Deep Data Analysis of Conductive Phenomena on Complex Oxide Interfaces: Physics from Data Mining

Journal Article · Wed Jan 01 00:00:00 EST 2014 · ACS Nano

DOI:https://doi.org/10.1021/nn502029b· OSTI ID:1156750

Strelcov, Evgheni ^[1]; Belianinov, Alex ^[1]; Hsieh, Ying-Hui ^[2]; Jesse, Stephen ^[1]; Baddorf, Arthur P ^[1]; Chu, Ying Hao ^[2]; Kalinin, Sergei V ^[1]

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1. ORNL
2. National Chiao Tung University, Hsinchu, Taiwan

Spatial variability of electronic transport in BiFeO3-CoFe2O4 (BFO-CFO) self-assembled heterostructures is explored using spatially resolved first order reversal curve (FORC) current voltage (IV) mapping. Multivariate statistical analysis of FORC-IV data classifies statistically significant behaviors and maps characteristic responses spatially. In particular, regions of grain, matrix, and grain boundary responses are clearly identified. K-means and Bayesian demixing analysis suggests the characteristic response be separated into four components, with hysteretic type behavior localized at the BFO-CFO tubular interfaces. The conditions under which Bayesian components allow direct physical interpretation are explored, and transport mechanisms at the grain boundaries and individual phases are analyzed. This approach conjoins multivariate statistical analysis with physics-based interpretation, actualizing a robust, universal, data driven approach to problem solving, which can be applied to exploration of local transport and other functional phenomena in other spatially inhomogeneous systems.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 1156750
• Journal Information: ACS Nano, Vol. 8, Issue 6; ISSN 1936--0851
• Country of Publication: United States
• Language: English

Related Subjects

Conduction hysteresis
oxide heterostructures
multivariate analysis
big data
scanning probe microscopy
FORC-IV