Accurate Six-Band Nearest-Neighbor Tight-Binding Model for the π-Bands of Bulk Graphene and Graphene Nanoribbons

Timothy B. Boykin, The University of Alabama, Huntsville
Mathieu Luisier, NCN, Purdue University
Gerhard Klimeck, NCN, Purdue University
Xueping Jiang, Rensselaer Polytechnic Institute
Neerav Kharche, Rensselaer Polytechnic Institute
Yu Zhou, Rensselaer Polytechnic Institute
Saroj K. Nayak, Rensselaer Polytechnic Institute

Date of this Version



Timothy B. Boykin, Mathieu Luisier, Gerhard Klimeck, Xueping Jiang, Neerav Kharche, Yu Zhou, and Saroj K. Nayak. Accurate six-band nearest-neighbor tight-binding model for the π-bands of bulk graphene and graphene nanoribbons. Journal of Applied Physics 109, 104304 (2011); doi:


Copyright (2011) American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Journal of Applied Physics 109, 104304 (2011) and may be found at The following article has been submitted to/accepted by Journal of Applied Physics. Copyright (2011) Timothy B. Boykin, Mathieu Luisier, Gerhard Klimeck, Xueping Jiang, Neerav Kharche, Yu Zhou, and Saroj K. Nayak. This article is distributed under a Creative Commons Attribution 3.0 Unported License.


Accurate modeling of the ␣-bands of armchair graphene nanoribbons (AGNRs) requires correctly reproducing asymmetries in the bulk graphene bands as well as providing a realistic model for hydrogen passivation of the edge atoms. The commonly used single-pz orbital approach fails on both these counts. To overcome these failures we introduce a nearest-neighbor, three orbital per atom p/d tight-binding model for graphene. The parameters of the model are fit to first-principles density-functional theory (DFT) – based calculations as well as to those based on the many-body Green’s function and screened-exchange (GW) formalism, giving excellent agreement with the ab initio AGNR bands. We employ this model to calculate the current-voltage characteristics of an AGNR MOSFET and the conductance of rough-edge AGNRs, finding significant differences versus the single-pz model. These results show that an accurate bandstructure model is essential for predicting the performance of graphene-based nanodevices.


Nanoscience and Nanotechnology