Research on Thermodynamic Properties of Polybrominated Diphenylamine by Neural Network.

Based on the location of bromine substituents and conjugation matrix, a new substituent position index ⁰X not only was defined, but also molecular shape indexes K_m and electronegativity distance vectors M_m of diphenylamine and 209 kinds of polybrominated diphenylamine (PBDPA) molecules were calculated. Then the quantitative structure-property relationships (QSPR) among the thermodynamic properties of 210 organic pollutants and ⁰X, K₃, M₂₉, M₃₆ were founded by Leaps-and-Bounds regression. Using the four structural parameters as input neurons of the artificial neural network, three satisfactory QSPR models with network structures of 4:21:1, 4:24:1, and 4:24:1 respectively, were achieved by the back-propagation algorithm. The total correlation coefficients R were 0.9999, 0.9997, and 0.9995 respectively and the standard errors S were 1.036, 1.469, and 1.510 respectively. The relative mean deviation between the predicted value and the experimental value of S⌮, ΔfH⌮ and ΔfG⌮ were 0.11%, 0.34% and 0.24% respectively, which indicated that the QSPR models had good stability and superior predictive ability. The results showed that there were good nonlinear correlations between the thermodynamic properties of PBDPAs and the four structural parameters. Thus, it was concluded that the ANN models established by the new substituent position index were fully applicable to predict properties of PBDPAs. [ABSTRACT FROM AUTHOR]