1. Research on Thermodynamic Properties of Polybrominated Diphenylamine by Neural Network
- Author
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Chang-jun Feng, Xihua Du, Xiao-qin Shi, and Wen-chang Zhuang
- Subjects
Electronegativity ,Matrix (chemical analysis) ,Quantitative structure–activity relationship ,chemistry.chemical_compound ,Standard error ,Molecular geometry ,Artificial neural network ,chemistry ,Diphenylamine ,Substituent ,Thermodynamics ,Organic chemistry ,Physical and Theoretical Chemistry - Abstract
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 Km and electronegativity distance vectors Mm 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% respecti...
- Published
- 2015
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