About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
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Novel Strategies for Rapid Acquisition and Processing of Large Datasets From Advanced Characterization Techniques
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| Presentation Title |
Nanocrystalline Films: Imaging, Orientation Mapping, Machine Learning and Data Analytics |
| Author(s) |
Katayun Barmak, Jeffrey Rickman |
| On-Site Speaker (Planned) |
Katayun Barmak |
| Abstract Scope |
The interrogation of nanometer-scale thin-film microstructures requires the use of scanning/transmission electron microscopy (S/TEM). Advances in low thermal mass holders, MEMS chips for film deposition onto electron transparent membranes, and in-situ heating capabilities combined with drift correction have revolutionized our ability to image grain growth in real time. 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 innovations in 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 grain growth. In this context, ML is employed to reconstruct grain-boundary networks, quantify the rate of microstructural learning and inform coarsening models with experimental data. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Thin Films and Interfaces, Machine Learning |