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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/23/18/10712 |
| _version_ | 1797487185211949056 |
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| author | Elizabeth Champa-Bujaico Pilar García-Díaz Ana M. Díez-Pascual |
| author_facet | Elizabeth Champa-Bujaico Pilar García-Díaz Ana M. Díez-Pascual |
| author_sort | Elizabeth Champa-Bujaico |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T23:44:00Z |
| format | Article |
| id | doaj.art-9c1ecf868d1b4c92b17102dc85727a9e |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T23:44:00Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-9c1ecf868d1b4c92b17102dc85727a9e2023-11-23T16:46:42ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-09-0123181071210.3390/ijms231810712Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-ArtElizabeth Champa-Bujaico0Pilar García-Díaz1Ana M. Díez-Pascual2Universidad de Alcalá, Departamento de Teoría de la Señal y Comunicaciones, Ctra. Madrid-Barcelona Km. 33.6, 28805 Alcalá de Henares, Madrid, SpainUniversidad de Alcalá, Departamento de Teoría de la Señal y Comunicaciones, Ctra. Madrid-Barcelona Km. 33.6, 28805 Alcalá de Henares, Madrid, SpainUniversidad de Alcalá, Facultad de Ciencias, Departamento de Química Analítica, Química Física e Ingeniería Química, Ctra. Madrid-Barcelona Km. 33.6, 28805 Alcalá de Henares, Madrid, SpainRecently, 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.https://www.mdpi.com/1422-0067/23/18/10712machine learningartificial neural networkcarbon nanomaterialspolymer nanocompositesproperty predictionoptimization |
| spellingShingle | Elizabeth Champa-Bujaico Pilar García-Díaz Ana M. Díez-Pascual Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art International Journal of Molecular Sciences machine learning artificial neural network carbon nanomaterials polymer nanocomposites property prediction optimization |
| 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 | machine learning artificial neural network carbon nanomaterials polymer nanocomposites property prediction optimization |
| url | https://www.mdpi.com/1422-0067/23/18/10712 |
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