Prediction of critical properties of sulfur-containing compounds: New QSPR models
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- @Article{GHOMISHEH:2020:JMGM,
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author = "Zahra Ghomisheh and Ali Ebrahimpoor Gorji and
Mohammad Amin Sobati",
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title = "Prediction of critical properties of sulfur-containing
compounds: New {QSPR} models",
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journal = "Journal of Molecular Graphics and Modelling",
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volume = "101",
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pages = "107700",
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year = "2020",
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ISSN = "1093-3263",
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DOI = "doi:10.1016/j.jmgm.2020.107700",
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URL = "http://www.sciencedirect.com/science/article/pii/S1093326320304897",
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keywords = "genetic algorithms, genetic programming, Critical
properties, Sulfur-containing compounds, Quantitative
structure-property relationship (QSPR), Genetic
programming (GP), The domain of applicability",
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abstract = "In this study, new models have been proposed for the
prediction of different critical properties (critical
temperature (TC), critical pressure (PC), critical
volume (VC), and acentric factor (omega)) of the
sulfur-containing compounds based on quantitative
structure-property relationship (QSPR). An extensive
data set containing experimental data of over 130
different sulfur-containing compounds was employed.
Enhanced Replacement Method (ERM) was applied for
subset variable selection. Based on ERM selected
descriptors, two different models, including linear
model and genetic programming (GP) based non-linear
model have been proposed for each critical property.
The predicted values of each target were in good
agreement with the experimental data. For GP-based
models, the values of the coefficient of determination
(R2) were 0.936, 0.976, 0.990, and 0.917 for TC, PC,
VC, and omega, respectively. After revisiting the
available QSPR models, it was found that the domain of
applicability of new models has been expanded",
- }
Genetic Programming entries for
Zahra Ghomisheh
Ali Ebrahimpoor Gorji
Mohammad Amin Sobati
Citations