Advertisement

Journal of Materials Science

, Volume 44, Issue 12, pp 3156–3164 | Cite as

Artificial neural network prediction of glass transition temperature of fluorine-containing polybenzoxazoles

  • Liwei NingEmail author
Article

Abstract

Fluorine-containing polymers belong to high-performance polymers with unique chemical and physical properties that are not observed with other organic polymers. In this article, three structural parameters were used to correlate with glass transition temperature Tg values for 52 fluorine-containing polybenzoxazoles. The descriptors obtained directly from the structures of repeating units can reflect the chain stiffness (or mobility). Back propagation artificial neural network (ANN) and multiple linear regression (MLR) analysis were used in the study. The final optimum neural network with [3-1-1] structure produced a training set root mean square (rms) error of 2.35 K (R = 0.980) and a test set rms error of 2.30 K (R = 0.978). The statistical results indicate that the ANN model given here has better predictive capability than other existing models.

Keywords

Artificial Neural Network Artificial Neural Network Model Multiple Linear Regression Model Benzoxazoles Significant Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bicerano J (2003) Encyclopedia of polymer science and technology. Wiley, New YorkGoogle Scholar
  2. 2.
    Katritzky AR, Sild S, Lobanov V, Karlson M (1998) J Chem Inf Comput Sci 38:300CrossRefGoogle Scholar
  3. 3.
    Van Krevelen DW (1990) Properties of polymers. Elsevier Science, New YorkGoogle Scholar
  4. 4.
    Bicerano J (1996) Prediction of polymers properties, 2nd edn. Marcel Dekker, New YorkGoogle Scholar
  5. 5.
    Katrizky AR, Rachwal P, Law KW, Karelson M, Lobanov VS (1996) J Chem Inf Comput Sci 36:879CrossRefGoogle Scholar
  6. 6.
    Yu XL, Yi B, Wang XY, Xie ZM (2007) Chem Phys 332:115CrossRefGoogle Scholar
  7. 7.
    Mattioni BE, Jurs PC (2002) J Chem Inf Comput Sci 42:232CrossRefGoogle Scholar
  8. 8.
    Chen X, Sztandera L, Cartwright HM (2008) Int J Intell Syst 23:22CrossRefGoogle Scholar
  9. 9.
    Hougham G, Cassidy PE, Johns K, Davidson T (1999) Fluoropolymers I. Kluwer Academic/Plenum Publishers, New YorkGoogle Scholar
  10. 10.
    Lee JK, Kim JH, Kim YJ (2003) Bull Korean Chem Soc 24:1029CrossRefGoogle Scholar
  11. 11.
    Пpиaлкo BП (1995) Handbook of polymer physical chemistry, vol 2. China Petrochemical Press, Beijing (trans: Yan J, Zhang Y)Google Scholar
  12. 12.
    Yu XL, Yi B, Liu F, Wang XY (2008) React Funct Polym 68:1557CrossRefGoogle Scholar
  13. 13.
    Yu XL, Yi B, Wang XY (2008) Eur Polym J 44:3997CrossRefGoogle Scholar
  14. 14.
    Yu XL, Yi B, Wang XY (2008) J Theor Comput Chem 7:953CrossRefGoogle Scholar
  15. 15.
    Jurs PC (1996) Computer software applications in chemistry, 2nd edn. Wiley, New YorkGoogle Scholar
  16. 16.
    Yu XL, Wang XY, Li XB, Gao JW, Wang HL (2006) Macromol Theory Simul 15:94CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.College of Mechanical EngineeringHunan Institute of EngineeringXiangtanChina

Personalised recommendations