Deep learning for visualization and novelty detection in large X-ray diffraction datasets

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 oth...

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Bibliographic Details
Published in:arXiv.org 2021-04
Main Authors: Banko, Lars, Maffettone, Phillip M, Naujoks, Dennis, Olds, Daniel, Ludwig, Alfred
Format: Article
Language:English
Subjects:
Agents (artificial intelligence)
Artificial intelligence
Crystal structure
Crystals
Data analysis
Deep learning
Diffraction patterns
Thin films
X-ray diffraction
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Summary: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 effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it does not know, rapidly identifying novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for materials discovery and understanding XRD measurements both on-the-fly and during post hoc analysis.
ISSN:2331-8422