Export citation

Export citation

Choose format for download:

Download Citation

    Tuning superconducting pairing symmetry via a staggered potential in the doped honeycomb Hubbard model

    Yanmei Cai1, Yicheng Xiong1, Ying Liang2,1, and Tianxing Ma1,*

    • *Contact author: txma@bnu.edu.cn

    Phys. Rev. B 114, 115102 – Published 6 August, 2026

    DOI: https://doi.org/10.1103/glxl-l925

    Abstract

    The ability to control superconducting pairing symmetry is crucial for designing unconventional and topological superconductors, yet practical tuning parameters beyond chemical doping remain limited. In this study, we investigate the effect of a tunable sublattice staggered potential on the pairing symmetry in the doped honeycomb Hubbard model. Determinant quantum Monte Carlo at finite temperature and constrained-path quantum Monte Carlo at zero temperature are employed to compute spin susceptibilities and pairing correlations in different channels. We find that increasing the staggered potential suppresses antiferromagnetic fluctuations, and at low doping, induces a transition in the dominant pairing tendency from d+id-wave to fn-wave, with consistent results from both quantum Monte Carlo methods. In contrast, at higher doping levels, the system remains dominated by d+id-wave pairing even under an enhanced staggered potential. Moreover, strengthening the on-site interaction U enhances the dominant pairing channel, underscoring the essential role of electronic correlations. Our results establish the staggered potential as a practical band-engineering tool for selecting unconventional pairing symmetries without varying the doping concentration, providing inspiration for designing graphene-based artificial superconductors and related doped band insulators such as LixMNCl.

    Physics Subject Headings (PhySH)

    Authorization Required

    We need you to provide your credentials before accessing this content.

    References (Subscription Required)

    Outline

    Information

    Sign In to Your Journals Account

    Filter

    Filter

    Article Lookup

    Enter a citation