1.
Martins, Bruno VC; Brunetto, Gustavo; Sato, Fernando; Coluci, Vitor R; Galvao, Douglas S
Designing conducting polymers using bioinspired ant algorithms Journal Article
In: Chemical Physics Letters, vol. 453, no. 4, pp. 290–295, 2008.
@article{martins2008designing,
title = {Designing conducting polymers using bioinspired ant algorithms},
author = {Martins, Bruno VC and Brunetto, Gustavo and Sato, Fernando and Coluci, Vitor R and Galvao, Douglas S},
url = {http://www.sciencedirect.com/science/article/pii/S0009261408000845},
year = {2008},
date = {2008-01-01},
journal = {Chemical Physics Letters},
volume = {453},
number = {4},
pages = {290--295},
publisher = {Elsevier},
abstract = {Ant algorithms are inspired in real ants and the main idea is to create virtual ants that travel into the space of possible solutions depositing virtual pheromone proportional to how good a specific solution is. This creates an autocatalytic (positive feedback) process that can be used to generate automatic solutions to very difficult problems. In the present work we show that these algorithms can be used coupled to tight-binding Hamiltonians to design conducting polymers with pre-specified properties. The methodology is completely general and can be used for a large number of optimizations problems in materials science.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ant algorithms are inspired in real ants and the main idea is to create virtual ants that travel into the space of possible solutions depositing virtual pheromone proportional to how good a specific solution is. This creates an autocatalytic (positive feedback) process that can be used to generate automatic solutions to very difficult problems. In the present work we show that these algorithms can be used coupled to tight-binding Hamiltonians to design conducting polymers with pre-specified properties. The methodology is completely general and can be used for a large number of optimizations problems in materials science.
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.
@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
2008
2.
Martins, Bruno VC; Brunetto, Gustavo; Sato, Fernando; Coluci, Vitor R; Galvao, Douglas S
Designing conducting polymers using bioinspired ant algorithms Journal Article
In: Chemical Physics Letters, vol. 453, no. 4, pp. 290–295, 2008.
Abstract | Links | BibTeX | Tags: ANTS algorithms, Artificial Intelligence, Conducting Polymer, Design of Materials
@article{martins2008designing,
title = {Designing conducting polymers using bioinspired ant algorithms},
author = {Martins, Bruno VC and Brunetto, Gustavo and Sato, Fernando and Coluci, Vitor R and Galvao, Douglas S},
url = {http://www.sciencedirect.com/science/article/pii/S0009261408000845},
year = {2008},
date = {2008-01-01},
journal = {Chemical Physics Letters},
volume = {453},
number = {4},
pages = {290--295},
publisher = {Elsevier},
abstract = {Ant algorithms are inspired in real ants and the main idea is to create virtual ants that travel into the space of possible solutions depositing virtual pheromone proportional to how good a specific solution is. This creates an autocatalytic (positive feedback) process that can be used to generate automatic solutions to very difficult problems. In the present work we show that these algorithms can be used coupled to tight-binding Hamiltonians to design conducting polymers with pre-specified properties. The methodology is completely general and can be used for a large number of optimizations problems in materials science.},
keywords = {ANTS algorithms, Artificial Intelligence, Conducting Polymer, Design of Materials},
pubstate = {published},
tppubtype = {article}
}
Ant algorithms are inspired in real ants and the main idea is to create virtual ants that travel into the space of possible solutions depositing virtual pheromone proportional to how good a specific solution is. This creates an autocatalytic (positive feedback) process that can be used to generate automatic solutions to very difficult problems. In the present work we show that these algorithms can be used coupled to tight-binding Hamiltonians to design conducting polymers with pre-specified properties. The methodology is completely general and can be used for a large number of optimizations problems in materials science.
2003
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.
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
http://scholar.google.com/citations?hl=en&user=95SvbM8AAAAJ
