Unsupervised learning of ferroic variants from atomically resolved STEM images
An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optima...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2022-10-01
|
| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0105406 |