Deep learning for visualization and novelty detection in large X-ray diffraction datasets
Abstract We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-021-00575-9 |