| Autor: |
PRATHAMESH P. DESHPANDE, KAREN J. DEMILLE, AOWABIN RAHMAN, SUSANTA GHOSH, ASHLEY D. SPEAR, GREGORY M. ODEGARD |
| Rok vydání: |
2022 |
| Zdroj: |
American Society for Composites 2022. |
| DOI: |
10.12783/asc37/36458 |
| Popis: |
The matrix-reinforcement interface has been studied extensively to enhance the performance of polymer matrix composites (PMCs). One commonly practiced approach is functionalization of the reinforcement, which significantly improves the interfacial interaction. A molecular dynamics (MD) and machine learning (ML) workflow is proposed to identify the optimal functionalization parameters that result in improved mechanical performance of a 3-layer graphene nanoplatelet (GNP)/ bismaleimide (BMI) nanocomposite. MD is used to generate the training set for a graph convolutional neural network (GCN). This article reports the MD methodology and an example mechanical response from a pull-out simulation. Upcoming work in the proposed MD-ML workflow for designing a nanocomposite with improved mechanical performance is also discussed. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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