Electron backscatter diffraction is one standard technique for determining crystal structure, typically of materials or geological samples. However, this method requires structural guesses and user input that are often time consuming or incorrect. Kaufmann et al. developed a general methodology using a convolutional neural network that automatically determines the crystal structure quickly and with high accuracy. After the network is exposed to a training set, it can identify the crystal structure without any additional input most of the time, providing a method for eliminating some of the guesswork from crystal structure determination.
Science, this issue p. 564
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.
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