| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Methods, Techniques, and Materials Discovery of Irradiation Effect Using In-situ Microscopy
|
| Presentation Title |
Uncovering Transient Grain Boundary Absorption States Using Deep Learning Object Detection |
| Author(s) |
Emily H. Mang, Sicong He, Ryan Jacobs, Priyam Patki, Chang-Yu Hung, James Nathaniel, Dane Morgan, Kevin Field, Jaime Marian, Mitra Taheri |
| On-Site Speaker (Planned) |
Emily H. Mang |
| Abstract Scope |
Achieving radiation tolerance in crystalline materials will require a thorough understanding of defect evolution and corresponding material responses to ion bombardment. Tailoring grain boundaries to behave as enhanced defect sinks poses a potential solution toward the development of more radiation tolerant materials; however, critical nuances illustrating the microstructural response of grain boundaries under irradiation have yet to be explained. In particular, the relationship between GB structural states and their effect on the rate of defect absorption is unclear. In this study, we utilize automated object detection models of in situ TEM experiments to offer new insight into transient GB states and analyze cyclic recovery using rate theory models. By providing an indicator of changes in GB absorption mechanisms, we move closer to explaining GB metastability with the onset of radiation damage. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Nuclear Materials, Machine Learning |