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Title: Big Data in Reciprocal Space: Sliding Fast Fourier Transforms for Determining Periodicity

Abstract

Significant advances in atomically resolved imaging of crystals and surfaces have occurred in the last decade allowing unprecedented insight into local crystal structures and periodicity. Yet, the analysis of the long-range periodicity from the local imaging data, critical to correlation of functional properties and chemistry to the local crystallography, remains a challenge. Here, we introduce a Sliding Fast Fourier Transform (FFT) filter to analyze atomically resolved images of in-situ grown La5/8Ca3/8MnO3 (LCMO) films. We demonstrate the ability of sliding FFT algorithm to differentiate two sub-lattices, resulting from a mixed-terminated surface. Principal Component Analysis and Independent Component Analysis of the Sliding FFT dataset reveal the distinct changes in crystallography, step edges, and boundaries between the multiple sub-lattices. The implications for the LCMO system are discussed. The method is universal for images with any periodicity, and is especially amenable to atomically resolved probe and electron-microscopy data for rapid identification of the sub-lattices present.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1]
+ Show Author Affiliations
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1185879
Alternate Identifier(s):
OSTI ID: 1420542
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Applied Physics Letters
Additional Journal Information:
Journal Volume: 106; Journal Issue: 9; Journal ID: ISSN 0003-6951
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 74 ATOMIC AND MOLECULAR PHYSICS; Atomic Structure; Periodicity; Fourier Transforms; Big-Data

Citation Formats

Vasudevan, Rama K., Belianinov, Alex, Gianfrancesco, Anthony G., Baddorf, Arthur P., Tselev, Alexander, Kalinin, Sergei V., and Jesse, Stephen. Big Data in Reciprocal Space: Sliding Fast Fourier Transforms for Determining Periodicity. United States: N. p., 2015. Web. doi:10.1063/1.4914016.
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Vasudevan, Rama K., Belianinov, Alex, Gianfrancesco, Anthony G., Baddorf, Arthur P., Tselev, Alexander, Kalinin, Sergei V., & Jesse, Stephen. Big Data in Reciprocal Space: Sliding Fast Fourier Transforms for Determining Periodicity. United States. https://doi.org/10.1063/1.4914016
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Vasudevan, Rama K., Belianinov, Alex, Gianfrancesco, Anthony G., Baddorf, Arthur P., Tselev, Alexander, Kalinin, Sergei V., and Jesse, Stephen. Tue . "Big Data in Reciprocal Space: Sliding Fast Fourier Transforms for Determining Periodicity". United States. https://doi.org/10.1063/1.4914016. https://www.osti.gov/servlets/purl/1185879.
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@article{osti_1185879,
title = {Big Data in Reciprocal Space: Sliding Fast Fourier Transforms for Determining Periodicity},
author = {Vasudevan, Rama K. and Belianinov, Alex and Gianfrancesco, Anthony G. and Baddorf, Arthur P. and Tselev, Alexander and Kalinin, Sergei V. and Jesse, Stephen},
abstractNote = {Significant advances in atomically resolved imaging of crystals and surfaces have occurred in the last decade allowing unprecedented insight into local crystal structures and periodicity. Yet, the analysis of the long-range periodicity from the local imaging data, critical to correlation of functional properties and chemistry to the local crystallography, remains a challenge. Here, we introduce a Sliding Fast Fourier Transform (FFT) filter to analyze atomically resolved images of in-situ grown La5/8Ca3/8MnO3 (LCMO) films. We demonstrate the ability of sliding FFT algorithm to differentiate two sub-lattices, resulting from a mixed-terminated surface. Principal Component Analysis and Independent Component Analysis of the Sliding FFT dataset reveal the distinct changes in crystallography, step edges, and boundaries between the multiple sub-lattices. The implications for the LCMO system are discussed. The method is universal for images with any periodicity, and is especially amenable to atomically resolved probe and electron-microscopy data for rapid identification of the sub-lattices present.},
doi = {10.1063/1.4914016},
journal = {Applied Physics Letters},
number = 9,
volume = 106,
place = {United States},
year = {Tue Mar 03 00:00:00 EST 2015},
month = {Tue Mar 03 00:00:00 EST 2015}
}
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https://doi.org/10.1063/1.4914016
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    Works referencing / citing this record:

    Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images
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    Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography
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    Direct atomic fabrication and dopant positioning in Si using electron beams with active real-time image-based feedback
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    Surface-screening mechanisms in ferroelectric thin films and their effect on polarization dynamics and domain structures
    journal, January 2018

    • Kalinin, Sergei V.; Kim, Yunseok; Fong, Dillon D.
    • Reports on Progress in Physics, Vol. 81, Issue 3
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    Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets
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    • Belianinov, Alex; Vasudevan, Rama; Strelcov, Evgheni
    • Advanced Structural and Chemical Imaging, Vol. 1, Issue 1
    • DOI: 10.1186/s40679-015-0006-6

    Ghost identification based on single-pixel imaging in big data environment
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