Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

Belianinov, Alex; Panchapakesan, G.; Lin, Wenzhi; Sales, Brian C.; Sefat, Athena Safa; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.

doi:10.1063/1.4902996

Title: Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

Journal Article · Tue Dec 02 00:00:00 EST 2014 · APL Materials

DOI:https://doi.org/10.1063/1.4902996· OSTI ID:1265676

Belianinov, Alex ^[1]; Panchapakesan, G. ^[1]; Lin, Wenzhi ^[1]; Sales, Brian C. ^[1]; Sefat, Athena Safa ^[1]; Jesse, Stephen ^[1]; Pan, Minghu ^[1]; Kalinin, Sergei V. ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

In this paper, atomic level spatial variability of electronic structure in Fe-based superconductor FeTe_0.55Se_0.45 (T_c = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe_1-x Se_x structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. Finally, this approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1265676

Alternate ID(s):: OSTI ID: 1286693

Journal Information:: APL Materials, Vol. 2, Issue 12; ISSN 2166-532X

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 6 works

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References (25)

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Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography Jesse, S.; Chi, M.; Belianinov, A. Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep26348	journal	May 2016
Smart machine learning or discovering meaningful physical and chemical contributions through dimensional stacking Griffin, Lee A.; Gaponenko, Iaroslav; Zhang, Shujun npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0222-z	journal	August 2019
Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets Belianinov, Alex; Vasudevan, Rama; Strelcov, Evgheni Advanced Structural and Chemical Imaging, Vol. 1, Issue 1 https://doi.org/10.1186/s40679-015-0006-6	journal	May 2015
G-mode magnetic force microscopy: Separating magnetic and electrostatic interactions using big data analytics Collins, Liam; Belianinov, Alex; Proksch, Roger Applied Physics Letters, Vol. 108, Issue 19 https://doi.org/10.1063/1.4948601	journal	May 2016
Machine learning-based multidomain processing for texture-based image segmentation and analysis Borodinov, Nikolay; Tsai, Wan-Yu; Korolkov, Vladimir V. Applied Physics Letters, Vol. 116, Issue 4 https://doi.org/10.1063/1.5135328	journal	January 2020

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