A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM)

no code implementations • 12 Sep 2023 • Luis Rangel DaCosta, Katherine Sytwu, Catherine Groschner, Mary Scott

Machine learning techniques are attractive options for developing highly-accurate automated analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM).

Synthetic Data Generation

Generalization Across Experimental Parameters in Machine Learning Analysis of High Resolution Transmission Electron Microscopy Datasets

1 code implementation • 20 Jun 2023 • Katherine Sytwu, Luis Rangel DaCosta, Mary C. Scott

Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters.

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