arXiv subjects

Karin Everschor-Sitte

Publications and source records attributed to Karin Everschor-Sitte.

At least 19 recordsLinked to original sources

arXiv2026

Inferring Magnetic Material Parameters from Statistical Measures in Strongly Fluctuating Magnetization Dynamics

Magnetic material parameters such as the exchange stiffness and magnetic anisotropy govern the behavior and functionality of magnetic systems, yet their local inference from magnetization data remains challenging, particularly in strongly fluctuating regimes with polycrystalline or multiphase microstructure, where conventional texture-based methods become unreliable. We introduce a magnetization-only framework for inferring material parameters from thermally driven magnetization dynamics. Using micromagnetic simulations, we extract statistical quantities such as temporal mean and latent entropy from the magnetization dynamics, fit models to these descriptors, and invert the models to infer material parameters. We show that this framework enables material-parameter inference as well as grain-boundary detection in a heterogeneous sample. Among the descriptors considered, latent entropy yields more accurate parameter estimates than the temporal mean. Our results establish latent entropy as an efficient descriptor for inferring magnetic material parameters from dynamical magnetization data and point toward its use for experimental parameter extraction at high temperatures and, more broadly, under strongly fluctuating conditions.

cond-mat.mtrl-sci↗

arXiv2026

Topological Classification of Non-Normalizable Vector Fields

Topological classification of physical vector fields conventionally relies on field normalization and homotopy-based invariants. However, when field amplitudes vanish, normalization becomes ill-defined, preventing a direct topological characterization. Here, we introduce a general framework for the topological classification of non-normalizable $n$-dimensional vector fields with compactifiable base spaces by transforming them into $(n+1)$-dimensional normalized vector fields. This construction extends homotopy-based classification to fields containing amplitude zeros. We explicitly demonstrate the approach for one-, two-, and three-dimensional non-normalized vector fields and derive the corresponding topological invariants. The resulting topological charges are robust under continuous deformations and can change only when the embedding structure becomes singular. Our framework provides a unified route to the topological characterization of non-normalizable fields and opens the door to the study of topological phenomena in a broad range of systems, including magnetic textures, ferroelectrics, electromagnetic fields, and wave systems.

cond-mat.other↗

arXiv2026

Photo-induced currents and short-term memory for reservoir computing in a ferroelectric semiconductor

Physical reservoir computing represents an energy efficient approach for processing temporal signals by exploiting the intrinsic nonlinear dynamics and fading memory of a physical system. Recently, ferroelectric semiconductors moved into focus as reservoir materials motivated by their versatile electronic responses to external stimuli. Here, we explore the fundamental possibility to recognize time-varying light pulses via photo-induced currents, using the small-band-gap p-type semiconductor ErMnO$_3$ as a model system. Under white light illumination, ErMnO$_3$ exhibits non-linearly evolving photo-induced currents and controllable relaxation dynamics that naturally realize the high-dimensional projection and fading memory capabilities required for reservoir computing. The reservoir capability of ErMnO$_3$ is reflected by the improved recognition accuracy of "Past" input pulses, which increases from ~33% to ~93% after applying reservoir transformation to the input signal. The results present ferroelectric hexagonal manganites as a promising platform for photo-induced current-based reservoir computing and highlight the potential of light-driven oxide semiconductors for temporal information processing.

cond-mat.mtrl-sci↗

arXiv2026

Analysis of the Topology of a Plasmonic Target-Skyrmion Texture

Topological concepts are frequently used to describe structured optical fields, including plasmonic near fields. Topological descriptions in terms of skyrmion numbers implicitly assume the compactness of the underlying manifold. Even when skyrmion-like textures appear locally, the compactness is usually not fulfilled in extended optical fields. Here, we use photoemission electron microscopy to investigate a plasmonic nano-focus that exhibits a sequence of radially extending alternating skyrmion and antiskyrmion textures. The full spatio-temporal reconstruction of the electric field vectors and their topology is accessible by vector polarimetry. The experiments confirm the expected oscillatory behavior of the skyrmion number and demonstrate that a global skyrmion number cannot be assigned in such non-compact fields.

