About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Discovery and Design of Emerging Materials
|
Presentation Title |
Tuning Mechanical Behavior of Graphene: From Microscopic Defect Modeling to Macroscopic Property Prediction |
Author(s) |
Bowen Zheng, Grace Gu |
On-Site Speaker (Planned) |
Bowen Zheng |
Abstract Scope |
Understanding defect behavior in graphene enables on-demand manipulation of mechanical properties for various applications. In this study, microscopic defect behaviors and their influence on the macroscopic mechanical properties of graphene are investigated via molecular dynamics simulation. Fundamental insights on the mechanics of defects are provided by analyzing stress fields, which enables the identification and prediction of defect interaction and structural failure. Bridging detailed defect behaviors to macroscopic mechanical properties, the proposed multiscale paradigm is capable of investigating two novel attributes of defective graphene: tuning mechanical anisotropy and recovering from defect-induced mechanical degradation. Finally, a machine-learning approach is applied not only to create optimized defect patterns that lead to superior mechanical properties, but also to predict fracture behaviors such as initiation location and propagation pattern. This work may open up new possibilities to enhance the performance of graphene-based applications such as stretchable electronics and supercapacitor devices. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |