Multiflat Bands and Strong Correlations in Twisted Bilayer Boron Nitride: Doping-Induced Correlated Insulator and Superconductor

Nano Letters (2019)

Multiflat Bands and Strong Correlations in Twisted Bilayer Boron Nitride: Doping-Induced Correlated Insulator and Superconductor

Lede Xian,Dante M. Kennes,Nicolas Tancogne-Dejean,Massimo Altarelli, Angel Rubio

Two-dimensional materials, obtained by van der Waals stacking of layers, are fascinating objects of contemporary condensed matter research, exhibiting a variety of new physics. Inspired by the breakthroughs of twisted bilayer graphene (TBG), we demonstrate that twisted bilayer boron nitride (TBBN) is an even more exciting novel system that turns out to be an excellent platform to realize new correlated phases and phenomena; exploration of its electronic properties shows that in contrast to TBG in TBBN multiple families of 2,4, and 6-fold degenerate flat bands emerge without the need to fine tune close to a “magic angle”, resulting in dramatic and tunable changes in optical properties and exciton physics, and providing an additional platform to study strong correlations. Upon doping, unforeseen new correlated phases of matter (insulating and superconducting) emerge. TBBN could thus provide a promising experimental platform, insensitive to small deviations in the twist angle, to study novel exciton condensate and spatial confinement physics, and correlations in two dimensions.

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http://dx.doi.org/10.1021/acs.nanolett.9b00986
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The authors thank Martin Claassen and Mei-Yin Chou for useful discussions. This work was supported by the European Research Council (ERC-2015-AdG694097). The Flatiron Institute is a division of the Simons Foundation. L.X. acknowledges the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement number 709382 (MODHET). D.M.K. acknowledges funding from the Deutsche Forschungsgemeinschaft through the Emmy Noether program (KA 3360/2-1). FRG calculations were performed with computing resources granted by RWTH Aachen University under projects prep0010. We acknowledge computing resources from the Garching supercomputing center of the Max Planck Society as well as Columbia University’s Shared Research Computing Facility project, which is supported by NIH Research Facility Improvement Grant 1G20RR030893-01, and associated funds from the New York State Empire State Development, Division of Science Technology and Innovation (NYSTAR) Contract C090171, both awarded April 15, 2010.

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