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Hydrodynamic flow of charge carriers in graphene is an energy flow unlike the usual mass flow in conventional fluids. In neutral graphene, the energy flow is decoupled from the electric current, making it difficult to observe the hydrodynamic effects and measure the viscosity of the electronic fluid by means of electric current measurements. In particular, we show that the hallmark Poiseuille flow in a narrow channel cannot be driven by the electric field irrespective of boundary conditions at the channel edges. Nevertheless one can observe nonuniform current densities similarly to the case of the well-known ballistic-diffusive crossover. The standard diffusive behavior with the uniform current density across the channel is achieved under the assumptions of specular scattering on the channel boundaries. This flow can also be made nonuniform by applying weak magnetic fields. In this case, the curvature of the current density profile is determined by the quasiparticle recombination processes dominated by the disorder-assisted electron-phonon scattering -- the so-called supercollisions., Comment: 12pages, 2 figures

The ATLAS experiment at the LHC processes, analyses and stores vast amounts of data, which is either recorded by the detector or simulated worldwide using Monte Carlo methods. ATLAS Computing metadata is generated at very high rates and volumes. The necessity to analyze this metadata is constantly increasing, since the heterogeneous, distributed and dynamically changing computing infrastructure requires sophisticated optimization decisions, made by human or/and by machines. Visual analytics is one of the methods facilitating the analysis of massive amounts of data (structured, semi-structured, and unstructured) which leverages human judgement by means of interactive visual representations. Given the huge number of ATLAS computing jobs that need to be visualized simultaneously for error investigations or other optimization processes, resources of the client application responsible for such visualization may reach its limits. Data objects that share similar feature values can be represented and visualized as a single group, thus initial large data sample would be represented at different levels of detail. This approach will also avoid client overload. In this paper we evaluate implementations of k-means-based Level-of-Detail generator method applied to the metadata of ATLAS jobs. This method is used in the visual analytics application InVEx (Interactive Visual Explorer) that is under development, and which is based on 3-dimensional interactive visualization of multidimensional data.

We propose a quartic chiral term $m_x m_y m_z \nabla \cdot \mathbf{m}$ for the energy density of a cubic ferromagnet with broken parity symmetry (point group $T_d$). We demonstrate that this interaction causes a phase transition from a collinear ferromagnetic state to a non-collinear magnetic cone ground state provided its strength exceeds the geometric mean of magnetic exchange and cubic anisotropy. The corresponding non-collinear ground state may also be additionally stabilized by an external magnetic field pointing along certain crystallographic directions. The four-spin chiral exchange does also manifest itself in peculiar magnon spectra and favors spin waves with the wave vector that is perpendicular to the average magnetization direction., Comment: 8 pages

In compensated two-component systems in confined, two-dimensional geometries, nonlocal response may appear due to an external magnetic field. Within a phenomenological two-fluid framework, we demonstrate the evolution of charge flow profiles and the emergence of a giant nonlocal pattern dominating charge transport in a magnetic field. Applying our approach to the specific case of intrinsic graphene, we suggest a simple physical explanation for the experimental observation of giant nonlocality. Our results provide an intuitive way to predict the outcome of future experiments exploring the rich physics of many-body electron systems in confined geometries as well as to design possible applications.

Publisher's version (útgefin grein), The two-dimensional Heisenberg exchange model with out-of-plane anisotropy and a Dzyaloshinskii-Moriya interaction is employed to investigate the lifetime and stability of antiferromagnetic (AFM) skyrmion as a function of temperature and external magnetic field. An isolated AFM skyrmion is metastable at zero temperature in a certain parameter range set by two boundaries separating the skyrmion state from the uniform AFM phase and a stripe domain phase. The distribution of the energy barriers for the AFM skyrmion decay into the uniform AFM state complements the zero-temperature stability diagram and demonstrates that the skyrmion stability region is significantly narrowed at finite temperatures. We show that the AFM skyrmion stability can be enhanced by an application of magnetic field, whose strength is comparable to the spin-flop field. This stabilization of AFM skyrmions in external magnetic fields is in sharp contrast to the behavior of their ferromagnetic counterparts. Furthermore, we demonstrate that the AFM skyrmions are stable on the timescales of milliseconds below 50 K for realistic material parameters, making it feasible to observe them in modern experiments., P.F.B. acknowledges support from the Icelandic Research Fund (Grant No. 163048-052), the University of Iceland Research Fund, and Alexander von Humboldt Foundation. P.F.B., D.Y., D.R.G., and M.T. acknowledge support from the Russian Science Foundation under Project No. 17-12-01359. The work of M.T. was partially supported by ICC-IMR, Tohoku University (Japan). O.A.T. acknowledges support by a grant of the Center for Science and Innovation in Spintronics (Core Research Cluster), Tohoku University, by Ministry of Science and Higher Education of the Russian Federation in the framework of Increase Competitiveness Program of NUST “MISi” (No. K2-2019-006), implemented by a governmental decree dated 16th of March 2013, N 211, and by JSPS and RFBR under the Japan-Russia Research Cooperative Program.

