Variational Convolutional Autoencoders for Anomaly Detection in Scanning Transmission Electron Microscopy
Abstract: Identifying point defects and other structural anomalies using scanning transmission electron microscopy (STEM) is important to understand a material's properties caused by the disruption of the regular pattern of crystal lattice. Due to improvements in instrumentation stability and electron optics, atomic‐resolution images with a field of view of several hundred nanometers can now be routinely acquired at 1–10 Hz frame rates and such data, which often contain thousands of atomic columns, need to be analyzed. To date, image analysis is performed largely manually, but recent developments in computer vision (CV) and machine learning (ML) now enable automated analysis of atomic structures and associated defects. Here, the authors report on how a Convolutional Variational Autoencoder (CVAE) can be utilized to detect structural anomalies in atomic‐resolution STEM images. Specifically, the training set is limited to perfect crystal images, and the performance of a CVAE in differentiating between single‐crystal bulk data or point defects is demonstrated. It is found that the CVAE can reproduce the perfect crystal data but not the defect input data. The disagreesments between the CVAE‐predicted data for defects allows for a clear and automatic distinction and differentiation of several point defect types.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Variational Convolutional Autoencoders for Anomaly Detection in Scanning Transmission Electron Microscopy ; day:18 ; month:01 ; year:2023 ; extent:12
Small ; (18.01.2023) (gesamt 12)
- Creator
-
Prifti, Enea
Buban, James P.
Thind, Arashdeep Singh
Klie, Robert F.
- DOI
-
10.1002/smll.202205977
- URN
-
urn:nbn:de:101:1-2023011814381438427645
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:35 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Prifti, Enea
- Buban, James P.
- Thind, Arashdeep Singh
- Klie, Robert F.
Other Objects (12)
Anomaly detection by using a combination of generative adversarial networks and convolutional autoencoders
Analytical Description of Variational Autoencoders and Application of Temporal Variational Autoencoders to Financial Risk Management
Modelling urban networks using Variational Autoencoders
Variational autoencoders for 3D data processing
Learning variational autoencoders via MCMC speed measures
Learning hard quantum distributions with variational autoencoders
Generating Ultrasonic Foliage Echoes with Variational Autoencoders
Adversarial and variational autoencoders improve metagenomic binning
Variational autoencoders learn transferrable representations of metabolomics data
Modelling forest fire dynamics using conditional variational autoencoders
Autoencoders for unsupervised anomaly detection in high energy physics
Autoencoders for unsupervised anomaly detection in high energy physics
Anomaly detection by using a combination of generative adversarial networks and convolutional autoencoders
Analytical Description of Variational Autoencoders and Application of Temporal Variational Autoencoders to Financial Risk Management
Modelling urban networks using Variational Autoencoders
Variational autoencoders for 3D data processing
Learning variational autoencoders via MCMC speed measures
Learning hard quantum distributions with variational autoencoders
Generating Ultrasonic Foliage Echoes with Variational Autoencoders
Adversarial and variational autoencoders improve metagenomic binning
Variational autoencoders learn transferrable representations of metabolomics data
Modelling forest fire dynamics using conditional variational autoencoders
Autoencoders for unsupervised anomaly detection in high energy physics
Autoencoders for unsupervised anomaly detection in high energy physics
Anomaly detection by using a combination of generative adversarial networks and convolutional autoencoders
Analytical Description of Variational Autoencoders and Application of Temporal Variational Autoencoders to Financial Risk Management
Modelling urban networks using Variational Autoencoders
Variational autoencoders for 3D data processing
Learning variational autoencoders via MCMC speed measures
Learning hard quantum distributions with variational autoencoders
Generating Ultrasonic Foliage Echoes with Variational Autoencoders
Adversarial and variational autoencoders improve metagenomic binning
Variational autoencoders learn transferrable representations of metabolomics data
Modelling forest fire dynamics using conditional variational autoencoders
Autoencoders for unsupervised anomaly detection in high energy physics