Generating extreme quantum scattering in graphene with machine learning

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.

arXiv comment16 pages, 8 figures

References in corpus (33)

• Electric Field Effect in Atomically Thin Carbon Films
• The electronic properties of graphene
• Two-Dimensional Gas of Massless Dirac Fermions in Graphene
• Energy Gaps in Graphene Nanoribbons
• Chiral tunneling and the Klein paradox in graphene
• Outlook for inverse design in nanophotonics
• Valley filter and valley valve in graphene
• Achieving transparency with plasmonic coatings
• Graphene Spintronics
• Electronic States of Graphene Nanoribbons
• A Generative Model for Inverse Design of Metamaterials
• Dirac materials
• Colloquium: Spintronics in graphene and other two-dimensional materials
• The Rare Two-Dimensional Materials with Dirac Cones
• Global optimization of dielectric metasurfaces using a physics-driven neural network
• Freeform Diffractive Metagrating Design Based on Generative Adversarial Networks
• Cloaking of Matter Waves
• Elastic scattering theory and transport in graphene
• Imaging electrostatically confined Dirac fermions in graphene quantum dots
• Creating and Probing Electron Whispering Gallery Modes in Graphene
• Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
• An On/Off Berry Phase Switch in Circular Graphene Resonators
• On Learning Hamiltonian Systems from Data
• Physics enhanced neural networks predict order and chaos
• Nanoscale control of rewriteable doping patterns in pristine graphene/boron nitride heterostructures
• Quantum gate learning in engineered qubit networks: Toffoli gate with always-on interactions
• Adaptable Hamiltonian neural networks
• Design of Ultra-compact Graphene-based Superscatterers
• Deep-Learning-Enabled Inverse Engineering of Multi-Wavelength Invisibility-to-Superscattering Switching with Phase-Change Materials
• Imaging Quantum Interference in Stadium-Shaped Monolayer and Bilayer Graphene Quantum Dots
• Tomography of time-dependent quantum spin networks with machine learning
• Cloaking Resonant Scatterers and Tuning Electron Flow in Graphene
• The transfer matrix approach to circular graphene quantum dots

Cited by in corpus (2)

• Optical properties of two dimensional Dirac Weyl materials with a flatband
• Deep-learning design of graphene metasurfaces for quantum control and Dirac electron holography