Over the last two decades, breakthrough works in the field of non-linear phononics have revealed that high-frequency lattice vibrations, when driven to high amplitude by mid- to far-infrared optical pulses, can bolster the light-matter interaction and thereby lend control over a variety of spontaneous orderings. This approach fundamentally relies on the resonant excitation of infrared-active transverse optical phonon modes, which are characterized by a maximum in the imaginary part of the medium's permittivity. Here, in this Perspective article, we discuss an alternative strategy where the light pulses are instead tailored to match the frequency at which the real part of the medium's permittivity goes to zero. This so-called epsilon-near-zero regime, popularly studied in the context of metamaterials, naturally emerges to some extent in all dielectric crystals in the infrared spectral range. We find that the light-matter interaction in the phononic epsilon-near-zero regime becomes strongly enhanced, yielding even the possibility of permanently switching both spin and polarization order parameters. We provide our perspective on how this hitherto-neglected yet fertile research area can be explored in future, with the aim to outline and highlight the exciting challenges and opportunities ahead.
The interplay between magnetism and charge transport is central to understanding colossal magnetoresistance (CMR), a phenomenon well studied in ferromagnets. Recently, antiferromagnetic (AFM) EuCd2P2 has attracted considerable interest due to its remarkable CMR, for which magnetic fluctuations and the formation of ferromagnetic clusters have been proposed as key mechanisms. Here we provide direct evidence that these effects originate from the formation and percolation of magnetic polarons. We employ a complementary set of sensitive probes that allows for a direct comparison of electronic and magnetic properties on multiple time scales revealing pronounced electronic and magnetic phase separation below T * ≈ 2T N . These measurements indicate an inhomogeneous, percolating electronic system below T * and well above the magnetic ordering temperature T N = 11 K. In applied magnetic fields, the onset of the pronounced negative MR in the paramagnetic regime emerges at a universal critical magnetization. The characteristic size of the magnetic polarons near the percolation threshold is estimated to be ~6-10 nm. Our results establish dynamic polaron percolation within an AFM matrix as the microscopic origin of CMR in EuCd2P2, providing a unified framework for magnetotransport in Eu-based correlated semiconductors.
Patient-specific bone models are essential for designing surgical guides and preoperative planning, as they enable the visualization of intricate anatomical structures. However, traditional CT-based approaches for creating bone models are limited to preoperative use due to the low flexibility and high radiation exposure of CT and time-consuming manual delineation. Here, we introduce Semi-Supervised Reconstruction with Knowledge Distillation (SSR-KD), a fast and accurate AI framework to reconstruct high-quality bone models from biplanar X-rays in 30 seconds, with an average error under 1.0 mm, eliminating the dependence on CT and manual work. Additionally, high tibial osteotomy simulation was performed by experts on reconstructed bone models, demonstrating that bone models reconstructed from biplanar X-rays have comparable clinical applicability to those annotated from CT. Overall, our approach accelerates the process, reduces radiation exposure, enables intraoperative guidance, and significantly improves the practicality of bone models, offering transformative applications in orthopedics.
We demonstrate room-temperature nucleation and manipulation of topological spin textures in the van der Waals (vdW) ferromagnet Fe3GaTe2 using laser-pulse excitation. Rapid laser-induced heating followed by cooling enables access to the skyrmion bubble state at low fields and drives reversible switching between this state and labyrinth domains. The switching requires a minimum of about 20 pulses, and further reduction of the pulse number is limited by sample degradation at higher fluence. The nucleation occurs at magnetic induction fields as low as 5 mT, which substantially lowers the field requirement compared to slow field-cooling approaches. Micromagnetic simulations attribute this switching to the thermal cycle induced by the laser. Our findings establish vdW ferromagnets as promising candidates for room-temperature, laser-controlled, non-volatile memory storage applications.
Filtering surface acoustic wave (SAW) signals of specified frequencies depending on the strength of an external magnetic field in a magnetostrictive material has garnered significant interest due to its potential scientific and industrial applications. Here, we propose a device that achieves selective SAW attenuation by instead programming its internal magnetic state. To this end, we perform micromagnetic simulations for the magnetoelastic interaction of the Rayleigh SAW mode with spin waves (SWs) in exchange-decoupled Co/Ni islets on a piezoelectric LiTaO3 substrate. Due to the islets exhibiting perpendicular magnetic anisotropy, the stray-field interaction between them leads to a shift in the SW dispersion depending on the magnetic alignment of neighboring islets. This significantly changes the efficiency of the magnetoelastic interaction at specified frequencies. We predict changes in SAW transmission of 52.0 dB/mm at 3.8 GHz depending on the state of the device. For the efficient simulation of the device, we extend a prior energy conservation argument based on analytical solutions of the SW to finite-difference numerical calculations, enabling the modeling of arbitrary magnetization patterns like the proposed islet-based design.
