Deep learning for visualization and novelty detection in large X-ray diffraction datasets
Deep learning for visualization and novelty detection in large X-ray diffraction datasets
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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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. While other artificial intelligence (AI) agents are effecti...
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Deep learning for visualization and novelty detection in large X-ray diffraction datasets
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TN_cdi_proquest_journals_2511606718
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2511606718
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2331-8422
