Strongly Correlated Superconductivity in Twisted Bilayer Graphene: A Gutzwiller Study
Abstract
We study strongly correlated superconductivity in magic-angle twisted bilayer graphene (MATBG) using variational Gutzwiller wavefunction where the Gutzwiller projector is allowed to break charge U(1) symmetry to accommodate superconducting (SC) order. The ground state energy is evaluated via the Gutzwiller Approximation applied to an 8-band model consisting of correlated f-orbitals and uncorrelated c-orbitals, with interactions including onsite Coulomb repulsion , phonon-mediated anti-Hund's coupling , and intra-orbital Hund's coupling . At filling , we map out the phase diagram as a function of and , finding a dome-shaped Fermi liquid (FL) phase that separates a weakly correlated BCS-like SC (BCS-SC) at small from a strongly correlated SC (SC-SC) at large . A nematic SC state, stabilized over a large region of the phase diagram including the realistic parameter regime of MATBG, acquires a nodal gap structure with V-shaped density of states at large via interaction-driven SC gap reconstruction. In the SC-SC regime, the off-diagonal (charge-U(1)-breaking) components of strongly suppress -orbital charge fluctuations while maintaining finite pairing order and a sizeable quasiparticle weight , distinguishing it from a conventional Mott insulator. We further identify a novel small Fermi liquid (sFL) state with effective Fermi surface volume . Interestingly, in the intermediate- ( meV) and large- ( meV) regimes, the conventional FL and the sFL are the lowest-energy normal phases, respectively, potentially serve as the parent states of the SC-SC phase. These results illuminate the interplay between strong correlations and unconventional pairing in MATBG, and establish a versatile Gutzwiller framework applicable to other strongly correlated superconductors.
Keywords
Cite
@article{arxiv.2604.04631,
title = {Strongly Correlated Superconductivity in Twisted Bilayer Graphene: A Gutzwiller Study},
author = {Matthew Shu Liang and Yi-Jie Wang and Geng-Dong Zhou and Zhi-Da Song and Xi Dai},
journal= {arXiv preprint arXiv:2604.04631},
year = {2026}
}