Dihedral-angle-corrected registry-dependent interlayer potential for multilayer graphene structures

Mingjian Wen, Stephen Carr, Shiang Fang, Efthimios Kaxiras, Ellad B. Tadmor

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16 Scopus citations

Abstract

The structural relaxation of multilayer graphene is essential in describing the interesting electronic properties induced by intentional misalignment of successive layers, including the recently reported superconductivity in twisted bilayer graphene. This is difficult to accomplish without an accurate interatomic potential. Here, we present a new, registry-dependent Kolmogorov-Crespi-type interatomic potential to model interlayer interactions in multilayer graphene structures. It consists of two parts, representing attractive interaction due to dispersion and repulsive interaction due to anisotropic overlap of electronic orbitals. An important new feature is a dihedral-angle-dependent term that is added to the repulsive part to describe correctly several distinct stacking states that the original Kolmogorov-Crespi potential cannot distinguish. We refer to the new model as the dihedral-angle-corrected registry-dependent interlayer potential (DRIP). Computations for several test problems show that DRIP correctly reproduces the binding, sliding, and twisting energies and forces obtained from ab initio total-energy calculations based on density-functional theory. We use the new potential to study the structural properties of a twisted graphene bilayer and the exfoliation of graphene from graphite. Our potential is available through the OpenKIM interatomic potential repository at https://openkim.org.

Original languageEnglish (US)
Article number235404
JournalPhysical Review B
Volume98
Issue number23
DOIs
StatePublished - Dec 4 2018

Bibliographical note

Funding Information:
This research was partly supported by the Army Research Office (W911NF-14-1-0247) under the MURI program, and the National Science Foundation (NSF) under Grants No. DMR-1408211 and No. DMR-1408717. The authors wish to acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the results reported in this paper. M.W. thanks the University of Minnesota Doctoral Dissertation Fellowship for supporting his research. The authors thank Alexey Kolmogorov for reading the paper and for his valuable comments.

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