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4D >Crystal: Deep Learning Crystallographic Information From Electron Diffraction Images

Published online by Cambridge University Press:  30 July 2021

Joydeep Munshi
Affiliation:
ANL, United States
Alexander M Rakowski
Affiliation:
LBNL, United States
Benjamin Savitzky
Affiliation:
Lawrence Berkeley National Laboratory, California, United States
Colin Ophus
Affiliation:
Lawrence Berkeley National Laboratory, California, United States
Matthew L Henderson
Affiliation:
LBNL, United States
Shreyas Cholia
Affiliation:
LBNL, United States
Maria KY Chan
Affiliation:
ANL, United States

Abstract

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Type
Full System and Workflow Automation for Enabling Big Data and Machine Learning in Electron Microscopy
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Ciston, J., et al. , The 4D Camera: Very High Speed Electron Counting for 4D-STEM. Microscopy and Microanalysis, 2019. 25(S2): p. 1930-1931.CrossRefGoogle Scholar
Savitzky, B.H., et al. , py4DSTEM: A software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets. arXiv preprint arXiv:2003.09523, 2020.Google Scholar
Ong, S.P., et al. , Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Computational Materials Science, 2013. 68: p. 314-319.CrossRefGoogle Scholar
Jain, A., et al. , Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 2013. 1(1): p. 011002.Google Scholar
Hicks, D., et al. , The AFLOW Library of Crystallographic Prototypes: Part 2. Computational Materials Science, 2019. 161: p. S1-S1011.Google Scholar
Mehl, M.J., et al. , The AFLOW Library of Crystallographic Prototypes: Part 1. Computational Materials Science, 2017. 136: p. S1-S828.CrossRefGoogle Scholar
Munshi, J., manipulatt v2021.01.14, Tool to manipulate primitive cell to rotate, tilt, tile to a supercell. Github repository, 2021 (https://github.com/MaterialEyes/manipulatt).Google Scholar
Ophus, C., A fast image simulation algorithm for scanning transmission electron microscopy. Advanced Structural and Chemical Imaging, 2017. 3(1): p. 13.CrossRefGoogle ScholarPubMed
Pryor, A., Ophus, C., and Miao, J., A streaming multi-GPU implementation of image simulation algorithms for scanning transmission electron microscopy. Advanced Structural and Chemical Imaging, 2017. 3(1): p. 15.Google ScholarPubMed
Ronneberger, O., Fischer, P., and Brox, T.. U-net: Convolutional networks for biomedical image segmentation. in International Conference on Medical image computing and computer-assisted intervention. 2015. Springer.Google Scholar