Using artificial intelligence methods to design new conducting polymers

Abstract

In the last years the possibility of creating new conducting polymers exploring the concept of copolymn. (different structural monomeric units) has attracted much attention from exptl. and theor. points of view. Due to the rich C reactivity an almost infinite no. of new structures is possible and the procedure of trial and error was the rule. The computational methodol. combines the use of neg. factor counting (NFC) technique with artificial intelligence methods (genetic algorithms, GAs). The authors present the results for a case study for poly(phenylenesulfide phenyleneamine) (PPSA), a copolymer formed by combination of homopolymers: polyaniline (PANI) and polyphenylenesulfide (PPS). The methodol. was successfully applied to the problem of obtaining binary up to quinternary disordered polymeric alloys with a pre-specific gap value or exhibiting metallic properties. It is completely general and can be in principle adapted to the design of new classes of materials with pre-specified properties. on SciFinder (R)