arXiv subjects

Gaëtan J. Percebois

Publications and source records attributed to Gaëtan J. Percebois.

3 recordsLinked to original sources

arXiv2023

Reconstructing the potential configuration in a high-mobility semiconductor heterostructure with scanning gate microscopy

The weak disorder potential seen by the electrons of a two-dimensional electron gas in high-mobility semiconductor heterostructures leads to fluctuations in the physical properties and can be an issue for nanodevices. In this paper, we show that a scanning gate microscopy (SGM) image contains information about the disorder potential, and that a machine learning approach based on SGM data can be used to determine the disorder. We reconstruct the electric potential of a sample from its experimental SGM data and validate the result through an estimate of its accuracy.

cond-mat.mes-hall↗

arXiv2022

Spontaneous orbital magnetization of mesoscopic dipole dimers

Ensembles of gold nanoparticles present a magnetic behavior which is at odds with the weakly diamagnetic response of bulk gold. In particular, an unusual ferromagnetic order has been unveiled by several experiments. Here we investigate if the combined effect of orbital magnetism of conduction electrons and interparticle dipolar interaction can lead to magnetic ordering. Using different model systems of interacting mesoscopic magnetic dipoles, together with a microscopic description of the electron dynamics within the nanoparticles, we find that a spontaneous magnetic moment may arise in dimers of metallic nanoparticles when the latter are characterized by a large orbital paramagnetic susceptibility.

cond-mat.mes-hall↗

arXiv2021

Deep neural networks for inverse problems in mesoscopic physics: Characterization of the disorder configuration from quantum transport properties

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 is 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.

cond-mat.dis-nn↗