About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
Accelerated Discovery and Qualification of Nuclear Materials for Energy Applications
|
Presentation Title |
Machine Learning for Accelerating Property Prediction and Materials Characterization in Irradiated Materials |
Author(s) |
Dane Morgan, Mingren Shen, Ryan Jacobs, G. Robert Odette, Kevin Field |
On-Site Speaker (Planned) |
Dane Morgan |
Abstract Scope |
Machine learning methods have the potential to greatly accelerate nuclear materials development through predicting properties and automating analysis. In this talk, I will discuss our recent efforts to predict hardening in reactor pressure vessel steels, focusing on the challenges of extrapolation to irradiation conditions outside those in the training data. I will also discuss our recent efforts to automate deep learning object detection approaches to find the location and geometry of different defect types in electron microscopy images of irradiated steels. We show that an accuracy comparable to human analysis can be achieved with relatively small training data sets, suggesting a future where defect analysis is more standardized and orders of magnitude faster than today. |
Proceedings Inclusion? |
Planned: |