About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Chemistry and Physics of Interfaces
|
| Presentation Title |
Data-Driven Materials Design: Advances in Thin Film Grain Growth Studies |
| Author(s) |
Katayun Barmak |
| On-Site Speaker (Planned) |
Katayun Barmak |
| Abstract Scope |
A grand challenge problem in engineering of polycrystals is to develop prescriptive process technologies capable of producing an arrangement of grains that provides for a desired set of materials properties. One method by which the grain structure is engineered is through grain growth. Our ability to image grain growth in the TEM in real time has been revolutionized by advances in low thermal-mass holders and in-situ heating capabilities. Additionally, grain-boundary crystallography and character distribution (GBCD) of nanocrystalline films can now be obtained using precession electron diffraction (PED) 4D-STEM. Thin films therefore offer a unique platform for accessing both direct space (imaging) and reciprocal space (orientation mapping) at high spatial/temporal resolution. This talk will summarize how automated boundary detection of bright-field TEM images using machine learning (ML) followed by analysis of the imaging and mapping data provide unprecedented ability to deepen our understanding of thin film grain structures and their evolution. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Machine Learning, Thin Films and Interfaces |