About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Capturing Nanoscale Lattice Variations by Applying AI-powered Computer Vision Techniques on Synthetic X-ray Diffraction Data |
Author(s) |
Niaz Abdolrahim |
On-Site Speaker (Planned) |
Niaz Abdolrahim |
Abstract Scope |
Recent advancements in X-ray scattering techniques allow for real-time probe of physical structure of materials at the molecular and nanoscales. However, real-time x-ray crystallography generates very large data that almost makes it impossible to store and analyze manually by experts. This research combines molecular dynamics (MD) simulations with AI-powered computer vision techniques to mine information-rich x-ray diffraction data to filter and detect lattice-level mechanisms that are responsible for phase transformation and other plastic deformation processes. Large-scale MD simulations will be performed on materials that undergo various physical phenomena (such as nucleation of defects, phase transformation, slip and twinning, etc.) and generate large datasets. AI-powered computer vision techniques will then be developed to automatically learn image features and identify lattice-level mechanisms from synthetic data. The proposed framework will be significantly useful in capturing and analyzing new and unknown phenomena in materials under extreme conditions when no prior knowledge is available. |
Proceedings Inclusion? |
Planned: |