Prediction of Thermal Decomposition Temperature of Polymers Using QSPR Methods

The relationship between thermal decomposition temperature and structure of a new data set of eighty monomers of different polymers were studied by multiple linear regression (MLR). The stepwise method was used in order to variable selection. The best descriptors were selected from over 1400 descriptors including; topological, geometrical, electronic and hybrid descriptors. The effect of number of descriptors on the correlation coefficient (R) and F-ratio were considered. Two models were suggested, one model having four descriptors (<TEX>$R^2$</TEX> = 0.894, <TEX>$Q^2_{cv}$</TEX> = 0.900, F = 172.1) and other model involving 13 descriptors (<TEX>$R^2$</TEX> = 0.956, <TEX>$Q^2_{cv}$</TEX> = 0.956, F = 125.4).

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