Flat-band superconductivity in periodically strained graphene : mean-field and Berezinskii–Kosterlitz–Thouless transition
Peltonen, T. J., & Heikkilä, T. T. (2020). Flat-band superconductivity in periodically strained graphene : mean-field and Berezinskii–Kosterlitz–Thouless transition. Journal of Physics: Condensed Matter, 32(36), Article 365603. https://doi.org/10.1088/1361-648X/ab8b9d
Published inJournal of Physics: Condensed Matter
© 2020 IOP Publishing Ltd.
In the search of high-temperature superconductivity one option is to focus on increasing the density of electronic states. Here we study both the normal and s-wave superconducting state properties of periodically strained graphene, which exhibits approximate flat bands with a high density of states, with the flatness tunable by the strain profile. We generalize earlier results regarding a one-dimensional harmonic strain to arbitrary periodic strain fields, and further extend the results by calculating the superfluid weight and the Berezinskii–Kosterlitz–Thouless (BKT) transition temperature T BKT to determine the true transition point. By numerically solving the self-consistency equation, we find a strongly inhomogeneous superconducting order parameter, similarly to twisted bilayer graphene. In the flat-band regime the order parameter magnitude, critical chemical potential, critical temperature, superfluid weight, and BKT transition temperature are all approximately linear in the interaction strength, which suggests that high-temperature superconductivity might be feasible in this system. We especially show that by using realistic strain strengths T BKT can be made much larger than in twisted bilayer graphene, if using similar interaction strengths. We also calculate properties such as the local density of states that could serve as experimental fingerprints for the presented model. ...
PublisherInstitute of physics
Publication in research information system
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Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF
Additional information about fundingT.J.P. acknowledges funding from the Emil Aaltonen Foundation and T.T.H. from the Academy of Finland via its project number 317118. We acknowledge grants of computer capacity from the Finnish Grid and Cloud Infrastructure (persistent identifier urn:nbn:fi:research-infras-2016072533).
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