In materials science, we have been increasing the number of constituent elements in an alloy and compounds to improve their properties. For example, in magnetism and spintronics, ternary alloys, such as NdFeB and CoFeB have been developed and widely used in permanent magnets and memories/sensors, respectively. It has now been considered to be a time to add more elements to further explore their horizon. For such a complicated development, a manual systematic study is no longer practical, leading to the utilisation of machine learning to predict a candidate. These candidates can then be additionally screened by ab initio calculations before experimental confirmation, which can be performed routinely. Additional use of quantum annealing may also broaden the adoptability of machine learning on the materials development. In this perspective, we plan to offer a standardised process for such a development with some requirements for improvement.
The interplay of electronic charge, spin, and orbital currents, coherently driven by picosecond long oscillations of light fields in spin-orbit coupled systems, is the foundation of emerging terahertz lightwave spintronics and orbitronics. The essential rules for how terahertz fields interact with these systems in a nonlinear way are still not understood. In this work, we demonstrate a universally applicable electronic nonlinearity originating from spin-orbit interactions in conducting materials, wherein the interplay of light-induced spin and orbital textures manifests. We utilized terahertz harmonic generation spectroscopy to investigate the nonlinear dynamics over picosecond timescales in various transition metal films. We found that the terahertz harmonic generation efficiency scales with the spin Hall conductivity in the studied films, while the phase takes two possible values (shifted by π), depending on the d-shell filling. These findings elucidate the fundamental mechanisms governing non-equilibrium spin and orbital polarization dynamics at terahertz frequencies, which is relevant for potential applications of terahertz spin- and orbital-based devices.
We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The library implements a novel nodal finite-difference discretization scheme that provides improved accuracy over traditional finite-difference methods without increasing computational complexity. NeuralMag is particularly well-suited for solving inverse problems, especially those with time-dependent objectives, thanks to its automatic differentiation capabilities. Performance benchmarks show that NeuralMag is competitive with state-of-the-art simulation codes while offering enhanced flexibility through its Python interface and integration with high-level computational backends.
Molecular and lattice vibrations are able to couple to the spin of electrons and lead to their relaxation and decoherence. Ab initio simulations have played a fundamental role in shaping our understanding of this process but further progress is hindered by their high computational cost. Here we present an accelerated computational framework based on machine-learning models for the prediction of molecular vibrations and spin-phonon coupling coefficients. We apply this method to three open-shell coordination compounds exhibiting long relaxation times and show that this approach achieves semi-to-full quantitative agreement with ab initio methods reducing the computational cost by about 80%. Moreover, we show that this framework naturally extends to molecular dynamics simulations, paving the way to the study of spin relaxation in condensed matter beyond simple equilibrium harmonic thermal baths.
The inverse design approach in magnonics exploits the wave nature of magnons and machine learning to develop logical devices with functionalities that exceed the capabilities of analytical methods. While promising for analog, Boolean, and neuromorphic computing, current implementations face memory limitations that hinder the design of complex systems. This study presents a level-set parameterization method for topology optimization, combined with an adjoint-state approach for memory-efficient simulation of magnetization dynamics. The framework is implemented in NeuralMag, a GPU-accelerated micromagnetic solver featuring a nodal finite-difference scheme and automatic differentiation tools. To validate the method, we optimized the shape of a magnetic nanoparticle by applying constraints to the objective function, and designed a 300 nm-wide yttrium iron garnet demultiplexer achieving frequency-selective spin-wave separation. These results highlight the algorithm's efficiency in exploring local minima across various initial configurations, establishing its utility as a versatile tool for the inverse design of magnonic logic devices.
