Multiscale Green's functions for modeling graphene and other Xenes

Published

January 27, 2023

Author(s)

Vinod Tewary, Edward Garboczi

Abstract

We give a review of the multiscale Green's function method for modeling modern two-dimensional nanomaterials such as graphene and other Xenes. The method is applicable to materials at different space and time scales and is computationally efficient. This method is actually a generalized Green's function, which gives the response of a multiparticle system to a probe and is a powerful technique for solving a variety of problems in science and engineering. In the static case, the method can seamlessly link the length scales from atomistic to continuum in an integrated formalism. The multiscale nature of the static Green's function is largely determined by the underlying Bravais lattice of a solid, which is the same for graphene and other Xenes. This allows us to treat graphene as a special case of Xenes. For time-dependent problems, we calculate a causal Green's function by using the Laplace transform technique, which accounts for causality. It can simulate physical processes in a multiparticle system over a wide range of time extending from femtoseconds to microseconds. For illustration, the static method is applied to simulate elastic deformation due to a point defect, such as a monovacancy, in graphene and silicene. For time-dependent problems, we illustrate the causal Green's function method to simulate propagation of elastic waves in two-dimensional graphene.

Citation

Modeling, Characterization and Production of Nanomaterials

Publisher Info

Elsevier, New York, NY

Pub Type

Book Chapters

Download Paper

https://doi.org/10.1016/B978-0-12-819905-3.00005-1

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Keywords

extending time scales, graphene, lattice defects, lattice statics, multiscale Green’s functions, silicene, strain field, Xenes

Citation

Tewary, V. and Garboczi, E. (2023), Multiscale Green's functions for modeling graphene and other Xenes, Modeling, Characterization and Production of Nanomaterials, Elsevier, New York, NY, [online], https://doi.org/10.1016/B978-0-12-819905-3.00005-1, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933474 (Accessed November 21, 2024)