Valley notch filter in a graphene strain superlattice: Green's function and machine learning approach
| dc.contributor.author | Torres V. | |
| dc.contributor.author | Silva P. | |
| dc.contributor.author | De Souza E.A.T. | |
| dc.contributor.author | Silva L.A. | |
| dc.contributor.author | Bahamon D.A. | |
| dc.date.accessioned | 2024-03-12T23:51:07Z | |
| dc.date.available | 2024-03-12T23:51:07Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | © 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. | |
| dc.description.issuenumber | 20 | |
| dc.description.volume | 100 | |
| dc.identifier.doi | 10.1103/PhysRevB.100.205411 | |
| dc.identifier.issn | 2469-9969 | |
| dc.identifier.uri | https://dspace.mackenzie.br/handle/10899/35157 | |
| dc.relation.ispartof | Physical Review B | |
| dc.rights | Acesso Restrito | |
| dc.title | Valley notch filter in a graphene strain superlattice: Green's function and machine learning approach | |
| dc.type | Artigo | |
| local.scopus.citations | 19 | |
| local.scopus.eid | 2-s2.0-85075378413 | |
| local.scopus.subject | Complex relationships | |
| local.scopus.subject | Computational effort | |
| local.scopus.subject | Counterpropagating | |
| local.scopus.subject | Design parameters | |
| local.scopus.subject | Filter effects | |
| local.scopus.subject | Machine learning approaches | |
| local.scopus.subject | Physical effects | |
| local.scopus.subject | Transverse mode | |
| local.scopus.updated | 2024-05-01 | |
| local.scopus.url | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85075378413&origin=inward |