Crystallographic phase identifier of a convolutional self-attention neural network (CPICANN) on powder diffraction patterns.

ABSTRACT: Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase id

SUBMITTER: Zhang S 

PROVIDER: S-EPMC11220882 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature