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Strelcov E, Belianinov A, Hsieh YH, et al. Deep data analysis of conductive phenomena on complex oxide interfaces: physics from data mining. ACS Nano. 2014;8(6):6449-57doi: 10.1021/nn502029b.
Strelcov, E., Belianinov, A., Hsieh, Y. H., Jesse, S., Baddorf, A. P., Chu, Y. H., & Kalinin, S. V. (2014). Deep data analysis of conductive phenomena on complex oxide interfaces: physics from data mining. ACS nano, 8(6), 6449-57. https://doi.org/10.1021/nn502029b
Strelcov, Evgheni, et al. "Deep data analysis of conductive phenomena on complex oxide interfaces: physics from data mining." ACS nano vol. 8,6 (2014): 6449-57. doi: https://doi.org/10.1021/nn502029b
Strelcov E, Belianinov A, Hsieh YH, Jesse S, Baddorf AP, Chu YH, Kalinin SV. Deep data analysis of conductive phenomena on complex oxide interfaces: physics from data mining. ACS Nano. 2014 Jun 24;8(6):6449-57. doi: 10.1021/nn502029b. Epub 2014 Jun 02. PMID: 24869675.
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ACS Nano. 2014 Jun 24;8(6):6449-57. doi: 10.1021/nn502029b. Epub 2014 Jun 02.

Deep data analysis of conductive phenomena on complex oxide interfaces: physics from data mining.

ACS nano

Evgheni Strelcov, Alexei Belianinov, Ying-Hui Hsieh, Stephen Jesse, Arthur P Baddorf, Ying-Hao Chu, Sergei V Kalinin

Affiliations

  1. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory , Oak Ridge, Tennessee 37831, United States.

PMID: 24869675 DOI: 10.1021/nn502029b

Abstract

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 suggest 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.

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