Abstract
In the present work, a new molecular-based model is presented for estimation of the upper flammability limit (UFL) of pure compounds. The parameters of the model are the number of occurrences of a new collection of 113 functional groups. On the basis of these 113 functional groups, a feed-forward neural network is presented to estimate the UFL of pure compounds. The squared correlation coefficient, absolute percent error, standard deviation, and root-mean-square error of the model over the 867 pure compounds used for the development of the model are 0.9469, 7.07%, 0.883, 0.882, respectively. Therefore, the model is accurate and can be used to predict the UFL for a wide range of pure compounds.
Data set of 867 pure compounds found and their UFLs used to develop the model. This material is available free of charge via the Internet at http://pubs.acs.org.
