Hostname: page-component-7b7567c8c-2rsb6 Total loading time: 0 Render date: 2026-11-12T17:06:23.847Z Has data issue: false hasContentIssue false
  • English
  • Français

Designing Conducting Polymers with Genetic Algorithms

Published online by Cambridge University Press:  17 March 2011

R. Giro,
M. Cyrillo and
D.S. Galvão
R. Giro
Affiliation:
Instituto de Física, Universidade Estadual de Campinas - UNICAMP, Campinas, São Paulo, CEP 13083-970, CP 6165, Brazil.
M. Cyrillo
Affiliation:
Instituto de Física, Universidade Estadual de Campinas - UNICAMP, Campinas, São Paulo, CEP 13083-970, CP 6165, Brazil.
D.S. Galvão
Affiliation:
Instituto de Física, Universidade Estadual de Campinas - UNICAMP, Campinas, São Paulo, CEP 13083-970, CP 6165, Brazil.
Get access

Abstract

We have developed a new methodology to design conducting polymers with pre-specified properties using genetic algorithms (GAs). The methodology combines GAs with the Negative Factor Counting (NFC) technique. NFC is a powerful technique to obtain the eigenvalues of large matrices without direct diagonalization.We present the results for a case study of polyanilines, one of the most important families of conducting polymers. The methodology proved to be able of generating automatic solutions for the problem of determining the optimum relative concentration for binary and ternary disordered polyaniline alloys exhibiting metallic properties. The methodology is completely general and can be used to design new classes of materials.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

[1] For an overview of the area see for example, Handbook of Organic Conductive Molecules and Polymers, vols. 1–3, Nalwa, H.S. (Ed.), John Wiley & Sons Ltd., New York (1997).Google Scholar
[2] Shirakawa, H., Jovi, E. J., MacDiarmid, A. G., Chiang, C. K. and Heeger, A. J., J. Amer. Chem. Soc. 99, 578 (1977).Google Scholar
[3] See for instance, Proceedings of the International Conference on Science and Technology of Synthetic Metals, Synth. Met. 119–120 (2001).Google Scholar
[4] Dean, P., Martin, J. L., Proc. Roy. Soc. A259, 409 (1960).Google Scholar
[5] Ladik, J., Seel, M., Otto, P. and Bakhshi, A.K., Chem. Phys. 108, 203 (1986).Google Scholar
[6] Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, New York – Addison-Wesley Public Company (1989).Google Scholar
[7] Galvão, D. S., Santos, D. A. dos, Laks, B., Melo, C. P. de, and Caldas, M. J., Phys. Rev. Lett. 63, 786 (1989).Google Scholar
[8] Lavarda, F.C., Santos, M.C. dos, Galvão, D.S., and Laks, B., Phys. Rev. Lett. 73, 1267 (1994).Google Scholar
[9] Holland, J., Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor (1975).Google Scholar
[10] Forrest, S., Science 261, 872 (1993).Google Scholar
[11] MacDiarmid, A. G.. Chiang, J. C., Richter, A. F., and Epstein, A. J., Synth. Met. 18, 285 (1987).Google Scholar
[12] Haupt, R.L. and Haupt, S.E., Practical Genetic Algorithms, John Wiley & Sons, Inc., New York (1998).Google Scholar
[13] Mitchell, M., An Introduction to Genetic Algorithms, Cambridge, MIT Press (1996).Google Scholar
[14] Streitwieser, A. Jr, Molecular Orbital Theory, Wiley, New York, (1961).Google Scholar
[15] Bell, R.G., Dean, P., Hibbins-Butler, D. C., J. Phys. 3, 2111 (1970).Google Scholar
[16] Dean, P., Rev. Mod. Phys., 44, 122 (1972).Google Scholar
[17] Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P., Numerical Recipes in Fortran: The Art of Scientific Computing, Vol. 1, 2nd edition - Cambridge University Press, p. 271, Cambridge (1992).Google Scholar