Magnetoresistance of compensated semimetals in confined geometries

Two-component conductors -- e.g., semi-metals and narrow band semiconductors -- often exhibit unusually strong magnetoresistance in a wide temperature range. Suppression of the Hall voltage near charge neutrality in such systems gives rise to a strong quasiparticle drift in the direction perpendicular to the electric current and magnetic field. This drift is responsible for a strong geometrical increase of resistance even in weak magnetic fields. Combining the Boltzmann kinetic equation with sample electrostatics, we develop a microscopic theory of magnetotransport in two and three spatial dimensions. The compensated Hall effect in confined geometry is always accompanied by electron-hole recombination near the sample edges and at large-scale inhomogeneities. As the result, classical edge currents may dominate the resistance in the vicinity of charge compensation. The effect leads to linear magnetoresistance in two dimensions in a broad range of parameters. In three dimensions, the magnetoresistance is normally quadratic in the field, with the linear regime restricted to rectangular samples with magnetic field directed perpendicular to the sample surface. Finally, we discuss the effects of heat flow and temperature inhomogeneities on the magnetoresistance.Comment: 22 pages, 7 figures, published versio

Assessment of the impacts of clear-cutting on soil loss by water erosion in Italian forests: First comprehensive monitoring and modelling approach

Abstract As a member of the European Union, Italy has committed to the maintenance and protection of its forests based on sustainable forest development and management practices. According to Eurostat, Italy has the seventh largest forest surface available for wood supply in the EU-28, which is equal to 8.086 million hectares. For 2012, the Italian National Institute of Statistics estimated the total roundwood production of Italy to be 7.7 million m3, from a harvested forest surface of 61,038 ha. Large parts of the country's forests, mainly located in vulnerable mountainous landscapes that are highly sensitive to environmental changes, are subject to anthropogenic disturbance driven by wood supply interests. Despite the extensive logging activities and the well-known impacts that such management practices have on the soil-related forest ecosystems, there is a lack of spatially and temporally explicit information about the removal of trees. Hence, this study aims to: i) assess the soil loss by water erosion in Italian forest areas, ii) map forest harvests and iii) evaluate the effects of logging activities in terms of soil loss by means of comprehensive remote sensing and GIS modelling techniques. The study area covers about 785.6 × 104 ha, which corresponds to the main forest units of the CORINE land cover 2006 database (i.e. broad-leaved forests, coniferous forests and mixed forests). Annual forest logging activities were mapped using Landsat imagery. Validation procedures were applied. A revised version of the Universal Soil Loss Equation (USLE) was used to predict the soil loss potential due to rill and inter-rill processes. To ensure a thorough modelling approach, the input parameters were calculated using the original methods reported in the USDA handbooks. The derived high-resolution data regarding forest cover change shows that 317,535 ha (4.04% of the total forest area in Italy) were harvested during the period under review. The predicted long-term annual average soil loss rate was 0.54 Mg ha− 1 yr− 1. The average rate of soil loss in forests that remained undisturbed during the modelled period is equal to 0.33 Mg ha− 1 yr− 1. Notably, about half of the soil loss (45.3%) was predicted for the logged areas, even though these cover only about 10.6% of the Italian forests. The identified erosion hotspots may represent a serious threat for the soil-related forest ecosystems, and are in contrast to the EC Thematic Strategy for Soil Protection and Water Framework Directive

Coulomb interaction in graphene: Relaxation rates and transport

We analyze the inelastic electron-electron scattering in undoped graphene within the Keldysh diagrammatic approach. We demonstrate that finite temperature strongly affects the screening properties of graphene, which, in turn, influences the inelastic scattering rates as compared to the zero-temperature case. Focussing on the clean regime, we calculate the quantum scattering rate which is relevant for dephasing of interference processes. We identify an hierarchy of regimes arising due to the interplay of a plasmon enhancement of the scattering and finite-temperature screening of the interaction. We further address the energy relaxation and transport scattering rates in graphene. We find a non-monotonic energy dependence of the inelastic relaxation rates in clean graphene which is attributed to the resonant excitation of plasmons. Finally, we discuss the temperature dependence of the conductivity at the Dirac point in the presence of both interaction and disorder. Our results complement the kinetic-equation and hydrodynamic approaches for the collision-limited conductivity of clean graphene and can be generalized to the treatment of physics of inelastic processes in strongly non-equilibrium setups.Comment: 28 pages, 16 figure

SchNet - a deep learning architecture for molecules and materials

Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning in general and deep learning in particular is ideally suited for representing quantum-mechanical interactions, enabling to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for \emph{molecules and materials} where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study of the quantum-mechanical properties of C$_{20}$-fullerene that would have been infeasible with regular ab initio molecular dynamics

Generation of Relativistic Electron Bunches with Arbitrary Current Distribution via Transverse-to-Longitudinal Phase Space Exchange

We propose a general method for tailoring the current distribution of relativistic electron bunches. The technique relies on a recently proposed method to exchange the longitudinal phase space emittance with one of the transverse emittances. The method consists of transversely shaping the bunch and then converting its transverse profile into a current profile via a transverse-to-longitudinal phase-space-exchange beamline. We show that it is possible to tailor the current profile to follow, in principle, any desired distributions. We demonstrate, via computer simulations, the application of the method to generate trains of microbunches with tunable spacing and linearly-ramped current profiles. We also briefly explore potential applications of the technique.Comment: 13 pages, 17 figure

SchNetPack 2.0: A neural network toolbox for atomistic machine learning

SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks as well as a PyTorch implementation of molecular dynamics. An optional integration with PyTorch Lightning and the Hydra configuration framework powers a flexible command-line interface. This makes SchNetPack 2.0 easily extendable with custom code and ready for complex training task such as generation of 3d molecular structures

Coulomb drag in graphene near the Dirac point

We study Coulomb drag in double-layer graphene near the Dirac point. A particular emphasis is put on the case of clean graphene, with transport properties dominated by the electron-electron interaction. Using the quantum kinetic equation framework, we show that the drag becomes $T$-independent in the clean limit, $T\tau \to \infty$, where $T$ is temperature and $1/\tau$ impurity scattering rate. For stronger disorder (or lower temperature), $T\tau \ll 1/\alpha^2$, where $\alpha$ is the interaction strength, the kinetic equation agrees with the leading-order ($\alpha^2$) perturbative result. At still lower temperatures, $T\tau \ll 1$ (diffusive regime) this contribution gets suppressed, while the next-order ($\alpha^3$) contribution becomes important; it yields a peak centered at the Dirac point with a magnitude that grows with lowering $T\tau$.Comment: 3 figures, with expanded Supplemental Material attached as an appendi