In this study we have measured the magnetoresistance response of inverted HgTe quantum wells in the presence of a large parallel magnetic field up to 33 T. We show that in quantum wells with inverted band structure a monotonically decreasing magnetoresistance is observed when a magnetic field up to the order of 10 T is applied parallel to the quantum well plane. This feature is accompanied by a vanishing of nonlocality and is consistent with a predicted modification of the energy spectrum that becomes gapless at a critical in-plane field ${B}_{c}$. Magnetic fields in excess of ${B}_{c}$ allow us to investigate the evolution of the magnetoresistance in this field-induced semimetallic region beyond the known regime. After the longitudinal resistance reaches a minimum in the presumably gapless phase, we observe a strong upturn of the longitudinal resistance. A small residual Hall signal picked up in nonlocal measurements suggests that this feature is likely a bulk phenomenon and is caused by the semimetallicity of the sample. Theoretical calculations indeed support that the origin of these features is classical and a power law upturn of the resistance can be expected due to the specifics of two-carrier transport in thin (semi)metallic samples subjected to large magnetic fields.

In ultra-pure conductors, collective motion of charge carriers at relatively high temperatures may become hydrodynamic such that electronic transport may be described similarly to a viscous flow. In confined geometries (e.g., in ultra-high quality nanostructures), the resulting flow is Poiseuille-like. When subjected to a strong external magnetic field, the electric current in semimetals is pushed out of the bulk of the sample towards the edges. Moreover, we show that the interplay between viscosity and fast recombination leads to the appearance of counterflows. The edge currents possess a non-trivial spatial profile and consist of two stripe-like regions: the outer stripe carrying most of the current in the direction of the external electric field and the inner stripe with the counterflow., 10 pages, 5 figures

Giant Gilbert damping anisotropy is identified as a signature of strong Rashba spin-orbit coupling in a two-dimensional antiferromagnet on a honeycomb lattice. The phenomenon originates in spin-orbit induced splitting of conduction electron subbands that strongly suppresses certain spin-flip processes. As a result, the spin-orbit interaction is shown to support an undamped non-equilibrium dynamical mode that corresponds to an ultrafast in-plane N\'eel vector precession and a constant perpendicular-to-the-plane magnetization. The phenomenon is illustrated on the basis of a two dimensional $s$-$d$ like model. Spin-orbit torques and conductivity are also computed microscopically for this model. Unlike Gilbert damping these quantities are shown to reveal only a weak anisotropy that is limited to the semiconductor regime corresponding to the Fermi energy staying in a close vicinity of antiferromagnetic gap., Comment: 12 pages, 3 figures

The development of the Interactive Visual Explorer (InVEx), a visual analytics tool for the computing metadata of the ATLAS experiment at LHC, includes research of various approaches for data handling both on server and client sides. InVEx is implemented as a web-based application which aims at the enhancing of analytical and visualization capabilities of the existing monitoring tools and facilitates the process of data analysis with the interactivity and human supervision. The current work is focused on the architecture enhancements of the InVEx application. First, we will describe the user-manageable data preparation stage for cluster analysis. Then, the Level-of-Detail approach for the interactive visual analysis will be presented. It starts with the low detailing, when all data records are grouped (by clustering algorithms or by categories) and aggregated. We provide users with means to look deeply into this data, incrementally increasing the level of detail. Finally, we demonstrate the development of data storage backend for InVEx, which is adapted for the Level-of-Detail method to keep all stages of data derivation sequence.

The Interactive Visual Explorer (InVEx) application is designed as a visual analytics tool for Big Data analysis. Visual analytics is an integral approach to data analysis, combining methods of intellectual data analysis with advanced interactive visualization. One of the main objectives of InVExis to process large data samples by decreasing their level of detail (LoD).The proposed approach includes clustering as well as flexible grouping by different parameters, providing the exploration of data from the lowest to the highest level of details. The results of grouping and clusterization arevisualized using interactive 3D scene and parallel coordinates, allowing the user to gain insight into data, to explore hidden correlations and trends of parameters.

