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 transmission with a precision similar to the Green's function but with much less computational effort.

Forward and Inverse Design of Kirigami via Supervised Autoencoder

“…As online databases for mechanical systems, such as the mechanical MNIST database [32], are developed, our model will be important for learning the underlying physics in a reduced-dimensional space, as well as for proposing novel designs. Moreover, as the local structures are tightly connected to electronic properties, this method can be extended for learning electronic properties in 2D materials, such as pseudomagnetic and electric polarization, as a function of defects or kirigami cut patterns [33][34][35][36][37][38]. P. Z. H. developed the codes and machine learning methods, performed the simulations and data analysis, and wrote the manuscript with input from all authors.…”

Predicting magnetic edge behaviour in graphene using neural networks

“…Such methods have been used to estimate ab-initio figures-of-merit for materials design [44,45], reproduce the mapping performed by functionals in complex many-body models [46], and predict critical behaviour in lattice models [47,48]. In graphene systems, ML approaches have been employed to directly predict transport properties in the presence of disorder [49], strain [50,51] or doping [52].…”

Switching valley filtered current directions in multi-terminal graphene systems

“…One significant achievement in this context was the development of a 4-kink valley polarized router device based on graphene bilayers [10]. Moreover, several deformed graphene systems have been explored as strategic set-ups to modulate electronic responses [11,12,13,14,15,16,17,18,19,20]. In particular, valley-splitting occurrence and valley-inversion were proven possible experimentally in a graphene quantum dot induced by the tip of a scanning tunneling microscope (STM) in strained graphene regions [21,22].…”

“…Depending on the deformation profile, it is possible to generate valley polarized local density of states (LDOS) [3,12] in regions that work as wave-guides for polarized currents [11,25,26]. Other works have explored valley filtered currents in graphene considering the valley spatial-separation combined with different mechanisms, such as edge disorder, strain superlattices, external magnetic fields, and multi-terminal configurations [11,13,16,27]. For example, graphene superlattices designed by out-of-plane Gaussian deformations are shown to improve the valley filter capabilities of a single perturbation, with the conductance exhibiting a sequence of valley filtered plateaux [16].…”