摘要
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 doesn’t know:it can rapidly identify data outside the distribution it was trained on,such as novel phases and mixtures.These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both‘on-the-fly’and during post hoc analysis.
基金
This study was funded by the German Research Foundation(DFG)as part of Collaborative Research Centers SFB-TR 87 and SFB-TR 103
This research used resources of the National Synchrotron Light Source II,a U.S.Department of Energy(DOE)Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under Contract No.DE-SC0012704
the BNL Laboratory Directed Research and Development(LDRD)project 20-032‘Accelerating materials discovery with total scattering via machine learning’.The center for interface dominated high-performance materials(ZGH,Ruhr-Universität Bochum,Bochum,Germany)is acknowledged for X-ray diffraction experiments.
