Ab initio modelling of a bilayer graphene

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Using the electron density functional theory, numerical modelling of bilayer graphene has been performed. The structure and binding energy of the layers depending on the representation of the wave function of the system have been studied: plane waves (VASP package) and atomic-like orbitals (SIESTA package); and choice of approximation for the exchange-correlation (XC) functional. It has been shown that being in the free form the system creates a stable AB structure of bilayer graphene. The calculation of the layer binding energy has shown that the results of modelling performed with different basis for the wave function are consistent when using XC functionals corresponding to each other and considering the correction to the basis set superposition error in the tight binding method. As expected, the generalized gradient approximation (GGA) has shown underestimated values of the interaction energy of graphene layers. Comparison with experimental data has shown that the energy and geometric characteristics of bilayer graphene are best described by the local electron density approximation (LDA). The 2nd and 3rd generation semi-empirical Grimme corrections for GGA have given estimates of the binding energy higher than LDA, but also close to the experimental results.

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Bilayer graphene, density functional theory, ab initio modelling, binding energy

Короткий адрес: https://sciup.org/147237462

IDR: 147237462

Список литературы Ab initio modelling of a bilayer graphene

  • Lee J.-K., Lee S.-Ch., Ahn J.-P., Kima S.-C., Wilson J.I.B., John P. The growth of A graphite on (111) diamond. The Journal of Chemical Physics, 2008, Vol. 129, Iss. 23, p. 234709. DOI: 10.1063/1.2975333
  • Liu Z., Suenaga K., Harris P.J.F., Iijima S. Open and Closed Edges of Graphene Layers. Physical Review Letters, 2009, Vol. 102, Iss. 1, p. 015501. DOI: 10.1103/physrevlett.102.015501
  • Lee, J.K., Kim, J.-G., Hembram, K., Kim, Y., Min, B.-K., Park, Y., Lee, J.-K., Moon, D., Lee, W., Lee, S.-G., John, P. The Nature of Metastable AA' Graphite: Low Dimensional Nano- and Single-Crystalline Forms. Scientific Reports, 2016, Vol. 6, p. 39624. D0I:10.1038/srep39624
  • McCann E., Koshino M. The electronic properties of bilayer graphene. Reports on Progress in Physics, 2013, Vol. 76, no. 5, p. 056503 (28pp). DOI: 10.1088/0034-4885/76/5/056503
  • Rozhkov A.V., Sboychakov A.O., Rakhmanov A.L., Franco Nori. Electronic properties of graphene-based bilayer systems. Physics Reports, 2016, Vol. 648, pp. 1-104. DOI: 10.1016/j.physrep.2016.07.003
  • Dai S., Xiang Y., Srolovitz D.J. Twisted Bilayer Graphene: Moire with a Twist. Nano Letters, 2016, Vol. 16, Iss. 9, pp. 5923-5927. DOI: 10.1021/acs.nanolett.6b02870
  • Havener R.W., Zhuang H., Brown L., Hennig R.G., Park J. Angle-Resolved Raman Imaging of Interlayer Rotations and Interactions in Twisted Bilayer Graphene. Nano Letters, 2012, Vol. 12, Iss. 6, pp. 3162-3167. DOI: 10.1021/nl301137k
  • Soler J.M., Artacho E., Gale J.D., García A., Junquera J., Ordejón P., Sánchez-Portal D. The SIESTA method for ab initio order-N materials simulation. Journal of Physics: Condensed Matter, 2002, Vol. 14, no. 11, pp. 2745-2779. DOI: 10.1088/0953-8984/14/11/302
  • Kresse G. F.J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Physical Review B, 1996, Vol. 54, Iss. 16, pp. 11169-11186. DOI: 10.1103/PhysRevB.54.11169
  • Ceperley D.M., Alder B.J. Ground State of the Electron Gas by a Stochastic Method. Physical Review Letters, 1980, Vol. 45, Iss. 7, pp. 566-569. DOI: 10.1103/PhysRevLett.45.566
  • Perdew J.P., Burke K., Ernzerhof M. Generalized Gradient Approximation Made Simple. Physical Review Letters, 1996, Vol. 77, no. 18, pp. 3865-3868. DOI: 10.1103/PhysRevLett.78.1396
  • Grimme S. Semiempirical GGA-type density functional constructed with a long-range dispersion correction. Journal of computational chemistry, 2006, Vol. 27, Iss. 15. pp. 1787-1799. DOI: 10.1002/jcc.20495
  • Grimme S., Antony J., Ehrlich S., Krieg H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys., 2010, Vol. 132, no. 15, p. 154104. DOI: 10.1063/1.3382344
  • Abinit's Fritz-Haber-Institute (FHI) pseudo database. URL: https://departments.icmab.es/ leem/SIESTA_MATERIAL/Databases/Pseudopotentials/periodictable-intro.html.
  • P. Rivero, V.M. Garcia-Suarez, D. Pereniguez et al.. Systematic pseudopotentials from reference eigenvalue sets for DFT calculations: Pseudopotential files. Data in Brief, 2015, Vol. 3, pp. 21-23. DOI: 10.1016/j .dib.2014.12.005
  • Anikina E.V., Beskachko V.P. Optimizatsiya parametrov bazisnogo nabora dlya modelirovaniya adsorbtsii vodoroda na uglerodnykh metananotrubkakh v pakete SIESTA (Optimization of the parameters of the basic set for modeling hydrogen adsorption on carbon nanotubes in the SIESTA package). Materialy devyatoy nauchnoy konferentsii aspirantov i doktorantov, Chelyabinsk. 2017 (Proc. Ninth Scientific Conference of Postgraduates and Doctoral Students, Chelyabinsk, 2017), pp. 126-134.
  • Boys S.F., Bernardi F. The calculation of small molecular interactions by the differences of separate total energies. Some procedures with reduced errors. Molecular Physics, 1970, Vol. 19, Iss. 4, pp. 553-566. DOI: 10.1080/00268977000101561
  • Mostaani E., Drummond N.D. Fal'ko V.-I. Quantum Monte Carlo calculation of the binding energy of bilayer graphene. Physical Review Letters, 2015, Vol. 115, Iss. 11, pp. 115501. DOI: 10.1103/physrevlett.115.115501
  • Liu Z., Liu J.Z., Cheng Y., Li Z., Wang L., Zheng Q. Interlayer binding energy of graphite: a mesoscopic determination from deformation. Physical Review B., 2012, Vol. 85, Iss. 20, p. 205418. DOI: 10.1103/physrevb.85.205418
  • Lebegue S., Harl J., Gould T., Angyan J.G., Kresse G., Dobson J.F. Cohesive properties and asymptotics of the dispersion interaction in graphite by the random phase approximation. Physical Review Letters, 2010, Vol. 105, Iss. 19, p. 196401. DOI: 10.1103/physrevlett.105.196401
  • Kostenetskiy P., Semenikhina P. SUSU Supercomputer Resources for Industry and fundamental Science. 2018 Global Smart Industry Conference (GloSIC), 2018, pp. 1-7. DOI: 10.1109/GloSIC.2018.8570068
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