Neural network based temperature-dependent quantitative structure property relations (QSPRs) for predicting vapor pressure of hydrocarbons.
A neural network based quantitative structure-property relationship (QSPR) was developed for the vapor pressure-temperature behavior of hydrocarbons based on a data set for 274 compounds. The optimal QSPR model was developed based on a 7-29-1 back-propagation neural network architecture using valance molecular connectivity indices (1chi(v), 3chi(v), 4chi(v)), molecular weight, and temperature as input parameters. The average absolute errors in vapor pressure predictions for the test, validation, and overall data sets were 8.2% (0.036 log P units or 23.2 kPA), 9.2% (0.039 log P units or 26.8 kPA), and 10.7% (0.046 log P units or 31.1 kPA), respectively. The performance of the QSPR for temperature-dependent vapor pressure, which was developed from a simple set of molecular descriptors, displayed accuracy of better than or well within the range of other available estimation methods.
Department of Chemical Engineering, University of California, Los Angeles, Los Angeles, California 90095-1592, USA.
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Yaffe
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Cohen