arXiv subjects

Max Fischer

Publications and source records attributed to Max Fischer.

3 recordsLinked to original sources

arXiv2025

Fragility of local moments against hybridization with flat bands

The Kondo screening of a localized magnetic moment crucially depends on the spectral properties of the electronic bath to which it is coupled. Unlike textbook examples, realistic systems as well as dynamical mean-field theory of correlated lattice models force us to consider sharp features in the hybridization function near the Fermi energy. Divergencies of this kind can play a relevant role in twisted bilayer graphene, for which local-moment formation and isospin entropy at finite temperature are currently under the spotlight. We clarify how a low-frequency singularity impacts the screening mechanisms by means of a toy model with a tunable $\delta$-peak in the hybridization function, superimposed to a regular part. Our analysis unveils an unexpectedly big impact on the local-moment physics already for a parametrically small weight of the flat band in the bath.

cond-mat.str-el↗

arXiv2023

Interacting nodal semimetals with non-linear bands

We investigate the quasi-particle and transport properties of a model describing interacting Dirac and Weyl semimetals in the presence of local Hubbard repulsion $U$, where we explicitly include a deviation from the linearity of the energy-momentum dispersion through an intermediate-energy scale $\Lambda$. Our focus lies on the correlated phase of the semimetal. At the nodal point, the renormalization of spectral weight at a fixed temperature $T$ exhibits a weak dependence on $\Lambda$ but is sensitive to the proximity to the Mott transition. Conversely, the scattering rate of quasi-particles and the resistivity display high-temperature exponents that crucially rely on $\Lambda$, leading to a crossover towards a conventional Fermi-liquid behaviour at finite T. Finally, by employing the Nernst-Einstein relation for conductivity, we identify a corresponding density crossover as a function of the chemical potential.

cond-mat.str-el↗

arXiv2021

Detecting Slag Formations with Deep Convolutional Neural Networks

We investigate the ability to detect slag formations in images from inside a Grate-Kiln system furnace with two deep convolutional neural networks. The conditions inside the furnace cause occasional obstructions of the camera view. Our approach suggests dealing with this problem by introducing a convLSTM-layer in the deep convolutional neural network. The results show that it is possible to achieve sufficient performance to automate the decision of timely countermeasures in the industrial operational setting. Furthermore, the addition of the convLSTM-layer results in fewer outlying predictions and a lower running variance of the fraction of detected slag in the image time series.

cs.CV↗