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

2021/04/09 by Banko, Lars, Maffettone, Phillip M., Naujoks, Dennis +2 · 2 citations
#Data Analysis #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Statistics and Probability (physics.data-an)

paper · doi:10.48550/arxiv.2104.04392

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. 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.

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