NDLI: Calculation of polyamides melting point by quantum-chemical method and BP artificial neural networks

Content Provider	Springer Nature Link
Author	Gao, Jinwei Wang, Xueye Yu, Xinliang Li, Xiaobing Wang, Hanlu
Copyright Year	2006
Abstract	Quantitative structure–property relationships (QSPR) for the melting point of the polyamides have been determined. All descriptors were calculated from molecular structures at the B3LYP/6–31G(d) level and a QSPR model was generated by multiple linear regression (MLR). The important molecular descriptors for polyamide melting-point temperatures (T m) are the number of benzene rings in the backbone chain, the proportion of methylene and acylamino in the backbone chain, the total molecular energy and the atomic charge for the oxygen atom in the acylamino group. The MLR determination coefficient (r 2) and the standard error of estimation for the model are 0.865 and 21.34 K, respectively. In addition to the nonlinear regression technique, error back-propagation artificial neural networks (BPANN) was used to study the relationships between molecular structures and melting-point temperatures. It is concluded that melting-point temperatures for polyamides can be described by molecular chain rigidity and interchain attractive interactions. The more accurate predicted results were obtained from BPANN. Figure Experimental vs calculated Tm using BPANN
Starting Page	521
Ending Page	527
Page Count	7
File Format	PDF
ISSN	16102940
Journal	Journal of Molecular Modeling
Volume Number	12
Issue Number	4
e-ISSN	09485023
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-01-14
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Polyamides Melting point QSPR DFT BP artificial neural networks Computer Application in Life Sciences Life Sciences Health Informatics Molecular Medicine Biomedicine general Computer Applications in Chemistry
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Physical and Theoretical Chemistry Computational Theory and Mathematics Catalysis Inorganic Chemistry Computer Science Applications

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