physics.optics↗

arXiv2026

Reconstruction of spin structures from topological charge distributions via generative neural network systems

Localized topological defects inherently possess a multiscale character. While their microstructure configuration depends on the specific physical system, their topological features and mutual interactions can be described on the macroscale in terms of a particle representation. However, determining the physical properties associated with a given defect pattern often requires knowledge of the underlying microscopic structure. In this work, we extend a Wasserstein generative adversarial neural network by incorporating physical constraints and Fourier-space information to generate microscopic spin configurations consistent with prescribed macroscopic patterns and thermodynamic parameters. Using the two-dimensional XY model as a test case, where vortex-antivortex pairs act as long-range interacting defects, we show that the model generates spin configurations that accurately reproduce magnetization, susceptibility, helicity modulus, and spin-spin correlations over a wide range of temperatures below the Kosterlitz-Thouless transition. At the same time, deviations in the specific heat reveal limitations in reproducing higher order energy fluctuations. A complementary analysis based on topological data analysis uncovers subtle differences in global spin-correlation structures at near critical temperatures that are not apparent from conventional correlation functions alone. These results demonstrate both the promise and current limitations of generative approaches for multiscale studies of defect-dominated spin systems and at the same time highlight topological methods as valuable tools for characterizing critical behavior.

cond-mat.stat-mech↗

arXiv2026

Defect Detection in Magnetic Systems Using U-Net and Statistical Measures

Local material inhomogeneities can strongly influence magnetization dynamics and macroscopic magnetic properties, yet detecting such defects from magnetic imaging data remains challenging when thermal fluctuations and experimental noise obscure static contrast. Here, we investigate defect detection in strongly fluctuating magnetization regimes where signatures of inhomogeneities largely average out in time-resolved measurements. Using finite-temperature micromagnetic simulations with randomly distributed defects and material parameters representative of \ce{Ni80Fe20}, we compute per-pixel temporal mean, temporal standard deviation, and latent entropy and use them as inputs for U-Net-based semantic segmentation models. We find that the most effective descriptor depends on the noise level and, importantly, that robust detection requires training data that reflect the expected noise statistics. These results provide practical guidance for designing noise-robust defect-detection workflows in magnetic imaging.

cond-mat.mtrl-sci↗

arXiv2026

Leveraging Interactions for Efficient Swarm-Based Brownian Computing

Drawing inspiration from swarm intelligence, we show that short-range attractive interactions between thermally driven Brownian quasiparticles enable energy-efficient optimization. As quasiparticles can be generated directly within a material, the swarm size can be adjusted with minimal energy overhead. Using an optimization task defined by a spatially varying temperature landscape, we quantitatively show that interacting swarms reliably identify global optima and significantly outperform non-interacting searchers within a well-defined regime of interaction strength and swarm size. This improvement arises from emergent cooperative behavior, where local interactions guide the swarm toward high-quality solutions without central coordination. To link our physical model to experimental realizations, we coarse-grain the quasiparticle dynamics onto a sensor lattice and generate trajectories emulating particle-tracking measurements. We further show that the interacting swarm adapts robustly to landscapes that evolve over time. These findings establish interacting Brownian quasiparticles as a physical platform for scalable and energy-efficient unconventional computing.

