An Acquisition Parameter Study for Machine-Learning-Enabled Electron Backscatter Diffraction

• Published in: Microscopy and microanalysis Vol. 27; no. 4; pp. 776 - 793
• Main Authors: Kaufmann, Kevin, Vecchio, Kenneth S.
• Format: Journal Article
• Language: English
• Published: New York, USA Cambridge University Press 01.08.2021; Oxford University Press
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Automation; Classification; Computer vision; Data collection; Deep learning; Diffraction patterns; Electron backscatter diffraction; Electron diffraction; Learning algorithms; Machine learning; Materials science; Mathematical models; Neural networks; Parameters; Performance degradation; Sensors; Software and Instrumentation; Transmission electron microscopy; machine learning; space groups; convolutional neural network; EBSD; electron diffraction

Methods within the domain of artificial intelligence are gaining traction for solving a range of materials science objectives, notably the use of deep neural networks for computer vision for the analysis of electron diffraction patterns. An important component of deploying these models is an underst...