Valley notch filter in a graphene strain superlattice: Green's function and machine learning approach

Autor:	Torres, V., Silva, P., de Souza, E. A. T., Silva, L. A., Bahamon, D. A.
Rok vydání:	2019
Předmět:	Condensed Matter - Mesoscale and Nanoscale Physics Condensed Matter - Disordered Systems and Neural Networks
Zdroj:	Phys. Rev. B 100, 205411 (2019)
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevB.100.205411
Popis:	The valley transport properties of a superlattice of out-of-plane Gaussians deformations are calculated using a Green's function and a Machine Learning approach. Our results show that periodicity significantly improves the valley filter capabilities of a single Gaussian deformation, these manifest themselves in the conductance as a sequence by valley filter plateaus. We establish that the physical effect behind the observed valley notch filter is the coupling between counter-propagating transverse modes; the complex relationship between the design parameters of the superlattice and the valley filter effect make difficult to estimate in advance the valley filter potentialities of a given superlattice. With this in mind, we show that a Deep Neural Network can be trained to predict valley polarization with a precision similar to the Green's function but with much less computational effort. Comment: 11 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1908.04604 Zobrazit plný text záznamu View this record from Arxiv