arXiv subjects

Elena Trukhan

Publications and source records attributed to Elena Trukhan.

4 recordsLinked to original sources

arXiv2026

When heat goes astray -- non-local heating in a semiconductor

Heating of semiconductor devices limits their performance and lifetime, which must be addressed by thermal management starting at the heat source. It is a common assumption that the heat source and the resulting heat spot locally coincide, if their size exceeds the mean free paths of the main heat carriers, the phonons. We show that this paradigm of heat locality breaks down on length scales spanning several micrometers. As a consequence, non-local heating occurs in contradiction to Fourier's law. Therefore, we heat laterally structured semiconductor membranes that feature a rising number of interfaces with a well-focussed laser and map-out lattice temperatures by Raman thermometry. Remarkably, the non-local heating can exceed the laser-induced local heating, which we attribute to ballistic phonon transport far above cryogenic temperatures.

cond-mat.mes-hall↗

arXiv2026

Ab initio study of Coulomb drag driven electron-hole bifluidity in doped graphene

Motivated by the notion that a preponderance of Coulomb interactions might lead to hydrodynamics, we carry out an ab initio calculation of the charge carrier transport properties of the electron-hole plasma of doped graphene. We include both the phonon and Coulomb interactions within a momentum and band resolved Boltzmann transport formalism. We find that, under suitable conditions, the strong Coulomb drag effect induces effects like negative conductivity and joint electron-hole hydrodynamics (bifluidity) in the plasma. We also identify the exclusive electron or hole hydrodynamics. We find that there is a strong violation of the Wiedemann-Franz law in the low doped regimes. Our work elucidates the roles of the microscopic scattering mechanisms that drive these hydrodynamic phenomena.

cond-mat.mtrl-sci↗

arXiv2025

Interband and kinetic corrections to the electronic Boltzmann transport equation

Interband effects such as coherence/tunneling have recently been shown to give an important contribution to the charge and heat transport properties under certain conditions. These can be captured by adding corrective terms to the semiclassical Boltzmann transport equation. In recent derivations of this type of transport equations that are based on the density matrix formalism, there remain, however, certain omissions. These derivations also rely on a particular type of relaxation time approximation and a band-diagonal form of the interaction self-energies. In this work we derive the interband terms of the electronic Boltzmann transport equation starting from the Keldysh formulation of the quantum kinetic equation and considering the band non-diagonality of the electron-impurity and electron-phonon self-energies. We introduce a minimally modified Kadanoff-Baym Ansatz, and find a quantum-corrected, matrix Boltzmann transport equation that is well beyond the current state of the art theory. We show that the occupations and coherences are interdependent, and that the kinetic corrections due to the included interactions cannot, in general, be ignored. This work clarifies the various approximations that must be introduced in an ab initio derivation of Boltzmann-like equations and finds a new matrix Boltzmann transport equation that is suitable for parameters-free numerical implementations.

cond-mat.mtrl-sci↗

arXiv2025

Acceleration of crystal structure relaxation with Deep Reinforcement Learning

We introduce a Deep Reinforcement Learning (DRL) model for the structure relaxation of crystal materials and compare different types of neural network architectures and reinforcement learning algorithms for this purpose. Experiments are conducted on Al-Fe structures, with potential energy surfaces generated using EAM potentials. We examine the influence of parameter settings on model performance and benchmark the best-performing models against classical optimization algorithms. Additionally, the model's capacity to generalize learned interaction patterns from smaller atomic systems to more complex systems is assessed. The results demonstrate the potential of DRL models to enhance the efficiency of structure relaxation compared to classical optimizers.

cond-mat.mtrl-sci↗