arXiv subjects

L. A. Silva

Publications and source records attributed to L. A. Silva.

2 recordsLinked to original sources

arXiv2025

Layer-Resolved Quantum Transport in Twisted Bilayer Graphene: Counterflow and Machine Learning Predictions

The layer-resolved quantum transport response of a twisted bilayer graphene device is investigated by driving a current through the bottom layer and measuring the induced voltage in the top layer. Devices with four- and eight-layer differentiated contacts were analyzed, revealing that in a nanoribbon geometry (four contacts), a longitudinal counterflow current emerges in the top layer, while in a square-junction configuration (eight contacts), this counterflow is accompanied by a transverse, or Hall, component. These effects persist despite weak coupling to contacts, onsite disorder, lattice relaxation and variations in device size. The observed counterflow response indicates a circulating interlayer current, which generates an in-plane magnetic moment excited by the injected current. Finally, due to the intricate relationship between the electrical layer response, energy, and twist angle, a clusterized machine learning model was trained, validated, and tested to predict various conductances.

cond-mat.mes-hall↗

arXiv2019

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

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.

cond-mat.mes-hall↗