Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condi...

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Published in:	Microscopy and microanalysis Vol. 28; no. 2; pp. 390 - 403
Main Authors:	Ophus, Colin, Zeltmann, Steven E., Bruefach, Alexandra, Rakowski, Alexander, Savitzky, Benjamin H., Minor, Andrew M., Scott, Mary C.
Format:	Journal Article
Language:	English
Published:	New York, USA Cambridge University Press 01.04.2022 Oxford University Press Microscopy Society of America (MSA)
Subjects:	Algorithms automated crystal orientation mapping (ACOM) Automation Crystal structure Datasets Diffraction Diffraction patterns Electron beams Electron probe Electron probes Experiments four-dimensional scanning Grains Libraries Machine learning Mapping MATERIALS SCIENCE Methods nanobeam electron diffraction (NBED) Nanotechnology Nanowires open-source software Orientation relationships Point defects Scanning electron microscopy scanning electron nanodiffraction (SEND) Software and Instrumentation Source code Transmission electron microscopy transmission electron microscopy (4D-STEM) automated crystal orientation mapping (ACOM) open-source software nanobeam electron diffraction (NBED) scanning electron nanodiffraction (SEND) four-dimensional scanning transmission electron microscopy (4D-STEM)
ISSN:	1431-9276, 1435-8115, 1435-8115
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Abstract	Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.
AbstractList	Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets. Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.
Author	Scott, Mary C. Bruefach, Alexandra Zeltmann, Steven E. Ophus, Colin Minor, Andrew M. Savitzky, Benjamin H. Rakowski, Alexander
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Copyright	Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of the Microscopy Society of America
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Keywords	automated crystal orientation mapping (ACOM) open-source software nanobeam electron diffraction (NBED) scanning electron nanodiffraction (SEND) four-dimensional scanning transmission electron microscopy (4D-STEM)
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Snippet	Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust...
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SubjectTerms	Algorithms automated crystal orientation mapping (ACOM) Automation Crystal structure Datasets Diffraction Diffraction patterns Electron beams Electron probe Electron probes Experiments four-dimensional scanning Grains Libraries Machine learning Mapping MATERIALS SCIENCE Methods nanobeam electron diffraction (NBED) Nanotechnology Nanowires open-source software Orientation relationships Point defects Scanning electron microscopy scanning electron nanodiffraction (SEND) Software and Instrumentation Source code Transmission electron microscopy transmission electron microscopy (4D-STEM)
Title	Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching
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