Skip to main content
    • Aa
    • Aa

Designing Conducting Polymers with Genetic Algorithms

  • R. Giro (a1), M. Cyrillo (a1) and D.S. Galvão (a1)

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.

Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

[5] J. Ladik , M. Seel , P. Otto and A.K. Bakhshi , Chem. Phys. 108, 203 (1986).

[7] D. S. Galvão , D. A. dos Santos , B. Laks , C. P. de Melo , and M. J. Caldas , Phys. Rev. Lett. 63, 786 (1989).

[8] F.C. Lavarda , M.C. dos Santos , D.S. Galvão , and B. Laks , Phys. Rev. Lett. 73, 1267 (1994).

[10] S. Forrest , Science 261, 872 (1993).

[11] A. G. MacDiarmid . J. C. Chiang , A. F. Richter , and A. J. Epstein , Synth. Met. 18, 285 (1987).

[16] P. Dean , Rev. Mod. Phys., 44, 122 (1972).

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

MRS Online Proceedings Library (OPL)
  • ISSN: -
  • EISSN: 1946-4274
  • URL: /core/journals/mrs-online-proceedings-library-archive
Please enter your name
Please enter a valid email address
Who would you like to send this to? *


Full text views

Total number of HTML views: 0
Total number of PDF views: 1 *
Loading metrics...

Abstract views

Total abstract views: 28 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 28th June 2017. This data will be updated every 24 hours.