Deep neural networks for inverse problems in mesoscopic physics: Characterization of the disorder configuration from quantum transport properties
Gaëtan J. Percebois, Dietmar Weinmann
Abstract
We present a machine-learning approach that allows to characterize the disorder potential of a two-dimensional electronic system from its quantum transport properties. Numerically simulated transport data for a large number of disorder configurations are used for the training of artificial neural networks. We show that the trained networks are able to recognize details of the disorder potential of an unknown sample from its transport properties, and that they can even reconstruct the complete potential landscape seen by the electrons.
Topics & Concepts
Mesoscopic physicsArtificial neural networkQuantumCharacterization (materials science)Artificial intelligenceComputer scienceSample (material)ElectronStatistical physicsInverse problemPhysicsMaterials scienceNanotechnologyQuantum mechanicsMathematicsMathematical analysisThermodynamicsMachine Learning in Materials ScienceAdvancements in Semiconductor Devices and Circuit DesignElectronic and Structural Properties of Oxides