Generating extreme quantum scattering in graphene with machine learning
This addresses the challenge of designing quantum-dot structures for applications such as cloaking or superscattering in 2D Dirac materials, which is computationally infeasible with brute-force methods, though it is incremental as it applies existing machine-learning techniques to a specific domain problem.
The authors tackled the inverse-design problem of finding quantum-dot structures in graphene to achieve desired scattering behaviors like cloaking and superscattering, using a machine-learning approach with physical constraints, and demonstrated that scattering efficiency can be varied over two orders of magnitude in the Klein tunneling regime.
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.