標題: Deep Data Analysis of Conductive Phenomena on Complex Oxide Interfaces: Physics from Data Mining
作者: Strelcov, Evgheni
Belianinov, Alexei
Hsieh, Ying-Hui
Jesse, Stephen
Baddorf, Arthur P.
Chu, Ying-Hao
Kalinin, Sergei V.
材料科學與工程學系
Department of Materials Science and Engineering
關鍵字: conduction hysteresis;oxide heterostructures;multivariate analysis;big data;scanning probe microscopy;FORC-IV
公開日期: 1-六月-2014
摘要: Spatial variability of electronic transport in BiFeO3-CoFe2O4 (BFO-CFO) self-assembled heterostructures is explored using spatially resolved first-order reversal curve (FOR) 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.
URI: http://dx.doi.org/10.1021/nn502029b
http://hdl.handle.net/11536/24660
ISSN: 1936-0851
DOI: 10.1021/nn502029b
期刊: ACS NANO
Volume: 8
Issue: 6
起始頁: 6449
結束頁: 6457
顯示於類別:期刊論文


文件中的檔案:

  1. 000338089200114.pdf