About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-Dimensional Datasets
|
| 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 nanometer scale of grain structure of thin films requires the use of scanning/transmission electron microscopy (TEM/STEM). Advances in low thermal mass TEM holders, microelectromechanical systems (MEMS) chips for direct deposition of films onto electron transparent membranes, and in-situ heating capabilities combined with computer vision-based drift correction have revolutionized our ability to image grain growth in real time. Grain boundary crystallography and the grain boundary 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. In this talk, I will summarize how innovations in automated boundary detection of bright-field TEM images using machine learning (ML) followed by an analysis of the resulting imaging and mapping data provide an unprecedented ability to deepen our understanding of grain growth and to advance materials innovations. |