Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns

no code implementations • 1 Feb 2022 • Joydeep Munshi, Alexander Rakowski, Benjamin H Savitzky, Steven E Zeltmann, Jim Ciston, Matthew Henderson, Shreyas Cholia, Andrew M Minor, Maria KY Chan, Colin Ophus

Implementation of a fast, robust, and fully-automated pipeline for crystal structure determination and underlying strain mapping for crystalline materials is important for many technological applications.

Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase Contrast Transmission Electron Microscopy Images

1 code implementation • 9 Dec 2020 • Robbie Sadre, Colin Ophus, Anstasiia Butko, Gunther H Weber

In this study, we compare the performance of a conventional Bragg filtering method to a deep learning routine based on the U-Net architecture.

py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets

1 code implementation • 20 Mar 2020 • Benjamin H Savitzky, Lauren A Hughes, Steven E Zeltmann, Hamish G Brown, Shiteng Zhao, Philipp M Pelz, Edward S Barnard, Jennifer Donohue, Luis Rangel DaCosta, Thomas C. Pekin, Ellis Kennedy, Matthew T Janish, Matthew M Schneider, Patrick Herring, Chirranjeevi Gopal, Abraham Anapolsky, Peter Ercius, Mary Scott, Jim Ciston, Andrew M Minor, Colin Ophus

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms.

Materials Science Mesoscale and Nanoscale Physics Applied Physics