Predicting the Net Heat of Combustion of Organosilicon Compounds from Molecular Structures

The net heat of combustion is one of the most important properties
of flammable substances that can be used to estimate the potential
fire hazards of chemicals once they ignite and burn. This study proposed
a quantitative structure–property relationship model to predict
the net heat of combustion of 308 organosilicon compounds from only
the knowledge of their molecular structures. Various kinds of molecular
descriptors, such as topological, charge, and geometric descriptors,
were calculated to represent the molecular structures of organosilicon
compounds. The genetic algorithm combined with multiple linear regression
is employed to select optimal subset of descriptors that have significant
contribution to the overall net heat of combustion property. The best
resulted model is a three-variable multilinear model, with the root-mean-square
error and average absolute error for the external test set being 176.8
and 111.2 kJ/mol, respectively. Model validation was also performed
to check the stability and predictive capability of the presented
model. The results showed that the presented model is a valid and
predictive model. This study can provide a new way for predicting
the net heat of combustion of organosilicon compounds for engineering.