Effective-field theory for high- cuprates
arXiv:2009.12790 · doi:10.3390/condmat6030024
Abstract
Starting with a minimal model for the CuO planes with the on-site Hilbert space reduced to only three effective valence centers [CuO] (nominally Cu) with different conventional spin and different orbital symmetry we propose a unified non-BCS model that allows one to describe the main features of the phase diagrams of doped cuprates within the framework of a simple effective field theory.
arXiv comment7 pages, 1 figure
References in corpus (14)
- Atomic-scale Electronic Structure of the Cuprate d-Symmetry Form Factor Density Wave State
- Strong charge fluctuations manifested in the high-temperature Hall coefficient of high-T_c cuprates
- Inhomogeneous superconductivity and the "pseudogap state of novel superconductors
- Unusual behavior of cuprates explained by heterogeneous charge localization
- What is the true charge transfer gap in parent insulating cuprates?
- The Effects of Phase Separation in the Cuprate Superconductors
- Dielectric versus magnetic pairing mechanisms in high-temperature cuprate superconductors investigated using Raman scattering
- Resistivity phase diagram of cuprates revisited
- Phase separation in a lattice model of a superconductor with pair hopping
- Perspectives of the disproportionation driven superconductivity in strongly correlated 3d compounds
- Superconducting and Pseudogap Transition Temperatures in High-Tc Cuprates and the Dependence on Pressure
- Topological structures in unconventional scenario for 2D cuprates
- Critical temperatures of a model cuprate
- Study of long-range orders of hard-core bosons coupled to cooperative normal modes in two-dimensional lattices
Cited by in corpus (6)
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- Phase separation in high-T cuprates
- Insulator-bad metal transition in RNiO nickelates beyond Hubbard model and density functional theory
- Monte Carlo simulation of a model cuprate
- Predicting parameters of a model cuprate superconductor using machine learning