Unsupervised clustering for identifying spatial inhomogeneity on local electronic structures

DOI: 10.1038/s41535-021-00407-5

Authors: Hideaki Iwasawa (Synchrotron Radiation Research Center, National Institutes for Quantum Science and Technology; QST Advanced Study Laboratory, National Institutes for Quantum Science and Technology; Hiroshima Synchrotron Radiation Center, Hiroshima University) , Tetsuro Ueno (Synchrotron Radiation Research Center, National Institutes for Quantum Science and Technology; QST Advanced Study Laboratory, National Institutes for Quantum Science and Technology) , Takahiko Masui (Kindai University) , Setsuko Tajima (Osaka University)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Npj Quantum Materials , VOL 7

State: Published (Approved)
Published: February 2022
Diamond Proposal Number(s): 16871 , 17192

Abstract: Spatial inhomogeneity on the electronic structure is one of the vital keys to provide a better understanding of the emergent quantum phenomenon. Given the recent developments on spatially resolved ARPES (ARPES: angle-resolved photoemission spectroscopy), the information on the spatial inhomogeneity on the local electronic structure is now accessible. However, the next challenge becomes apparent as the conventional analysis encounters difficulty handling a large volume of a spatial mapping dataset, typically generated in the spatially resolved ARPES experiments. Here, we propose a machine-learning-based approach using unsupervised clustering algorithms (K-means and fuzzy-c-means) to examine the spatial mapping dataset. Our analysis methods enable automated categorization of the spatial mapping dataset with a much-reduced human intervention and workload, thereby allowing quick identification and visualization of the spatial inhomogeneity on the local electronic structures.

Journal Keywords: Characterization and analytical techniques; Electronic properties and materials

Subject Areas: Physics, Materials, Information and Communication Technology


Instruments: I05-ARPES

Added On: 22/02/2022 08:56

Documents:
s41535-021-00407-5.pdf

Discipline Tags:

Quantum Materials Artificial Intelligence Physics Hard condensed matter - structures Information & Communication Technologies Materials Science Data processing

Technical Tags:

Spectroscopy Angle Resolved Photoemission Spectroscopy (ARPES)