Designing conducting polymers using genetic algorithms

Giro, Ronaldo; Cyrillo, Márcio; Galvão, Douglas S.

doi:10.1016/s0009-2614(02)01547-6

Cited by 28 publications

(17 citation statements)

References 17 publications

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“…We will utilize our insights into the underlying structure-property relationships in the creation of second generation screening libraries and as a starting point for the construction of new candidates via genetic algorithms. [48][49][50] In addition to expanding and improving our candidate characterization and data analysis capability, we will also employ other OPV performance models in order to advance the quality and robustness of our predictions. Finally, we will generalize our work to materials for multi-junction devices.…”

Section: Discussionmentioning

confidence: 99%

Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project

Hachmann

Olivares‐Amaya

Jinich

et al. 2014

Energy Environ. Sci.

239

209

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The virtual high-throughput screening framework of the Harvard Clean Energy Project allows for the computational assessment of candidate structures for organic electronic materials -in particular photovoltaics -at an unprecedented scale. We report the most promising compounds that have emerged after studying 2.3 million molecular motifs by means of 150 million density functional theory calculations. Our top candidates are analyzed with respect to their structural makeup in order to identify important building blocks and extract design rules for efficient materials. An online database of the results is made available to the community.

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Section: Discussionmentioning

confidence: 99%

Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project

Hachmann

Olivares‐Amaya

Jinich

et al. 2014

Energy Environ. Sci.

239

209

View full text Add to dashboard Cite

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“…Recently, many groups have attempt to use artificial intelligence or automatic methods to design new materials [1,2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Designing conducting polymers using bioinspired ant algorithms

Martins

Brunetto

Sato

et al. 2008

Chemical Physics Letters

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Ant algorithms are inspired in real ants and the main idea is to create virtual ants that travel into the space of possible solution depositing virtual pheromone proportional to how good a specific solution is. This creates a 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 optimization problems in materials science.

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“…This makes the systematic search for new structures almost impossible, and trial and error approach has been the rule. In this work we discuss a methodology 3,4 capable of generating automatic solutions for ordered and disordered polymeric alloys with pre-specified properties. It combines the use of negative factor counting technique (NFC) 5,6 , with genetic algorithms (GAs) 7 .…”

Section: Introductionmentioning

confidence: 99%

“…GAs follow these ideas in a very simple way and allow us to use the computer to evolve automatic solutions over time. This methodology was originally developed by us to study polyanilines 3,4 and it was the first time that the NFC technique coupled with artificial intelligence methods (Gas) was used in materials science. To our knowledge no other approach using electronic parameters and combinatorial/artificial intelligence methods has been applied to conducting polymers.…”

Section: Introductionmentioning

confidence: 99%

Using artificial intelligence methods to design new conducting polymers

2003

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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.

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Designing conducting polymers using genetic algorithms

Cited by 28 publications

References 17 publications

Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project

Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project

Designing conducting polymers using bioinspired ant algorithms

Using artificial intelligence methods to design new conducting polymers

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