Maximally-localized Wannier functions (MLWFs) are widely employed as an essential tool for calculating the physical properties of materials due to their localized nature and computational efficiency. Projectability-disentangled Wannier functions (PDWFs) have recently emerged as a reliable and efficient approach for automatically constructing MLWFs that span both occupied and lowest unoccupied bands. Here, we extend the applicability of PDWFs to magnetic systems and/or those including spin-orbit coupling, and implement such extensions in automated workflows. Furthermore, we enhance the robustness and reliability of constructing PDWFs by defining an extended protocol that automatically expands the projectors manifold, when required, by introducing additional appropriate hydrogenic atomic orbitals. We benchmark our extended protocol on a set of 200 chemically diverse materials, as well as on the 40 systems with the largest band distance obtained with the standard PDWF approach, showing that on our test set the present approach delivers a success rate of over 98% in obtaining accurate Wannier-function interpolations, defined as an average band distance below 20 meV between the DFT and Wannier-interpolated bands, up to 2 eV above the Fermi level for metals or above the conduction band minimum for insulators (and a 100% success rate when including only bands up to 1 eV above these values).
Ultrafast heating of FeRh by a femtosecond laser pulse launches a magneto-structural phase transition from an antiferromagnetic to a ferromagnetic state. Aiming to reveal the ultrafast kinetics of this transition, we studied magnetization dynamics with the help of the magneto-optical Kerr effect in a broad range of temperatures (from 4 K to 400 K) and magnetic fields (up to 25 T). Three different types of ultrafast magnetization dynamics were observed and, using a numerically calculated H-T phase diagram, the differences were explained by different initial states of FeRh corresponding to a (i) collinear antiferromagnetic, (ii) canted antiferromagnetic and (iii) ferromagnetic alignment of spins. We argue that ultrafast heating of FeRh in the canted antiferromagnetic phase launches practically the fastest possible emergence of ferromagnetism in this material. The magnetization emerges on a time scale of 2 ps, which corresponds to the earlier reported time scale of the structural changes during the phase transition.
Chiral crystals, due to the lack of inversion and mirror symmetries, exhibit unique spin responses to external fields, enabling physical effects rarely observed in high-symmetry systems. Here, we show that materials from the chiral dichalcogenide family TM3X6 (T = 3d, M = 4d/5d, X = S) exhibit persistent spin texture (PST) - unidirectional spin polarization of states across large regions of the reciprocal space - in their nonmagnetic metallic phase. Using the example of NiTa3S6 and NiNb3S6, we show that PSTs cover the full Fermi surface, a rare and desirable feature that enables efficient charge-to-spin conversion and suggests long spin lifetimes and coherent spin transport above magnetic ordering temperatures. At low temperatures, the materials that order antiferromagnetically become chiral altermagnets, where spin textures originating from spin-orbit coupling and altermagnetism combine in a way that sensitively depends on the orientation of the Néel vector. Using symmetry analysis and first-principles calculations, we classify magnetic ground states across the family, identify cases with weak ferromagnetism, and track the evolution of spin textures and charge-to-spin conversion across magnetic phases and different Néel vector orientations, revealing spin transport signatures that allow one to distinguish Néel vector directions. These findings establish TM3X6 as a tunable platform for efficient charge-to-spin conversion and spin transport, combining structural chirality, persistent spin textures, and altermagnetism.
Topological defects play a crucial role across various fields, mediating phase transitions and macroscopic behaviors as they propagate through space. Their role as robust information carriers has also generated much attention. However, controlling their motion remains challenging, especially towards achieving motion along well-defined paths, which typically require predefined structural patterning. Here, we demonstrate the tunable, unidirectional motion of topological defects in a laterally unconfined thin film. The motion of these defects-specifically magnetic dislocations-is shown to mediate the overall continuous rotation of the stripe pattern in which they are embedded. We determine the connection between the unidirectional motion of dislocations and the underlying three-dimensional (3D) magnetic structure by performing 3D magnetic vectorial imaging with in situ magnetic fields. A minimal model for dislocations in stripe patterns that encodes the symmetry breaking induced by the external magnetic field reproduces the motion of dislocations that facilitate the 2D rotation of the stripes, highlighting the universality of the phenomenon. This work establishes a framework for studying the field-driven behavior of topological textures and designing materials that enable well-defined, controlled motion of defects in unconfined systems, paving the way to manipulate information carriers in higher-dimensional systems.
The conversion efficiency from charge current to spin current via the spin Hall effect is evaluated by the spin Hall ratio (SHR). Through state-of-the-art ab initio calculations involving both charge conductivity and spin Hall conductivity, we report the SHRs of the III-V monolayer family, revealing an ultrahigh ratio of 0.58 in the hole-doped GaAs monolayer. In order to find more promising 2D materials, a descriptor for high SHR is proposed and applied to a high-throughput database, which provides the fully relativistic band structures and Wannier Hamiltonians of 216 exfoliable monolayer semiconductors and has been released to the community. Among potential candidates for high SHR, the MXene monolayer Sc2CCl2 is identified with the proposed descriptor and confirmed by computation, demonstrating the descriptor validity for high SHR materials discovery.
