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Journal Articles Molecular Informatics Year : 2019

New QSPR Models to Predict the Flammability of Binary Liquid Mixtures

Abstract

New Quantitative Structure‐Property Relationships (QSPR) are presented to predict the flash point of binary liquid mixtures, based on more than 600 experimental flash points for 60 binary mixtures. Two models are proposed based on a GA‐MLR approach that uses a genetic algorithm (GA) variable selection in multilinear regressions (MLR). In these models, mixtures were characterized by a series of mixture descriptors calculated from various mixture formula combining the molecular descriptors of the single compounds constituting the mixtures and their respective molar fractions in the mixture. The best model demonstrated good predictive capabilities with a mean absolute error of only 7.3 °C estimated for an external validation set. Moreover, this model is focused on mixture descriptors applicable to more complex mixtures, i. e. constituted of more than 2 components, and already demonstrated interesting predictions for a series of ternary mixtures.
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Dates and versions

ineris-03319070 , version 1 (11-08-2021)

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Guillaume Fayet, Patricia Rotureau. New QSPR Models to Predict the Flammability of Binary Liquid Mixtures. Molecular Informatics, 2019, 38 (8-9), pp.art. 1800122. ⟨10.1002/minf.201800122⟩. ⟨ineris-03319070⟩

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