  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.
