Study of simulated Bloch oscillations in strained graphene using neural networks

TL;DR

This study uses neural networks to classify strain in graphene by analyzing simulated Bloch oscillations, achieving high accuracy in identifying strain components and Poisson ratio under various electric field orientations.

Contribution

It introduces a neural network-based method to classify strain and Poisson ratio in graphene from simulated Bloch oscillation data, demonstrating high accuracy across different electric field orientations.

Findings

Neural network classifies strain with up to 90% accuracy for specific orientations.

Achieves up to 97% accuracy in classifying strain and Poisson ratio for arbitrary orientations.

Error margins in predictions range from ±1% to ±25% depending on the parameter and orientation.

Abstract

We consider a monolayer of graphene under uniaxial, tensile strain and simulate Bloch oscillations for different electric field orientations parallel to the plane of the monolayer using several values of the components of the uniform strain tensor, but keeping the Poisson ratio in the range of observable values. We analyze the trajectories of the charge carriers with different initial conditions using an artificial neural network, trained to classify the simulated signals according to the strain applied to the membrane. When the electric field is oriented either along the Zig-Zag or the Armchair edges, our approach successfully classifies the independent component of the uniform strain tensor with up to 90\% of accuracy and an error of $\pm1\%$ in the predicted value. For an arbitrary orientation of the field, the classification is made over the strain tensor component and the Poisson ratio simultaneously, obtaining up to 97\% of accuracy with an error that goes from $\pm5\%$ to $\pm10\%$ in the strain tensor component and an error from $\pm12.5\%$ to $\pm25\%$ in the Poisson ratio.