Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography

no code implementations • 29 Mar 2022 • Marcel Schloz, Johannes Müller, Thomas C. Pekin, Wouter Van den Broek, Christoph T. Koch

We present a method that lowers the dose required for a ptychographic reconstruction by adaptively scanning the specimen, thereby providing the required spatial information redundancy in the regions of highest importance.

reinforcement-learning Reinforcement Learning (RL)

Overcoming information reduced data and experimentally uncertain parameters in ptychography with regularized optimization

no code implementations • 4 May 2020 • Marcel Schloz, Thomas C. Pekin, Zhen Chen, Wouter Van den Broek, David A. Muller, Christoph T. Koch

The overdetermination of the mathematical problem underlying ptychography is reduced by a host of experimentally more desirable settings.

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