About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Synthetic Data Development towards Automated Defect Detection of Irradiated Materials |
Author(s) |
Matthew Lynch, Priyam Patki, Ryan Jacobs, Steven Chen, Gabriella Bruno, Dane Morgan, Kevin Field |
On-Site Speaker (Planned) |
Matthew Lynch |
Abstract Scope |
Machine Learning (ML) vision algorithms have attracted significant attention recently in enabling rapid labeling and analysis of data from transmission electron microscopy (TEM) experiments. However, recent efforts have exclusively focused on costly human-based labeling processes for ML training data sets. Here we demonstrate an effective synthetic ML database generation process using simplified physics-based phase contrast simulations. Individual cavity simulations are integrated into unirradiated microscopy images to produce a hybrid synthetic image. This novel process enables nearly instant and unlimited generation of automatically labeled training data, devoid of human bias. ML models trained on this data can perform similarly to those trained on traditional data, and there is a possible synergistic effect when combining datasets. This process can be applied to various defect types and used to simulate rare defect structures, generating data that is challenging to experimentally obtain. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Characterization, Nuclear Materials |