arXiv subjects

Robin Msiska

Publications and source records attributed to Robin Msiska.

6 recordsLinked to original sources

arXiv2026

X-ray magnetic circular dichroism in altermagnets: insights from symmetry

Altermagnets are a unique subset of antiferromagnets possessing ferromagnet-like responses that are odd in the Néel vector, making X-ray magnetic circular dichroism (XMCD) a natural probe of ground state altermagnetic order. Because the relativistic ingredient of XMCD at the $L$-edge is supplied predominantly by core-level spin--orbit coupling (SOC), the dichroic signal can reflect the non-relativistic order of the valence electrons. Here we develop a complete symmetry framework for XMCD in altermagnets (AMs) preserving the crystalline unit cell. We tabulate the symmetry-allowed optical Hall vector, anisotropic magnetic dipole (AMD), and underlying octupolar components for all magnetic point groups, thereby identifying the experimental geometries that maximize the XMCD signal. We show that only twelve magnetic point groups, none with a rotation axis of order three or higher, are compatible with both altermagnetism and finite XMCD. By extending the Landau theory of collinear AMs, we connect these relativistic constraints to the spin--orbit-free order. We show that the AMD is forced to vanish at zero valence SOC in conventional antiferromagnets, and that every AM sublattice structure allows the Hall vector and AMD at linear or cubic order in the Néel vector; whether the resulting XMCD is nonzero depends on the Néel vector orientation at finite SOC. The Landau theory thus fixes the Néel-vector dependence of the XMCD for all AM candidates, and separates $d$-wave AMs, whose signal is inherited from a non-relativistic AMD, from $g$- and $i$-wave AMs, whose signal requires valence SOC or core--valence multiplet effects. We illustrate these predictions with first-principles Bethe--Salpeter calculations of $L$-edge XMCD for five representative altermagnetic materials and one conventional antiferromagnet.

cond-mat.str-el↗

arXiv2026

Planar Interfaces for Transmission of Chiral Spin Textures

Lateral magnetic interfaces provide a direct way to test whether skyrmions remain robust when driven across abrupt changes in material parameters and magnetic order. Here we study skyrmion transmission across planar ferromagnet-ferromagnet (FM-FM), antiferromagnet-antiferromagnet (AFM-AFM), ferromagnet-antiferromagnet (FM-AFM), and antiferromagnet-ferromagnet (AFM-FM) interfaces using micromagnetic simulations and analytic reduced-coordinate criteria. The outcomes are organized into phase diagrams according to the morphology formed in the receiving region, distinguishing compact transmission from deformed skyrmions, stripe-domain states, amorphous textures, and relaxation into the background. Same-order FM-FM and AFM-AFM skyrmion transmission is captured by an analytically defined range of the reduced Dzyaloshinskii-Moriya interaction, identifying the wall-softening regime that supports compact transmission without stripe formation. Mixed-order FM-AFM and AFM-FM interfaces are directionally distinct, requiring conversion between ferromagnetic magnetization and antiferromagnetic Néel textures. These results show that planar interfaces act as active transport elements and provide reduced design criteria for heterogeneous skyrmion tracks.

cond-mat.mes-hall↗

arXiv2023

Perspective on unconventional computing using magnetic skyrmions

Learning and pattern recognition inevitably requires memory of previous events, a feature that conventional CMOS hardware needs to artificially simulate. Dynamical systems naturally provide the memory, complexity, and nonlinearity needed for a plethora of different unconventional computing approaches. In this perspective article, we focus on the unconventional computing concept of reservoir computing and provide an overview of key physical reservoir works reported. We focus on the promising platform of magnetic structures and, in particular, skyrmions, which potentially allow for low-power applications. Moreover, we discuss skyrmion-based implementations of Brownian computing, which has recently been combined with reservoir computing. This computing paradigm leverages the thermal fluctuations present in many skyrmion systems. Finally, we provide an outlook on the most important challenges in this field.

cs.ET↗

arXiv2022

Spatial Analysis of Physical Reservoir Computers

Physical reservoir computing is a computational framework that implements spatiotemporal information processing directly within physical systems. By exciting nonlinear dynamical systems and creating linear models from their state, we can create highly energy-efficient devices capable of solving machine learning tasks without building a modular system consisting of millions of neurons interconnected by synapses. To act as an effective reservoir, the chosen dynamical system must have two desirable properties: nonlinearity and memory. We present task agnostic spatial measures to locally measure both of these properties and exemplify them for a specific physical reservoir based upon magnetic skyrmion textures. In contrast to typical reservoir computing metrics, these metrics can be resolved spatially and in parallel from a single input signal, allowing for efficient parameter search to design efficient and high-performance reservoirs. Additionally, we show the natural trade-off between memory capacity and nonlinearity in our reservoir's behaviour, both locally and globally. Finally, by balancing the memory and nonlinearity in a reservoir, we can improve its performance for specific tasks.

cs.LG↗

arXiv2022

Audio Classification with Skyrmion Reservoirs

Physical reservoir computing is a computational paradigm that enables spatio-temporal pattern recognition to be performed directly in matter. The use of physical matter leads the way towards energy-efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. We propose a high performance "skyrmion mixture reservoir" that implements the reservoir computing model with multi-dimensional inputs. We show that our implementation solves spoken digit classification tasks at the nanosecond timescale, with an overall model accuracy of 97.4% and a less that 1% word error rate; the best performance ever reported for in-materio reservoir computers. Due to the quality of the results and the low power properties of magnetic texture reservoirs, we argue that skyrmion fabrics are a compelling candidate for reservoir computing.

cond-mat.mes-hall↗

arXiv2021

Nonzero Skyrmion Hall Effect in Topologically Trivial Structures

It is widely believed that the skyrmion Hall effect, often disruptive for device applications, vanishes for overall topologically trivial structures such as (synthetic) antiferromagnetic skyrmions and skyrmioniums due to a compensation of Magnus forces. In this manuscript, however, we report that in contrast to the case of spin-transfer torque driven skyrmion motion, this notion is generally false for spin-orbit torque driven objects. We show that the skyrmion Hall angle is directly related to their helicity and imposes an unexpected roadblock for developing faster and lower input racetrack memories based on spin-orbit torques.

cond-mat.mes-hall↗