Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography
Abstract
Electron microscopy is undergoing a transition; from the model of producing only a few micrographs, through the current state where many images and spectra can be digitally recorded, to a new mode where very large volumes of data (movies, ptychographic and multi-dimensional series) can be rapidly obtained. In this paper, we discuss the application of so-called “big-data” methods to high dimensional microscopy data, using unsupervised multivariate statistical techniques, in order to explore salient image features in a specific example of BiFeO3 domains. Remarkably, k-means clustering reveals domain differentiation despite the fact that the algorithm is purely statistical in nature and does not require any prior information regarding the material, any coexisting phases, or any differentiating structures. While this is a somewhat trivial case, this example signifies the extraction of useful physical and structural information without any prior bias regarding the sample or the instrumental modality. Further interpretation of these types of results may still require human intervention. Finally, however, the open nature of this algorithm and its wide availability, enable broad collaborations and exploratory work necessary to enable efficient data analysis in electron microscopy.
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences
- Florida State Univ., Tallahassee, FL (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Materials Sciences and Technology Division
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1261280
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 6; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; imaging techniques; transmission electron microscopy
Citation Formats
Jesse, S., Chi, M., Belianinov, A., Beekman, C., Kalinin, S. V., Borisevich, A. Y., and Lupini, A. R. Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography. United States: N. p., 2016.
Web. doi:10.1038/srep26348.
Jesse, S., Chi, M., Belianinov, A., Beekman, C., Kalinin, S. V., Borisevich, A. Y., & Lupini, A. R. Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography. United States. https://doi.org/10.1038/srep26348
Jesse, S., Chi, M., Belianinov, A., Beekman, C., Kalinin, S. V., Borisevich, A. Y., and Lupini, A. R. Mon .
"Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography". United States. https://doi.org/10.1038/srep26348. https://www.osti.gov/servlets/purl/1261280.
@article{osti_1261280,
title = {Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography},
author = {Jesse, S. and Chi, M. and Belianinov, A. and Beekman, C. and Kalinin, S. V. and Borisevich, A. Y. and Lupini, A. R.},
abstractNote = {Electron microscopy is undergoing a transition; from the model of producing only a few micrographs, through the current state where many images and spectra can be digitally recorded, to a new mode where very large volumes of data (movies, ptychographic and multi-dimensional series) can be rapidly obtained. In this paper, we discuss the application of so-called “big-data” methods to high dimensional microscopy data, using unsupervised multivariate statistical techniques, in order to explore salient image features in a specific example of BiFeO3 domains. Remarkably, k-means clustering reveals domain differentiation despite the fact that the algorithm is purely statistical in nature and does not require any prior information regarding the material, any coexisting phases, or any differentiating structures. While this is a somewhat trivial case, this example signifies the extraction of useful physical and structural information without any prior bias regarding the sample or the instrumental modality. Further interpretation of these types of results may still require human intervention. Finally, however, the open nature of this algorithm and its wide availability, enable broad collaborations and exploratory work necessary to enable efficient data analysis in electron microscopy.},
doi = {10.1038/srep26348},
journal = {Scientific Reports},
number = ,
volume = 6,
place = {United States},
year = {Mon May 23 00:00:00 EDT 2016},
month = {Mon May 23 00:00:00 EDT 2016}
}
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