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

Torres V.; Silva P.; De Souza E.A.T.; Silva L.A.; Bahamon D.A.

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

Tipo

Artigo

Data de publicação

2019

Periódico

Physical Review B

Citações (Scopus)

19

Autores

Torres V.
Silva P.
De Souza E.A.T.
Silva L.A.
Bahamon D.A.

Resumo

© 2019 American Physical Society.The valley transport properties of a superlattice of out-of-plane Gaussian 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 counterpropagating transverse modes; the complex relationship between the design parameters of the superlattice and the valley filter effect make it 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.

Assuntos Scopus

Complex relationships , Computational effort , Counterpropagating , Design parameters , Filter effects , Machine learning approaches , Physical effects , Transverse mode