Deep data mining in a real space: separation of intertwined electronic responses in a lightly doped BaFe2As2. (25th October 2016)

• Record Type: Journal Article
• Authors: Ziatdinov, Maxim; Maksov, Artem; Li, Li; Sefat, Athena S; Maksymovych, Petro; Kalinin, Sergei V
• Is Part Of: Nanotechnology. Volume 27:Number 47(2016)
• Publication Date: 2016-10-25
• Subjects: data mining -- statistical learning -- signal unmixing -- strongly correlated systems -- scanning tunneling microscopy; Nanotechnology -- Periodicals; Nanotechnology -- Periodicals; Nanotechnology; Publications périodiques; Nanotechnologies; Periodicals; 620.5
• DOI: 10.1088/0957-4484/27/47/475706
• Languages: English
• ISSNs: 0957-4484

Abstract

Abstract: Electronic interactions present in material compositions close to the superconducting dome play a key role in the manifestation of high- T c superconductivity. In many correlated electron systems, however, the parent or underdoped states exhibit strongly inhomogeneous electronic landscape at the nanoscale that may be associated with competing, coexisting, or intertwined chemical disorder, strain, magnetic, and structural order parameters. Here we demonstrate an approach based on a combination of scanning tunneling microscopy/spectroscopy and advanced statistical learning for an automatic separation and extraction of statistically significant electronic behaviors in the spin density wave regime of a lightly (∼1%) gold-doped BaFe2 As2 . We show that the decomposed STS spectral features have a direct relevance to fundamental physical properties of the system, such as SDW-induced gap, pseudogap-like state, and impurity resonance states.