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NN10.04 - Deep Data Analysis of Atomic Level Structure-Property Relationship in Superconductive Materials 
Date/Time:
December 4, 2014   2:30pm - 2:45pm
 
 
 
 
 
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Understanding the underlying physics of superconductive materials can be greatly facilitated by establishing a relationship between atomic structure and electronic properties at the nanoscale. Here, the structural data is obtained from high-resolution scanning tunneling microscopy imaging, whereas tunneling spectroscopy mapping provides information on the local electronic properties such as band structure and, in our case, the superconductor gap. We further explore local interactions with statistical methods such as Principle Component Analysis (PCA) and Bayesian Statistics to perform unsupervised classification and cross-correlative analysis of these data to establish the internal data structure and reveal the correlations between structure and functionality. We further explore the pathways to map these behaviors on the atomistic models parameterized via interaction Hamiltonian terms. In this approach, the statistically significant atomic configurations are established and further used as an input into first principle modelling. Thus, the determined electronic structure is then compared to the local tunneling spectra. This approach is applied to explore local behavior of the chemically phase separated FeSeTe superconductor, identifying effects of local phase separation, structural defects, and magnetic impurities on superconductive behavior. This research was conducted (AG, SK, PG, PM, AB, GS) at the CNMS, sponsored at ORNL by the Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. DOE, Materials Science and Energy Division (AB), the Compute and Data Environment for science at ORNL (GS), and the Institute for Functional Imaging of Materials. Fellowship support (AG) from the UT/ORNL Bredesen Center for Interdisciplinary Research and Graduate Education.
 


 
 
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