determination in combustion reactions. In this study, an approach combining the quantitative structure–property relation and the active subspace method was applied to predict the standard enthalpies of formation of different hydrocarbon classes, including alkanes, cycloalkanes, alkenes, alkynes, and aromatics. The standard enthalpies of formation in a database of 1020 hydrocarbons were predicted by a one-dimensional active subspace model, with 902 molecule topological indices being used as input descriptors. The correlation coefficient between the measured and predicted values was 0.99, and the average absolute error was 6.74 kJ/mol. The sensitivity of the prediction output to each descriptor was further evaluated, which enables us to build simplified models by only considering the most representative descriptors. Also, to assess the influences of model simplification on the prediction performance, the relation between the descriptor number and the prediction accuracy was explored. It was observed that simplified but well-predictive models can be established when the top 10 influential descriptors were selected for model building, and the correlation coefficient between the predicted and measured standard enthalpies of formation was as high as 0.96.

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