Structural optimization of PNIPAM-derived thermoresponsive polymers: a computational approach employing artificial neural networks and genetic algorithms

In this study, artificial neural networks (ANNs) and genetic algorithms (GAs) are employed together to design optimized polymeric structures with superior cloud points. The database from a previous study of polymer synthesis with thermoresponsive polymers was used to create ANN-based models, which enabled the formulation and solution of the inverse problem using the GA. The regressors, with an average RMSE of less than 0.7 ºC, were used in the polymer evolution process over 20 generations. Mutation and selection operations led to the creation of 10 novel hybrid macromolecules with an average cloud point of 80 ºC. Furthermore, the special roles of some chemical groups are recognized and favor the structural mapping of PNIPAM-based materials. The computational approach presented here demonstrates that it is a promising tool in the development of new materials.

Authors

Institutions

Publication Details

Journal
Ciência e Natura
Published
2024-11-04
DOI
https://doi.org/10.5902/2179460x87076
Primary Topic
Polymer composites and self-healing
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Structural optimization of PNIPAM-derived thermoresponsive polymers: a computational approach employing artificial neural networks and genetic algorithms

Antônio José da Silva Neto, Thomas Hille, Kelly Cristine da Silveira, Josiele da Silva Teixeira et al.
Ciência e Natura
Polymer composites and self-healing
article

Structural optimization of PNIPAM-derived thermoresponsive polymers: a computational approach employing artificial neural networks and genetic algorithms

Antônio José da Silva Neto, Thomas Hille, Kelly Cristine da Silveira, Josiele da Silva Teixeira, Jonathan N. Gois, Guilherme Leite, Matheus Moraes Gago
article en

Abstract

In this study, artificial neural networks (ANNs) and genetic algorithms (GAs) are employed together to design optimized polymeric structures with superior cloud points. The database from a previous study of polymer synthesis with thermoresponsive polymers was used to create ANN-based models, which enabled the formulation and solution of the inverse problem using the GA. The regressors, with an average RMSE of less than 0.7 ºC, were used in the polymer evolution process over 20 generations. Mutation and selection operations led to the creation of 10 novel hybrid macromolecules with an average cloud point of 80 ºC. Furthermore, the special roles of some chemical groups are recognized and favor the structural mapping of PNIPAM-based materials. The computational approach presented here demonstrates that it is a promising tool in the development of new materials.

Ciência e NaturaVol. 46(esp. 1)
Federal Center for Technological Education Celso Suckow da Fonseca (BR), Universidade do Estado do Rio de Janeiro (BR), Instituto Politécnico do Rio de Janeiro, Instituto Politécnico Nacional (MX)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Openalex Percentile: Top 85%
Polymer composites and self-healing
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.