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A quantitative structure−property relationship (QSPR) study is performed to develop mathematical models for prediction of the upper flammability limits (UFL) of organic compounds from their molecular structures. The structural features of the compounds are numerically represented by various kinds of calculated molecular descriptors such as topological, charge, and geometric descriptors. The genetic algorithm combined with multiple linear regression (GA-MLR) is used to select an optimal subset of descriptors that have significant contribution to the overall UFL property from the large pool of calculated descriptors. The best resulted model is a four-variable multilinear model with a defined applicability range. The average absolute error and root-mean-square error obtained for the external test set are 1.75 vol % and 2.77, respectively. The proposed model can be used to predict the UFL of organic compounds with only four preselected theoretical descriptors which can be directly calculated from molecular structure alone.

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A Quantitative Structure -Property Relationship (QSPR) model was developed to predict the flash points of organic compounds. The widely used group contribution method was employed, and a new collection of 57 functional groups were selected as the molecular descriptors. The new chemometrics method of Support Vector Machine (SVM) was employed for fitting the possible quantitative relationship that existed between these functional groups and flash points. A total of 1282 organic compounds of various chemical families were used and randomly divided into a training set (1026) and an external prediction set (256).The optimum parameters of the SVM were obtained by employing the leave-one-out cross-validation method. Simulated with the final optimum SVM, the results show that most of the predicted flash point values are in good agreement with the experimental data, with the average absolute error being 6.894 K, and the root mean square error being 11.367 for the whole dataset, which are lower than those obtained by previous works. Moreover, by employing the convenient group contribution method as well as the large modeling dataset, the presented model is also expected to be simple to apply and with a wide applicability range.