Decoding the conductance of disordered nanostructures: a quantum inverse problem
arXiv:2111.15441 · doi:10.1088/1361-648X/ac3a85
Abstract
Obtaining conductance spectra for a concentration of disordered impurities distributed over a nanoscale device with sensing capabilities is a well-defined problem. However, to do this inversely, i.e., extracting information about the scatters from the conductance spectrum alone, is not an easy task. In the presence of impurities, even advanced techniques of inversion can become particularly challenging. This article extends the applicability of a methodology we proposed capable of extracting composition information about a nanoscale sensing device using the conductance spectrum. The inversion tool decodes the conductance spectrum to yield the concentration and nature of the disorders responsible for conductance fluctuations in the spectra. We present the method for simple one-dimensional systems like an electron gas with randomly distributed delta functions and a linear chain of atoms. We prove the generality and robustness of the method using materials with complex electronic structures like hexagonal boron nitride, graphene nanoribbons, and carbon nanotubes. We also go on to probe the distribution of disorders on the sublattice structure of the materials using the proposed inversion tool.
arXiv comment16pages, 4 figures
References in corpus (24)
- Machine learning phases of matter
- The Statistical Theory of Quantum Dots
- Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds
- Mesoscopic Transport Through Ballistic Cavities: A Random S-Matrix Theory Approach
- Insightful classification of crystal structures using deep learning
- Quantum Machine Learning for Electronic Structure Calculations
- Design and discovery of a novel Half-Heusler transparent hole conductor made of all-metallic heavy elements
- Artificial Neural Network Approach to the Analytic Continuation Problem
- Computational inverse method for constructing spaces of quantum models from wave functions
- Numerical Methods for the Inverse Problem of Density Functional Theory
- Modeling Electronic Quantum Transport with Machine Learning
- Genetic Design of Enhanced Valley Splitting towards a Spin Qubit in Silicon
- Bayesian optimization for the inverse scattering problem in quantum reaction dynamics
- Dynamic stabilization of a quantum many-body spin system
- Quantum inverse iteration algorithm for programmable quantum simulators
- Inverse Problem Instabilities in Large-Scale Plasma Modelling
- Efficient linear-scaling quantum transport calculations on graphics processing units and applications on electron transport in graphene
- Statistical and computational intelligence approach to analytic continuation in Quantum Monte Carlo
- Phononic heat transport in the transient regime: An analytic solution
- Tree based machine learning framework for predicting ground state energies of molecules
- Disorder information from conductance: a quantum inverse problem
- Green Functions of Graphene: An Analytic Approach
- Decoding the DC and optical conductivities of disordered MoS films: an inverse problem
- An inverse problem from condense matter physics