Collective behavior is one of the most intriguing aspects of the hydrodynamic approach to electronic transport. Here we provide a consistent, unified calculation of the dispersion relations of the hydrodynamic collective modes in graphene. Taking into account viscous effects, we show that the hydrodynamic sound mode in graphene becomes overdamped at sufficiently large momentum scales. Extending the linearized theory beyond the hydrodynamic regime, we connect the diffusive hydrodynamic charge density fluctuations with plasmons., Comment: 17 pages, 11 figures

We consider a contribution $w_{\text{ch}}$ to the micromagnetic energy density that is linear with respect to the first spatial derivatives of the local magnetization direction. For a generalized 2D Rashba ferromagnet, we present a microscopic analysis of this contribution and, in particular, demonstrate that it cannot be expressed through Lifshitz invariants beyond the linear order in the spin-orbit coupling (SOC) strength. Terms in $w_{\text{ch}}$ beyond Lifshitz invariants emerge as a result of spin rotation symmetry breaking caused by SOC. Effects of these terms on the phase diagram of magnetic states and spin-wave dispersion are discussed. Finally, we present a classification of terms in $w_{\text{ch}}$, allowed by symmetry, for each crystallographic point group., 6 pages, 2 figures + 4 page supplementary information

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2–3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100 × to 1000 × improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼ 1011 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine.

Spin-transfer torques (STT), Gilbert damping (GD), and effective spin renormalization (ESR) are investigated microscopically in a 2D Rashba ferromagnet with spin-independent Gaussian white-noise disorder. Rashba spin-orbit coupling induced anisotropy of these phenomena is thoroughly analysed. For the case of two partly filled spin subbands, a remarkable relation between the anisotropic STT, GD, and ESR is established. In the absence of magnetic field and other torques on magnetization, this relation corresponds to a current-induced motion of a magnetic texture with the classical drift velocity of conduction electrons. Finally, we compute spin susceptibility of the system and generalize the notion of spin-polarized current., 20 pages, 4 figures

We investigate spin-orbit torques on magnetization in an insulating ferromagnetic (FM) layer that is brought into a close proximity to a topological insulator (TI). In addition to the well-known field-like spin-orbit torque, we identify an anisotropic anti-damping-like spin-orbit torque that originates in a diffusive motion of conduction electrons. This diffusive torque is vanishing in the limit of zero momentum (i. e. for spatially homogeneous electric field or current), but may, nevertheless, have a strong effect on spin-torque resonance at finite frequency provided external field is neither parallel nor perpendicular to the TI surface. The required electric field configuration can be created by a grated top gate., 10 page main text, 3 figures

Big data technology belongs to the one of high-tech solutions that will form the forthcoming digital epoch. We present short overview of the current approach to big data, highlighting the main problems it gives rise to. We point out that big data seems to be revolutionary new technology, but in fact should be treated just a new tool to produce knowledge, thus it produces the same risks and challenges as other breakthroughs the humanity endured in the past. We also conclude that cultural aspects of big data implementation should be considered the most crucial one, and the experts community should form the balanced attitude towards this technology. We show the impossibility to control big data with attempts to prohibit some peculiar features it possesses, and propose to focus on such practical steps as terminology improvements, and the evaluation of societal outcomes of the new technology.

Recent experiments on switching antiferromagnetic domains by electric current pulses have attracted a lot of attention to spin-orbit torques in antiferromagnets. In this work, we employ the tight-binding model solver, kwant, to compute spin-orbit torques in a two-dimensional antiferromagnet on a honeycomb lattice with strong spin-orbit interaction of Rashba type. Our model combines spin-orbit interaction, local s-d-like exchange, and scattering of conduction electrons on on-site disorder potential to provide a microscopic mechanism for angular momentum relaxation. We consider two versions of the model: one with preserved and one with broken sublattice symmetry. A non-equilibrium staggered polarization, that is responsible for the so-called Neel spin-orbit torque, is shown to vanish identically in the symmetric model but may become finite if sublattice symmetry is broken. Similarly, anti-damping spin-orbit torques vanish in the symmetric model but become finite and anisotropic in a model with broken sublattice symmetry. As expected, anti-damping torques also reveal a sizable dependence on impurity concentration. Our numerical analysis also confirms symmetry classification of spin-orbit torques and strong torque anisotropy due to in-plane confinement of electron momenta., 14 pages, 12 figures

Electromagnetic field interacting with a topologically protected one-dimensional electron helical state is shown to support a one-dimensional plasmon-polariton, which is a collective electron excitation dressed with the electromagnetic field. The electronic helical state arises at the surface of three-dimensional topological insulator in the proximity of the ferromagnet and is localized at the magnetization domain wall. This opens the possibilities to manipulate quantum optical states by altering magnetic domain configurations. An exact spectral equation for such topological plasmon-polariton is derived.

