Tight-binding description of intrinsic superconducting correlations in multilayer graphene
W.A. Munoz, L. Covaci, F.M. Peeters

TL;DR
This study uses GPU-based simulations to analyze intrinsic superconducting correlations in multilayer graphene, revealing stacking-dependent surface superconductivity and the effects of doping and hybrid stacking on the order parameter.
Contribution
It provides a detailed, self-consistent tight-binding analysis of superconductivity in multilayer graphene with various stacking configurations and doping scenarios, highlighting surface effects.
Findings
Surface superconductivity is robust in ABC stacking even at low pairing potentials.
In Bernal stacking, the surface order parameter is suppressed and a critical pairing potential exists.
Hybrid stacking with thin layers shows high-temperature surface superconductivity due to proximity effects.
Abstract
Using highly efficient GPU-based simulations of the tight-binding Bogoliubov-de Gennes equations we solve self-consistently for the pair correlation in rhombohedral (ABC) and Bernal (ABA) multilayer graphene by considering a finite intrinsic s-wave pairing potential. We find that the two different stacking configurations have opposite bulk/surface behavior for the order parameter. Surface superconductivity is robust for ABC stacked multilayer graphene even at very low pairing potentials for which the bulk order parameter vanishes, in agreement with a recent analytical approach. In contrast, for Bernal stacked multilayer graphene, we find that the order parameter is always suppressed at the surface and that there exists a critical value for the pairing potential below which no superconducting order is achieved. We considered different doping scenarios and find that homogeneous doping…
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