Generating extreme quantum scattering in graphene with machine learning

TL;DR

This paper introduces a physics-informed machine learning method to inverse-design quantum dot structures in graphene, enabling control over scattering behaviors like cloaking and superscattering, which was previously computationally infeasible.

Contribution

The paper presents a novel neural network approach that replaces traditional Dirac equation solvers for inverse design of quantum dots in graphene, focusing on scattering characteristics.

Findings

Scattering efficiency varies over two orders of magnitude in the Klein tunneling regime.

The method accurately predicts scattering characteristics based on gate potentials.

Any desired scattering curve can be generated through proper potential combinations.

Abstract

Graphene quantum dots provide a platform for manipulating electron behaviors in two-dimensional (2D) Dirac materials. Most previous works were of the "forward" type in that the objective was to solve various confinement, transport and scattering problems with given structures that can be generated by, e.g., applying an external electrical field. There are applications such as cloaking or superscattering where the challenging problem of inverse design needs to be solved: finding a quantum-dot structure according to certain desired functional characteristics. A brute-force search of the system configuration based directly on the solutions of the Dirac equation is computational infeasible. We articulate a machine-learning approach to addressing the inverse-design problem where artificial neural networks subject to physical constraints are exploited to replace the rigorous Dirac equation solver. In particular, we focus on the problem of designing a quantum dot structure to generate both cloaking and superscattering in terms of the scattering efficiency as a function of the energy. We construct a physical loss function that enables accurate prediction of the scattering characteristics. We demonstrate that, in the regime of Klein tunneling, the scattering efficiency can be designed to vary over two orders of magnitudes, allowing any scattering curve to be generated from a proper combination of the gate potentials. Our physics-based machine-learning approach can be a powerful design tool for 2D Dirac material-based electronics.