Recently, MnTe was established as an altermagnetic material that hosts spin-polarized electronic bands as well as anomalous transport effects like the anomalous Hall effect. In addition to these effects arising from altermagnetism, MnTe also hosts other magnetoresistance effects. Here, we study the manipulation of the magnetic order by an applied magnetic field and its impact on the electrical resistivity. In particular, we establish which components of anisotropic magnetoresistance are present when the magnetic order is rotated within the hexagonal basal plane. Our experimental results, which are in agreement with our symmetry analysis of the magnetotransport components, showcase the existence of an anisotropic magnetoresistance linked to both the relative orientation of current and magnetic order, as well as crystal and magnetic order. Altermagnetism is manifested as a three-fold component in the transverse magnetoresistance which arises due to the anomalous Hall effect.
Magnetic random access memory (MRAM) is a leading emergent memory technology that is poised to replace current non-volatile memory technologies such as eFlash. However, controlling and improving distributions of device properties becomes a key enabler of new applications at this stage of technology development. Here, we introduce a non-contact metrology technique deploying scanning NV magnetometry (SNVM) to investigate MRAM performance at the individual bit level. We demonstrate magnetic reversal characterization in individual, <60 nm-sized bits, to extract key magnetic properties, thermal stability, and switching statistics, and thereby gauge bit-to-bit uniformity. We showcase the performance of our method by benchmarking two distinct bit etching processes immediately after pattern formation. In contrast to ensemble averaging methods such as perpendicular magneto-optical Kerr effect, we show that it is possible to identify out of distribution (tail-bits) bits that seem associated to the edges of the array, enabling failure analysis of tail bits. Our findings highlight the potential of nanoscale quantum sensing of MRAM devices for early-stage screening in the processing line, paving the way for future incorporation of this nanoscale characterization tool in the semiconductor industry.
Quantum magnonics investigates the quantum-mechanical properties of magnons, such as quantum coherence or entanglement for solid-state quantum information technologies at the nanoscale. The most promising material for quantum magnonics is the ferrimagnetic yttrium iron garnet (YIG), which hosts magnons with the longest lifetimes. YIG films of the highest quality are grown on a paramagnetic gadolinium gallium garnet (GGG) substrate. The literature has reported that ferromagnetic resonance (FMR) frequencies of YIG/GGG decrease at temperatures below 50 K despite the increase in YIG magnetization. We investigated a 97 nm-thick YIG film grown on 500 μm-thick GGG substrate through a series of experiments conducted at temperatures as low as 30 mK, and using both analytical and numerical methods. Our findings suggest that the primary factor contributing to the FMR frequency shift is the stray magnetic field created by the partially magnetized GGG substrate. This stray field is antiparallel to the applied external field and is highly inhomogeneous, reaching up to 40 mT in the center of the sample. At temperatures below 500 mK, the GGG field exhibits a saturation that cannot be described by the standard Brillouin function for a paramagnet. Including the calculated GGG field in the analysis of the FMR frequency versus temperature dependence allowed the determination of the cubic and uniaxial anisotropies. We find that the total crystallographic anisotropy increases more than three times with the decrease in temperature down to 2 K. Our findings enable accurate predictions of the YIG/GGG magnetic systems behavior at low and ultralow millikelvin temperatures, crucial for developing quantum magnonic devices.
Recently the field of cavity magnonics, a field focused on controlling the interaction between magnons and photons confined within microwave resonators, has drawn significant attention as it offers a platform for enabling advancements in quantum- and spin-based technologies. Here, we introduce excitation vector fields, whose polarisation and profile can be easily tuned in a two-port cavity setup, thus acting as an effective experimental dial to explore the coupled dynamics of cavity magnon-polaritons. Moreover, we develop theoretical models that accurately predict and reproduce the experimental results for any polarisation state and field profile within the cavity resonator. This versatile experimental platform offers a new avenue for controlling spin-photon interactions by manipulating the polarisation of excitation fields. By introducing real-time tunable parameters that control the polarisation state, our experiment delivers a mechanism to readily control the exchange of information between hybrid systems.
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.