Document Type
Article
Publication Date
2-1-2024
Abstract
Selective area electron diffraction (SAED) patterns can provide valuable insight into the structure of a material. However, the manual identification of collected patterns can be a significant bottleneck in the overall phase classification workflow. In this work, we utilize the recent advances in computer vision and machine learning (ML) to automate the indexing of SAED patterns. The performance of six different ML algorithms is demonstrated using metallic plutonium-zirconium alloys. The most successful approach trained a neural network (NN) to make a classification of the phase and zone axis, and then utilized a second NN to synthesize multiple independent predictions of different tilts in a single sample to make an overall phase identification. The results demonstrate that automated SAED phase identification using ML is a viable route to accelerate materials characterization.
Keywords
selective area electron diffraction, machine learning, phase identification, metallic fuels, Pu alloys
Language
English
Publication Title
Journal of Materiomics
Rights
© The Author(s). This is an open access work under the CC BY-NC-ND license (https://creativecommons.org/licenses/BY-NC-ND/4.0/), which permits non-commercial copying and redistribution of the material in any medium or format, provided the original work is not changed in any way and is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Mika M, Tomczak N, Finney C, et al. Automating selective area electron diffraction phase identification using machine learning. Journal of Materiomics, 2024, 10(4): 896-905. https://doi.org/10.1016/j.jmat.2023.12.010
Manuscript Version
Final Publisher Version