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.
@article{arxiv.2503.02852,
title = {Large-Angle Convergent-Beam Electron Diffraction Patterns via Conditional Generative Adversarial Networks},
author = {Joseph J. Webb and Richard Beanland and Rudolf A. Römer},
journal= {arXiv preprint arXiv:2503.02852},
year = {2025}
}