Third nearest neighbor parameterized tight binding model for graphene nano-ribbons

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

Accurate modeling of the S-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-p z orbital approach fails on both these counts. To overcome these failures we introduce a nearest-neighbor, three orbital per atom p/d tight-bin...

Scalable tight-binding model for graphene.

Artificial graphene consisting of honeycomb lattices other than the atomic layer of carbon has been shown to exhibit electronic properties similar to real graphene. Here, we reverse the argument to show that transport properties of real graphene can be captured by simulations using "theoretical artificial graphene." To prove this, we first derive a simple condition, along with its restrictions,...

A Parallel Sparse Linear Solver for Nearest-Neighbor Tight-Binding Problems

A parallel sparse linear solver for nearest-neighbor tight-binding problems" (2008).

Diagonal parameter shifts due to nearest-neighbor displacements in empirical tight-binding theory

Nanoscale heterostructures are generally characterized by local strain variations. Because the atoms in such systems can be irregularly positioned, theroretical models and parameterizations that are restricted to hydrostatic and uniaxial strain are generally not applicable. To address this shortcoming, a method that enables the incorporation of general distortions into the empirical tight bindi...

Localised States of Fabry-Perot Type in Graphene Nano-Ribbons