Journal article
Py4DSTEM: A Software Package for Four-Dimensional Scanning Transmission Electron Microscopy Data Analysis
BH Savitzky, SE Zeltmann, LA Hughes, HG Brown, S Zhao, PM Pelz, TC Pekin, ES Barnard, J Donohue, L Rangel Dacosta, E Kennedy, Y Xie, MT Janish, MM Schneider, P Herring, C Gopal, A Anapolsky, R Dhall, KC Bustillo, P Ercius Show all
Microscopy and Microanalysis | Published : 2021
Abstract
Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all wh..
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Awarded by National Science Foundation
Funding Acknowledgements
This project is supported by the Toyota Research Institute. Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. BHS and LAH were supported by the Toyota Research Institute. SEZ and PP were supported by STROBE, an NSF Science and Technology Center, under Grant No. DMR 1548924. SZ was supported by the US Office of Naval Research under Grant No. N00014-17-1-2283. TCP acknowledges funding from the DFG-project BR 5095/2-1 (Compressed sensing in ptychography and transmission electron microscopy). JD was supported by the Dow University Partnership Initiative Program. YX was funded by under the U.S. Department of Energy Basic Energy Research Materials Sciences and Engineering Division Contract No. KC22ZH. MTJ and MMS were supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division under award LANLE4BU. Los Alamos National Laboratory, an affirmative action/equal opportunity employer, is managed by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy under contract 89233218CNA000001. CO acknowledges support of an Department of Energy Early Career Research Award. HGB and JC acknowledge additional support from the Presidential Early Career Award for Scientists and Engineers (PECASE) through the U.S. Department of Energy. The authors thank Shreyas Cholia, Matthew Henderson, Rollin Thomas, and Ludovico Bianchi from the National Energy Research Scientific Computing Center (NERSC) for support with high-performance computing integration. NERSC is a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. The authors thank Blas Uberuaga for sample acquisition and support. Thanks also to the developers of Hyperspy, openNCEM, SciPy, NumPy, and Matplotlib, without whom this project would not have been possible.