1.
Giro, Ronaldo; Cyrillo, Marcio; Galvao, Douglas Soares
Using artificial intelligence methods to design new conducting polymers Journal Article
In: Materials Research, vol. 6, no. 4, pp. 523–528, 2003.
@article{giro2003using,
title = {Using artificial intelligence methods to design new conducting polymers},
author = {Giro, Ronaldo and Cyrillo, Marcio and Galvao, Douglas Soares},
url = {http://www.scielo.br/scielo.php?pid=S1516-14392003000400017&script=sci_arttext},
year = {2003},
date = {2003-01-01},
journal = {Materials Research},
volume = {6},
number = {4},
pages = {523--528},
publisher = {SciELO Brasil},
abstract = {In the last years the possibility of creating new conducting polymers exploring the concept of copolymerization (different structural monomeric units) has attracted much attention from experimental and theoretical points of view. Due to the rich carbon reactivity an almost infinite number of new structures is possible and the procedure of trial and error has been the rule. In this work we have used a methodology able of generating new structures with pre-specified properties. It combines the use of negative factor counting (NFC) technique with artificial intelligence methods (genetic algorithms - GAs). We 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 methodology 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},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In the last years the possibility of creating new conducting polymers exploring the concept of copolymerization (different structural monomeric units) has attracted much attention from experimental and theoretical points of view. Due to the rich carbon reactivity an almost infinite number of new structures is possible and the procedure of trial and error has been the rule. In this work we have used a methodology able of generating new structures with pre-specified properties. It combines the use of negative factor counting (NFC) technique with artificial intelligence methods (genetic algorithms - GAs). We 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 methodology 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
2.
Giro, R; Cyrillo, M; Galvao, DS
Designing conducting polymers using genetic algorithms Journal Article
In: Chemical Physics Letters, vol. 366, no. 1, pp. 170–175, 2002.
@article{giro2002designing,
title = {Designing conducting polymers using genetic algorithms},
author = {Giro, R and Cyrillo, M and Galvao, DS},
url = {http://www.sciencedirect.com/science/article/pii/S0009261402015476},
year = {2002},
date = {2002-01-01},
journal = {Chemical Physics Letters},
volume = {366},
number = {1},
pages = {170--175},
publisher = {North-Holland},
abstract = {We have developed a new methodology to design conducting polymers with pre-specified properties. The methodology is based on the use of genetic algorithms (GAs) coupled to Negative Factor Counting technique. 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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We have developed a new methodology to design conducting polymers with pre-specified properties. The methodology is based on the use of genetic algorithms (GAs) coupled to Negative Factor Counting technique. 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.
2003
2.
Giro, Ronaldo; Cyrillo, Marcio; Galvao, Douglas Soares
Using artificial intelligence methods to design new conducting polymers Journal Article
In: Materials Research, vol. 6, no. 4, pp. 523–528, 2003.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Conducting Polymers, Genetic Algorithms, Material Design
@article{giro2003using,
title = {Using artificial intelligence methods to design new conducting polymers},
author = {Giro, Ronaldo and Cyrillo, Marcio and Galvao, Douglas Soares},
url = {http://www.scielo.br/scielo.php?pid=S1516-14392003000400017&script=sci_arttext},
year = {2003},
date = {2003-01-01},
journal = {Materials Research},
volume = {6},
number = {4},
pages = {523--528},
publisher = {SciELO Brasil},
abstract = {In the last years the possibility of creating new conducting polymers exploring the concept of copolymerization (different structural monomeric units) has attracted much attention from experimental and theoretical points of view. Due to the rich carbon reactivity an almost infinite number of new structures is possible and the procedure of trial and error has been the rule. In this work we have used a methodology able of generating new structures with pre-specified properties. It combines the use of negative factor counting (NFC) technique with artificial intelligence methods (genetic algorithms - GAs). We 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 methodology 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},
keywords = {Artificial Intelligence, Conducting Polymers, Genetic Algorithms, Material Design},
pubstate = {published},
tppubtype = {article}
}
In the last years the possibility of creating new conducting polymers exploring the concept of copolymerization (different structural monomeric units) has attracted much attention from experimental and theoretical points of view. Due to the rich carbon reactivity an almost infinite number of new structures is possible and the procedure of trial and error has been the rule. In this work we have used a methodology able of generating new structures with pre-specified properties. It combines the use of negative factor counting (NFC) technique with artificial intelligence methods (genetic algorithms - GAs). We 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 methodology 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
2002
1.
Giro, R; Cyrillo, M; Galvao, DS
Designing conducting polymers using genetic algorithms Journal Article
In: Chemical Physics Letters, vol. 366, no. 1, pp. 170–175, 2002.
Abstract | Links | BibTeX | Tags: Conducting Polymers, Electronic Structure, Genetic Algorithms, Material Design
@article{giro2002designing,
title = {Designing conducting polymers using genetic algorithms},
author = {Giro, R and Cyrillo, M and Galvao, DS},
url = {http://www.sciencedirect.com/science/article/pii/S0009261402015476},
year = {2002},
date = {2002-01-01},
journal = {Chemical Physics Letters},
volume = {366},
number = {1},
pages = {170--175},
publisher = {North-Holland},
abstract = {We have developed a new methodology to design conducting polymers with pre-specified properties. The methodology is based on the use of genetic algorithms (GAs) coupled to Negative Factor Counting technique. 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.},
keywords = {Conducting Polymers, Electronic Structure, Genetic Algorithms, Material Design},
pubstate = {published},
tppubtype = {article}
}
We have developed a new methodology to design conducting polymers with pre-specified properties. The methodology is based on the use of genetic algorithms (GAs) coupled to Negative Factor Counting technique. 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.
http://scholar.google.com/citations?hl=en&user=95SvbM8AAAAJ