The magnetic susceptibility of an ensemble of clean metallic nanoparticles is shown to change from paramagnetic to diamagnetic one with the onset of spin-orbit interaction. The effect is quantified on the basis of symmetry analysis with the help of the random matrix theory. In particular, the magnetic susceptibility is investigated as the function of symmetry breaking parameter representing magnetic flux in the crossover from symplectic to unitary and from orthogonal to unitary ensembles. Corresponding analytical and numerical results provide a qualitative explanation to the experimental data on diamagnetism of an ensemble of gold nanorods., 6 pages, 5 figures; extended version

The ATLAS experiment at the Large Hadron Collider has a complex heterogeneous distributed computing infrastructure, which is used to process and analyse exabytes of data. Metadata are collected and stored at all stages of data processing and physics analysis. All metadata could be divided into operational metadata to be used for the quasi on-line monitoring, and archival to study the behaviour of corresponding systems over a given period of time (i.e., long-term data analysis). Ensuring the stability and efficiency of functioning of complex and large-scale systems, such as those in the ATLAS Computing, requires sophisticated monitoring tools, and the long-term monitoring data analysis becomes as important as the monitoring itself. Archival metadata, which contains a lot of metrics (hardware and software environment descriptions, network states, application parameters, errors) accumulated for more than decade, can be successfully processed by various machine learning (ML) algorithms for classification, clustering and dimensionality reduction. However, the ML data analysis, despite the massive use, is not without shortcomings: the underlying algorithms are usually treated as “black boxes”, as there are no effective techniques for understanding their internal mechanisms, and the domain-experts involvement in the process of ML data analysis is very limited. As a result, the data analysis suffers from the lack of human supervision. Moreover, sometimes the conclusions made by algorithms with a high accuracy may have no sense regarding the real data model. In this work we will demonstrate how the interactive data visualization can be applied to extend the routine ML data analysis methods. Visualization allows an active use of human spatial thinking to identify new tendencies and patterns found in the collected data, avoiding the necessity of struggling with the instrumental analytics tools. The architecture and the corresponding prototype of Interactive Visual Explorer (InVEx) - visual analytics toolkit for the multidimensional data analysis of ATLAS computing metadata will be presented. The web-application part of the prototype provides an interactive visual clusterization of ATLAS computing jobs, search for computing jobs non-trivial behaviour and its possible reasons. The ATLAS experiment at the Large Hadron Collider has a complex heterogeneous distributed computing infrastructure, which is used to process and analyse exabytes of data. Metadata are collected and stored at all stages of data processing and physics analysis. All metadata could be divided into operational metadata to be used for the quasi on-line monitoring, and archival to study the behaviour of corresponding systems over a given period of time (i.e. long-term data analysis). Ensuring the stability and efficiency of complex and large-scale systems, such as those in the ATLAS Computing, requires sophisticated monitoring tools, and the long-term monitoring data analysis becomes as important as the monitoring itself. Archival metadata, which contains a lot of metrics (hardware and software environment descriptions, network states, application parameters, errors) accumulated for more than a decade, can be successfully processed by various machine learning (ML) algorithms for classification, clustering and dimensionality reduction. However, the ML data analysis, despite the massive use, is not without shortcomings: the underlying algorithms are usually treated as “black boxes”, as there are no effective techniques for understanding their internal mechanisms. As a result, the data analysis suffers from the lack of human supervision. Moreover, sometimes the conclusions made by algorithms may not be making sense with regard to the real data model. In this work we will demonstrate how the interactive data visualization can be applied to extend the routine ML data analysis methods. Visualization allows an active use of human spatial thinking to identify new tendencies and patterns found in the collected data, avoiding the necessity of struggling with the instrumental analytics tools. The architecture and the corresponding prototype of Interactive Visual Explorer (InVEx) - visual analytics toolkit for the multidimensional data analysis of ATLAS computing metadata will be presented. The web-application part of the prototype provides an interactive visual clusterization of ATLAS computing jobs, search for computing jobs non-trivial behaviour and its possible reasons.