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. 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.