Papers › Large-Angle Convergent-Beam Electron Diffraction Patterns via Conditional Generative...
Large-Angle Convergent-Beam Electron Diffraction Patterns via Conditional Generative Adversarial Networks
Joseph J. Webb, Richard Beanland, Rudolf A. Römer
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We show how generative machine learning can be used for the rapid computation of strongly dynamical electron diffraction directly from crystal structures, specifically in large-angle convergent-beam electron diffraction (LACBED) patterns. We find that a conditional generative adversarial network can learn the connection between the projected potential from a cubic crystal's unit cell and the corresponding LACBED pattern. Our model can generate diffraction patterns on a GPU many orders of magnitude faster than existing direct simulation methods. Furthermore, our approach can accurately retrieve the projected potential from diffraction patterns, opening a new approach for the inverse problem of determining crystal structure.
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