Big Data in Reciprocal Space [electronic resource] : Sliding Fast Fourier Transforms for Determining Periodicity
- Washington, D.C. : United States. Dept. of Energy. Office of Science, 2015.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- Article numbers 091,601 : digital, PDF file
- Additional Creators:
- Oak Ridge National Laboratory
United States. Department of Energy. Office of Science
United States. Department of Energy. Office of Scientific and Technical Information
- 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 films. We demonstrate the ability of sliding FFT algorithm to differentiate two sub-lattices, resulting from a mixed-terminated surface. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) of the Sliding FFT dataset reveal the distinct changes in crystallography, step edges and boundaries between the multiple sub-lattices. 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.
- Published through SciTech Connect.
Applied Physics Letters 106 9 ISSN 0003-6951 AM
Rama K. Vasudevan; Alex Belianinov; Anthony G. Gianfrancesco; Arthur P. Baddorf; Alexander Tselev; Sergei V. Kalinin; Stephen Jesse.
- Funding Information:
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