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dc.contributor.authorWahl, Peter
dc.contributor.authorSingh, Udai R.
dc.contributor.authorTsurkan, Vladimir
dc.contributor.authorLoidl, Alois
dc.identifier.citationWahl , P , Singh , U R , Tsurkan , V & Loidl , A 2020 , ' Nanoscale electronic inhomogeneity in FeSe 0.4 Te 0.6 revealed through unsupervised machine learning ' , Physical Review. B, Condensed matter and materials physics , vol. 101 , no. 11 , 115112 .
dc.identifier.otherPURE: 266507847
dc.identifier.otherPURE UUID: d9922a96-668b-416d-a7d9-a4e4c3116d12
dc.identifier.otherORCID: /0000-0002-8635-1519/work/70919915
dc.identifier.otherScopus: 85083198900
dc.identifier.otherWOS: 000518533900004
dc.description.abstractWe report on an apparent low-energy nanoscale electronic inhomogeneity in FeSe0.4Te0.6 due to the distribution of selenium and tellurium atoms revealed through unsupervised machine learning. Through an unsupervised clustering algorithm, characteristic spectra of selenium- and tellurium-rich regions are identified. The inhomogeneity linked to these spectra can clearly be traced in the differential conductance and is detected both at energy scales of a few electron volts as well as within a few millielectronvolts of the Fermi energy. By comparison with ARPES, this inhomogeneity can be linked to an electron-like band just above the Fermi energy. It is directly correlated with the local distribution of selenium and tellurium. There is no clear correlation with the magnitude of the superconducting gap, however the height of the coherence peaks shows significant correlation with the intensity with which this band is detected, and hence with the local chemical composition.
dc.relation.ispartofPhysical Review. B, Condensed matter and materials physicsen
dc.rightsCopyright © 2020 American Physical Society. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQC Physicsen
dc.subjectTK Electrical engineering. Electronics Nuclear engineeringen
dc.titleNanoscale electronic inhomogeneity in FeSe0.4Te0.6 revealed through unsupervised machine learningen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Physics and Astronomyen
dc.contributor.institutionUniversity of St Andrews. Centre for Designer Quantum Materialsen
dc.contributor.institutionUniversity of St Andrews. Condensed Matter Physicsen
dc.description.statusPeer revieweden

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