Hidden self-energies as origin of cuprate superconductivity revealed by machine learning

• Yamaji, Youhei

  Center for Green Research on Energy and Environmental Materials, National Institute for Materials Science; Department of Applied Physics, University of Tokyo; JST, PRESTO

• Yoshida, Teppei

  Graduate School of Human and Environmental Studies, Kyoto University

• Fujimori, Atsushi

  Department of Physics, University of Tokyo; Department of Applied Physics, Waseda University

• Imada, Masatoshi

  Toyota Physical and Chemical Research Institute; Research Institute for Science and Technology, Waseda University

Abstract

Experimental data are the source of understanding matter. However, measurable quantities are limited and theoretically important quantities are sometimes hidden. Nonetheless, recent progress of machine-learning techniques opens possibilities of exposing them only from available experimental data. In this paper, after establishing the reliability of the method in various careful benchmark tests, the Boltzmann machine method is applied to the angle-resolved photoemission spectroscopy spectra of cuprate high-temperature superconductors, Bi₂Sr₂CuO₆₊[δ] (Bi2201) and Bi₂Sr₂CuO₈₊[δ] (Bi2212). We find prominent peak structures in both normal and anomalous self-energies, but they cancel in the total self-energy making the structure apparently invisible, while the peaks make universally dominant contributions to superconducting gap, hence evidencing the signal that generates the high-Tc superconductivity. The relation between superfluid density and critical temperature supports involvement of universal carrier relaxation associated with dissipative strange metals, where enhanced superconductivity is promoted by entangled quantum-soup nature of the cuprates. The present achievement opens avenues for innovative machine-learning spectroscopy method to reveal fundamental properties hidden in direct experimental accesses.

Journal

• Physical Review Research

  Physical Review Research 3 (4), 043099-, 2021-11

  American Physical Society (APS)

Keywords

• Superconductivity
• Strongly correlated systems
• Machine learning
• Photoemission spectroscopy

Details

• CRID: 1050008597648705280
• NII Article ID: 120007168541
• DOI: 10.1103/physrevresearch.3.043099
• ISSN: 26431564
• HANDLE: 2433/265876
• Web Site: https://link.aps.org/article/10.1103/PhysRevResearch.3.043099
• Web Site: http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevResearch.3.043099/fulltext
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN