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

Abstract

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.

11 pages, 9 figures

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Cited by in corpus (15)

• Forward and Inverse Design of Kirigami via Supervised Autoencoder
• Dispersive Landau levels and valley currents in strained graphene nanoribbons
• Valley Hall Effect and Non-Local Resistance in Locally Gapped Graphene
• Valley polarization braiding in strained graphene
• Graphene nanodrums as valleytronic devices
• Valley current generation using biased bilayer graphene dots
• Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution
• Electron transport properties of graphene nanoribbons with Gaussian deformation
• Deep learning of deformation-dependent conductance in thin films: nanobubbles in graphene
• Tunable valley filtering in dynamically strained $\alpha$-${\mathcal{T}}_3$ lattices
• Switching valley filtered current directions in multi-terminal graphene systems
• Flat bands and superconductivity induced by periodic strain in monolayer graphene
• Predicting magnetic edge behaviour in graphene using neural networks
• Neural network-based recognition of multiple nanobubbles in graphene
• Layer-Resolved Quantum Transport in Twisted Bilayer Graphene: Counterflow and Machine Learning Predictions