cond-mat.stat-mech↗

arXiv2026

The 2026 Skyrmionics Roadmap

Magnetic skyrmions and related topological spin textures have emerged as a central topic in condensed-matter physics, combining fundamental significance with potential for transformative applications in spintronics, magnonics, and beyond. Over the past decade, advances in material platforms, imaging techniques, theoretical modeling, and device concepts have established skyrmionics as a rapidly expanding field. At the same time, challenges remain in stabilizing, controlling, and integrating such textures into functional architectures, while novel phenomena such as antiskyrmions, higher-order skyrmions, hopfions, and antiferromagnetic textures arise. The 2026 Skyrmionics Roadmap represents a collective effort of many authors, providing a comprehensive perspective on the current state-of-the-art and the outlook for the coming years. In 33 focused sections, each co-authored by two researchers, we chart progress in theory and modeling, material systems, skyrmion dynamics, and skyrmion technologies. By offering a consolidated vision, this Roadmap aims to guide both fundamental research and application-driven efforts, accelerating the transition of skyrmionics from conceptual breakthroughs toward practical technologies.

cond-mat.mes-hall↗

arXiv2026

Hopfions in screw chiral magnets

Three-dimensional topological spin textures have attracted growing interest due to their rich geometry and potential for functional magnetic phenomena. In this work, we propose the concept of symmetry-transforming magnetic models as a novel route to generate and stabilize complex three-dimensional textures in an arbitrary magnetic background. Using this framework, we predict a screw chiral magnet model that stabilizes magnetic Hopfions and other three-dimensional magnetic textures within a ferromagnetic background. We show that the resulting solitons display distinctive physical properties, including unconventional Goldstone modes. Our results establish continuous symmetry transformations as a general strategy for uncovering new classes of magnetic solitons with unique dynamical signatures.

cond-mat.other↗

arXiv2026

Metrics for spin-based computing

Spin-based computing is emerging as a powerful approach for energy-efficient and high-performance solutions to future data processing hardware. Spintronic devices function by electrically manipulating the collective dynamics of the electron spin, that is inherently non-volatile, nonlinear and fast-operating, and can couple to other degrees of freedom such as photonic and phononic systems. This review explores key advances in integrating magnetic and spintronic elements into computational architectures, ranging from fundamental components like radio-frequency neurons/synapses and spintronic probabilistic-bits to broader frameworks such as reservoir computing and magnetic Ising machines. We discuss hardware-specific and task-dependent metrics to evaluate the computing performance of spin-based components and associate them with physical properties. Finally, we discuss challenges and future opportunities, highlighting the potential of spin-based computing in next-generation technologies.

cond-mat.mes-hall↗

arXiv2024

3D Magnetic Textures with Mixed Topology: Unlocking the Tunable Hopf Index

Knots and links play a crucial role in understanding topology and discreteness in nature. In magnetic systems, twisted, knotted and braided vortex tubes manifest as Skyrmions, Hopfions, or screw dislocations. These complex textures are characterized by topologically non-trivial quantities, such as a Skyrmion number, a Hopf index $H$, a Burgers vector (quantified by an integer $ν$), and linking numbers. In this work, we introduce a discrete geometric definition of $H$ for periodic magnetic textures, which can be separated into contributions from the self-linking and inter-linking of flux tubes. We show that fractional Hopfions or textures with non-integer values of $H$ naturally arise and can be interpreted as states of ``mixed topology" that are continuously transformable to one of the multiple possible topological sectors. Our findings demonstrate a solid physical foundation for the Hopf index to take integer, non-integer, or specific fractional values, depending on the underlying topology of the system.

cond-mat.mes-hall↗

arXiv2024

Numerical Calculation of the Hopf Index for 3D Magnetic Textures

To gain deeper insight into the complex, stable, and robust configurations of magnetic textures, topological characterisation has proven essential. In particular, while the skyrmion number is a well-established topological invariant for 2D magnetic textures, the Hopf index serves as a key topological descriptor for 3D magnetic structures. In this work, we present and compare various methods for numerically calculating the Hopf index, provide implementations, and offer a detailed analysis of their accuracy and computational efficiency. Additionally, we identify and address common pitfalls and challenges associated with the numerical computation of the Hopf index, offering insights for improving the robustness of these techniques.

