Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning
Abstract
Abstract Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1914356
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Published Article
- Journal Name:
- Microscopy and Microanalysis
- Additional Journal Information:
- Journal Name: Microscopy and Microanalysis Journal Volume: 28 Journal Issue: 6; Journal ID: ISSN 1431-9276
- Publisher:
- Oxford University Press
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Bruefach, Alexandra, Ophus, Colin, and Scott, Mary C. Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning. United States: N. p., 2022.
Web. doi:10.1017/S1431927622012259.
Bruefach, Alexandra, Ophus, Colin, & Scott, Mary C. Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning. United States. https://doi.org/10.1017/S1431927622012259
Bruefach, Alexandra, Ophus, Colin, and Scott, Mary C. Thu .
"Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning". United States. https://doi.org/10.1017/S1431927622012259.
@article{osti_1914356,
title = {Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning},
author = {Bruefach, Alexandra and Ophus, Colin and Scott, Mary C.},
abstractNote = {Abstract Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.},
doi = {10.1017/S1431927622012259},
journal = {Microscopy and Microanalysis},
number = 6,
volume = 28,
place = {United States},
year = {Thu Dec 01 00:00:00 EST 2022},
month = {Thu Dec 01 00:00:00 EST 2022}
}
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