Optimizing quantum transport in multi-barrier graphene systems using differential evolution

TL;DR

This paper introduces a framework that uses differential evolution algorithms combined with transfer matrix methods to optimize electron transmission in multi-barrier graphene systems, enabling highly tunable electronic transport.

Contribution

It presents a novel optimization approach for designing barrier configurations in graphene to control quantum transport with high precision.

Findings

Effective barrier configurations were identified that match target transmission profiles.

Regularization techniques improved the balance between accuracy and complexity.

The method demonstrates potential for highly tunable electronic transport in graphene systems.

Abstract

Potential and mass barriers in graphene introduce electron scattering, modulating transmission probabilities. Complex multi-barrier setups allow electron transmission to be controlled with high precision, but have a huge design space of possible barrier geometries. This work presents a framework to optimize electronic transport in such systems using differential evolution algorithms. First, transfer matrix methods are employed to efficiently compute quantum transport through multi-barrier structures, before optimization is applied to find barrier configurations whose transmission profiles closely match a predefined target profile. To address the trade-off between the accuracy and complexity of resulting barrier configurations, regularization techniques are incorporated into the optimization process. Our approach demonstrates the potential for highly tunable electronic transport in graphene-based systems by exploiting evolution-inspired optimization techniques.