2019/02/10 by Kevin Kaufmann, Chaoyi Zhu, Alexander S. Rosengarten +5 · 170 citations
Chemistry · Computer Science · Materials Science · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Bravais lattice #Chemistry #Computer science #Convolutional neural network #Crystal structure #Crystallography #Crystallography and molecular interactions #Diffraction #Electron backscatter diffraction #Electron crystallography #Electron diffraction #Machine Learning in Materials Science #Materials science #Optics #Physics #Synchrotron #X-ray Diffraction in Crystallography #cond-mat.mtrl-sci #cs.CV
paper · pdf · doi:10.1126/science.aay3062
published in Science 367(6477), 564-568 (American Association for the Advancement of Science) · 35 pages, 17 figures with extended data included
arxiv created 2019/02/10 · openalex publication_date 2020/01/31 · arxiv updated 2020/02/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Electron backscatter diffraction (EBSD) is one of the primary tools for crystal structure determination. However, this method requires human input to select potential phases for Hough-based or dictionary pattern matching and is not well suited for phase identification. Automated phase identification is the first step in making EBSD into a high-throughput technique. We used a machine learning-based approach and developed a general methodology for rapid and autonomous identification of the crystal symmetry from EBSD patterns. We evaluated our algorithm with diffraction patterns from materials outside the training set. The neural network assigned importance to the same symmetry features that a crystallographer would use for structure identification.