Strange metallicity in the doped Hubbard model
Strange or bad metallic transport, defined by incompatibility with the conventional quasiparticle picture, is a theme common to many strongly correlated materials, including high-temperature superconductors. The Hubbard model represents a minimal starting point for modeling strongly correlated syste...
Saved in:
Published in | Science (American Association for the Advancement of Science) Vol. 366; no. 6468; pp. 987 - 990 |
---|---|
Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
United States
American Association for the Advancement of Science
22.11.2019
The American Association for the Advancement of Science AAAS |
Subjects | |
Online Access | Get full text |
ISSN | 0036-8075 1095-9203 1095-9203 |
DOI | 10.1126/science.aau7063 |
Cover
Summary: | Strange or bad metallic transport, defined by incompatibility with the conventional quasiparticle picture, is a theme common to many strongly correlated materials, including high-temperature superconductors. The Hubbard model represents a minimal starting point for modeling strongly correlated systems. Here we demonstrate strange metallic transport in the doped two-dimensional Hubbard model using determinantal quantum Monte Carlo calculations. Over a wide range of doping, we observe resistivities exceeding the Mott-Ioffe-Regel limit with linear temperature dependence. The temperatures of our calculations extend to as low as 1/40 of the noninteracting bandwidth, placing our findings in the degenerate regime relevant to experimental observations of strange metallicity. Our results provide a foundation for connecting theories of strange metals to models of strongly correlated materials. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 AC02-76SF00515; AC02-05CH11231 USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division National Energy Research Scientific Computing Center |
ISSN: | 0036-8075 1095-9203 1095-9203 |
DOI: | 10.1126/science.aau7063 |