cond-mat.mes-hall↗

arXiv2024

PRCpy: A Python Package for Processing of Physical Reservoir Computing

Physical reservoir computing (PRC) is a computing framework that harnesses the intrinsic dynamics of physical systems for computation. It offers a promising energy-efficient alternative to traditional von Neumann computing for certain tasks, particularly those demanding both memory and nonlinearity. As PRC is implemented across a broad variety of physical systems, the need increases for standardised tools for data processing and model training. In this manuscript, we introduce PRCpy, an open-source Python library designed to simplify the implementation and assessment of PRC for researchers. The package provides a high-level interface for data handling, preprocessing, model training, and evaluation. Key concepts are described and accompanied by experimental data on two benchmark problems: nonlinear transformation and future forecasting of chaotic signals. Throughout this manuscript, which will be updated as a rolling release, we aim to facilitate researchers from diverse disciplines to prioritise evaluating the computational benefits of the physical properties of their systems by simplifying data processing, model training and evaluation.

cs.CE↗

arXiv2024

Neuromorphic Spintronics

Neuromorphic spintronics combines two advanced fields in technology, neuromorphic computing and spintronics, to create brain-inspired, efficient computing systems that leverage the unique properties of the electron's spin. In this book chapter, we first introduce both fields - neuromorphic computing and spintronics and then make a case for neuromorphic spintronics. We discuss concrete examples of neuromorphic spintronics, including computing based on fluctuations, artificial neural networks, and reservoir computing, highlighting their potential to revolutionize computational efficiency and functionality.

cond-mat.mtrl-sci↗

arXiv2024

Neuromorphic Overparameterisation and Few-Shot Learning in Multilayer Physical Neural Networks

Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach's efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks.

cond-mat.mes-hall↗

arXiv2024

Roadmap for Unconventional Computing with Nanotechnology

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

cs.ET↗

arXiv2023

Novel implementations for reservoir computing -- from spin to charge

Topological textures in magnetic and electric materials are considered to be promising candidates for next-generation information technology and unconventional computing. Here, we discuss how the physical properties of topological nanoscale systems, such as skyrmions and domain walls, can be leveraged for reservoir computing, translating non-linear problems into linearly solvable ones. In addition to the necessary requirements of physical reservoirs, the topological textures give new opportunities for the downscaling of devices, enhanced complexity, and versatile input and readout options. Our perspective article presents topological magnetic and electric defects as an intriguing platform for non-linear signal conversion, giving a new dimension to reservoir computing and in-materio computing in general.

physics.app-ph↗

arXiv2023

Revealing the ultra-fast domain wall motion in Manganese Gold through permalloy capping

Antiferromagnets offer much faster dynamics compared to their ferromagnetic counterparts but their order parameter is extremely difficult to detect and control. So far, controlling the Néel order parameter electrically is limited to only very few materials where Néel spin-orbit torques are allowed by symmetry. In this work, we show that coupling a thin ferromagnet (permalloy) layer on top of an antiferromagnet (Mn$_2$Au) solves a major roadblock -- the controlled reading, writing, and manipulation of antiferromagnetic domains. We confirm by atomistic spin dynamics simulations that the domain wall patterns in the Mn$_2$Au are imprinted on the permalloy, therefore allowing for indirect imaging of the Néel order parameter. Our simulations show that the coupled domain wall structures in Mn$_2$Au-Py bilayers can be manipulated by either acting on the Néel order parameter via Néel spin-orbit torques or by acting on the magnetisation (the ferromagnetic order parameter) via magnetic fields. In both cases, we predict ultra-high domain wall speeds on the order of 8.5 km/s. Thus, employing a thin ferromagnetic layer has the potential to easily control the Néel order parameter in antiferromagnets even where Néel spin-orbit torques are forbidden by symmetry. The controlled manipulation of the antiferromagnetic order parameter provides a promising basis for the development of high-density storage and efficient computing technologies working in the THz regime.

cond-mat.mtrl-sci↗