We report experimental data and theoretical analysis of Coulomb drag between two closely positioned graphene monolayers in weak magnetic field. Close enough to the neutrality point, coexistence of electrons and holes in each layer leads to a dramatic increase of the drag resistivity. Away from charge neutrality, we observe non-zero Hall drag. The observed phenomena are explained by decoupling of electric and quasiparticle currents which are orthogonal at charge neutrality. The sign of magneto-drag depends on the energy relaxation rate and geometry of the sample.
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The discovery of magnetism in two-dimensional (2D) van der Waals (vdW) materials1–4 has flourished a new endeavour of fundamental problems in magnetism as well as potential applications in computing, sensing and storage technologies5–10. Of particular interest are antiferromagnets 11, 12, which due to their intrinsic antiferromagnetic exchange coupling show several advantages in relation to ferromagnets such as robustness against external magnetic perturbations. This property is one of the cornerstones of antiferromagnets13 and implies that in formation stored in antiferromagnetic domains is invisible to applied magnetic fields preventing it from being erased or manipulated. Here we show that, despite this fundamental understanding, the magnetic domains of recently discovered 2D vdW MnPS3 antiferromagnet14, 15 can be controlled via external magnetic fields and electric currents. We realize ultrafast domain-wall dynamics with velocities up to ∼1500 m s−1 and ∼3000 m s−1 respectively to a broad range of field magnitudes (0.0001−22 T) and current densities (108 − 1010 A cm−2). Both domain wall dynamics are determined by the edge terminations which generated uncompensated spins following the underlying symmetry of the honeycomb structure. We find that edge atoms belonging to different magnetic sublattices function as geometrical constrictions preventing the displacement of the wall, whereas having atoms of the same sublattice at both edges of the material allows for the field-driven domain wall motion which is only limited by the spin-flop transition of the antiferromagnet beyond 25 T. Conversely, electric currents can induce motion of domain walls in most of the edges except those where the two sublattices are present at the borders (e.g. armchair edges). Furthermore, the orientation of the layer relative to the current flow provides an additional degree of freedom for controlling and manipulating magnetic domains in MnPS3. Our results indicate that the implementation of 2D vdW antiferromagnets in real applications requires the engineering of the layer edges which enables an unprecedented functional feature in ultrathin device platforms.
We propose a new approach to generate and detect spin currents in graphene, based on a large spin-Hall response arising near the neutrality point in the presence of an external magnetic field. Spin currents result from the imbalance of the Hall resistivity for the spin-up and spin-down carriers induced by the Zeeman interaction, and do not involve a spin-orbit interaction. Large values of the spin-Hall response achievable in moderate magnetic fields produced by on-chip sources, and up to room temperature, make the effect viable for spintronics applications.
Propagation of ferromagnetic domain walls on sub-atomic scale was measured in a thin uniaxial garnet film by using ballistic Hall magnetometry. Domain walls are found to move by equidistant steps, which correspond to the crystal lattice constant in this material. Our results are in good agreement with the theory of intrinsic pinning of a domain wall in the Peierls potential. We have also measured AC susceptibility of a domain wall moving inside a Peierls valley. The observed nonlinear behavior of the AC susceptibility can be understood within the framework of kinks and breathers nucleating and spreading along the domain wall.
Macroscopic dynamical descriptions of complex physical systems are crucial for understanding and controlling material behavior. With the growing availability of data and compute, machine learning has become a promising alternative to first-principles methods to build accurate macroscopic models from microscopic trajectory simulations. However, for spatially extended systems, direct simulations of sufficiently large microscopic systems that inform macroscopic behavior is prohibitive. In this work, we propose a framework that learns the macroscopic dynamics of large stochastic microscopic systems using only small-system simulations. Our framework employs a partial evolution scheme to generate training data pairs by evolving large-system snapshots within local patches. We subsequently identify the closure variables associated with the macroscopic observables and learn the macroscopic dynamics using a custom loss. Furthermore, we introduce a hierarchical upsampling scheme that enables efficient generation of large-system snapshots from small-system trajectory distributions. We empirically demonstrate the accuracy and robustness of our framework through a variety of stochastic spatially extended systems, including those described by stochastic partial differential equations, idealised lattice spin systems, and a more realistic NbMoTa alloy system.