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Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced pe...

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Autores principales: Champa-Bujaico, Elizabeth, García-Díaz, Pilar, Díez-Pascual, Ana M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505448/
https://www.ncbi.nlm.nih.gov/pubmed/36142623
http://dx.doi.org/10.3390/ijms231810712
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author Champa-Bujaico, Elizabeth
García-Díaz, Pilar
Díez-Pascual, Ana M.
author_facet Champa-Bujaico, Elizabeth
García-Díaz, Pilar
Díez-Pascual, Ana M.
author_sort Champa-Bujaico, Elizabeth
collection PubMed
description Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system’s properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted.
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spelling pubmed-95054482022-09-24 Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art Champa-Bujaico, Elizabeth García-Díaz, Pilar Díez-Pascual, Ana M. Int J Mol Sci Review Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system’s properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted. MDPI 2022-09-14 /pmc/articles/PMC9505448/ /pubmed/36142623 http://dx.doi.org/10.3390/ijms231810712 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Champa-Bujaico, Elizabeth
García-Díaz, Pilar
Díez-Pascual, Ana M.
Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art
title Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art
title_full Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art
title_fullStr Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art
title_full_unstemmed Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art
title_short Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art
title_sort machine learning for property prediction and optimization of polymeric nanocomposites: a state-of-the-art
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9505448/
https://www.ncbi.nlm.nih.gov/pubmed/36142623
http://dx.doi.org/10.3390/ijms231810712
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