Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam composites
Abstract Graphene foam (GF), synthesized via Chemical Vapor Deposition (CVD), has been proven to be the ideal bulk porous material. The addition of poly(dimethylsiloxane) (PDMS) within the porous structure enables enhancement of mechanical strength and alteration of heat transfer behavior. This stud...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01710-6 |
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| author | Pingyang Zhang Shaodong Zhang Yihan Qin Tingting Du Lei Wei Xiangyu Li |
| author_facet | Pingyang Zhang Shaodong Zhang Yihan Qin Tingting Du Lei Wei Xiangyu Li |
| author_sort | Pingyang Zhang |
| collection | DOAJ |
| description | Abstract Graphene foam (GF), synthesized via Chemical Vapor Deposition (CVD), has been proven to be the ideal bulk porous material. The addition of poly(dimethylsiloxane) (PDMS) within the porous structure enables enhancement of mechanical strength and alteration of heat transfer behavior. This study focuses on the thermodynamic behavior of GF/PDMS composites during deformation, and employs stochastic modeling and neuroevolution potential (NEP) for complex material modeling with precise prediction of microscopic mechanisms governing thermal property variations. The results demonstrate that the composite with a 5% doping rate of PDMS achieves the optimal mechanical performance and shows a 7.13-fold modulation in thermal resistance during the deformation from 40% stretching to 50% compression. Findings indicate PDMS fortifies structural stability while enabling dynamic thermal conductivity modulation in GF. This research provides critical insights into the micro-mechanisms of GF/PDMS composites and offers a theoretical foundation for applications in dynamic thermal management and self-powered sensor networks. |
| format | Article |
| id | doaj-art-cafef2dda08e4770ba2a6c3a29df1d41 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-cafef2dda08e4770ba2a6c3a29df1d412025-08-20T03:37:38ZengNature Portfolionpj Computational Materials2057-39602025-07-0111111110.1038/s41524-025-01710-6Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam compositesPingyang Zhang0Shaodong Zhang1Yihan Qin2Tingting Du3Lei Wei4Xiangyu Li5School of Energy and Power Engineering, Shandong UniversityMechanical Aerospace and Biomedical Engineering, University of Tennessee KnoxvilleSchool of Energy and Power Engineering, Shandong UniversitySchool of Energy and Power Engineering, Shandong UniversityAdvanced Materials Institute, Qilu University of Technology (Shandong Academy of Sciences)Mechanical Aerospace and Biomedical Engineering, University of Tennessee KnoxvilleAbstract Graphene foam (GF), synthesized via Chemical Vapor Deposition (CVD), has been proven to be the ideal bulk porous material. The addition of poly(dimethylsiloxane) (PDMS) within the porous structure enables enhancement of mechanical strength and alteration of heat transfer behavior. This study focuses on the thermodynamic behavior of GF/PDMS composites during deformation, and employs stochastic modeling and neuroevolution potential (NEP) for complex material modeling with precise prediction of microscopic mechanisms governing thermal property variations. The results demonstrate that the composite with a 5% doping rate of PDMS achieves the optimal mechanical performance and shows a 7.13-fold modulation in thermal resistance during the deformation from 40% stretching to 50% compression. Findings indicate PDMS fortifies structural stability while enabling dynamic thermal conductivity modulation in GF. This research provides critical insights into the micro-mechanisms of GF/PDMS composites and offers a theoretical foundation for applications in dynamic thermal management and self-powered sensor networks.https://doi.org/10.1038/s41524-025-01710-6 |
| spellingShingle | Pingyang Zhang Shaodong Zhang Yihan Qin Tingting Du Lei Wei Xiangyu Li Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam composites npj Computational Materials |
| title | Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam composites |
| title_full | Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam composites |
| title_fullStr | Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam composites |
| title_full_unstemmed | Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam composites |
| title_short | Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam composites |
| title_sort | machine learning driven molecular dynamics decodes thermal tuning in graphene foam composites |
| url | https://doi.org/10.1038/s41524-025-01710-6 |
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