Papers

Quantum many-body scars are rare exceptions to thermalization: they sustain non-thermal stationary states without the protection of any local conservation law, and are generally expected to be fragile. Here we construct an analytically tractable random unitary circuit hosting a single scar, and derive from first principles the thermalization mechanism governing perturbations thereof - described by a picture of fluctuating interfaces. Surprisingly, despite being thermodynamically irrelevant for local observables, the scar leaves a sharp fingerprint in the entanglement dynamics, driving a transition as a function of perturbation strength that is not probed by any local measurement.

Fibrous networks are ubiquitous structural components in biology, spanning cellulose in plant cell walls, fibrin in blood clots, and collagen in the extracellular matrix of animal tissues. Theoretical models predict that network connectivity critically influences their mechanical behavior. However, accurately reconstructing network topology from 3D image data remains a major challenge as current segmentation methods are not designed to preserve network topology and often rely on intensity-based thresholding, which can fragment fibers and distort junction connectivity. Here, we introduce ToFiE, an open-source topology-aware fiber extraction workflow for reconstructing dense and heterogeneous fibrous networks from high resolution microscopy images while preserving connectivity in three dimensions. We validate ToFiE using synthetic fluorescence microscopy images of fiber networks with varying topologies and signal-to-noise ratios. We further demonstrate its performance by reconstructing the fiber networks of a library of collagen gels with various microstructures, imaged using confocal fluorescence microscopy. Altogether, the results establish ToFiE as a practical semi-automated framework for extracting mechanically relevant network information from imaging data across a broad range of fibrous materials.

We investigate the magnetotransport properties of the one-band Hubbard-Holstein model at half-filling in three dimensions (3D) using exact diagonalization based semi-classical Monte Carlo simulations with phonons treated in the adiabatic limit. The low-temperature electronic correlation $U$ vs electron-phonon coupling $V$ phase diagram reveals two insulating phases–antiferromagnetic (AF-I) and charge-ordered (CO-I)–separated by a first-order transition, with no metallic phase observed at their intersection, indicating robustness of these phases in 3D. For $U \sim bandwidth$, the $V$ vs temperature $T$ phase diagram exhibits multiple phases including AF-I, CO-I, Mott-Hubbard insulator, bipolaronic insulator, and two bipolaronic metallic states. Several first-order transitions occur near $V \sim 3.75$. Above the ordering temperature, the density of states shows universal behavior dominated by electronic contribution, while susceptibility and DOS analyses reveal pseudogap features. Magnetic and transport properties along the phase boundary highlight strong proximity effects between competing phases, suggesting routes for tuning correlated materials and emergent electronic states.

Dark and subradiant states have emerged as a promising resource for stabilizing open quantum batteries against dissipation, but existing studies are largely limited to qubit ensembles and symmetry-based constructions. Here we introduce a systematic, thermodynamically consistent framework for identifying long-lived energy storage states in interacting multilevel quantum batteries, combining the Davies master equation with a Morris-Shore (MS)-type decomposition of dissipative coupling blocks. Focusing on a minimal model of two interacting qutrits coupled to a common bath, we analytically construct dark, bright, and funnel states-excited states that decay exclusively into protected manifolds. We also derive quantitative robustness conditions governed by the ratio of interaction strength to anharmonicity. We show that multilevel ladder structure and exchange interactions enable energetic storage states beyond the qubit case. Numerical simulations confirm that these states exhibit long-lived energy storage under typical Markovian dissipation. Finally, we show that high-energy funnel states provide a natural design target for multilevel quantum batteries, as their decay pathways are highly structured and directed toward protected manifolds. The knowledge and understanding of these pathways offers a principled basis for developing future protection and control strategies in superconducting multilevel platforms.

We determine the fully resolved equilibrium density profiles for two binary hard-sphere crystal structures using classical density functional theory through the White Bear II functional from fundamental measure theory. While for the substitutional crystal, in which some hard spheres are replaced by spheres of slightly smaller diameter, the density profiles are rather similar to the single-component case (narrow Gaussian peaks centered at fcc lattice sites), we observe a more complex behavior for the case of interstitial solid solutions, where the small species is fairly delocalized in the unit cell. Further, we compute the species-resolved inhomogeneous two-body direct correlation functions, depending on two three-dimensional vectors, for these two types of binary crystals. The large–large components are mainly determined by the vacancy concentration $n_\text{vac}$ and show a characteristic magnitude $\sim 1/n_\text{vac}$. Based on this observation, we propose a simple geometric picture. The components of the direct correlation function involving the small spheres substantially differ in interstitial solid solutions from those of the substitutional crystal.

Understanding how coherent quantum dynamics give way to correlation-dominated behavior in low-dimensional systems remains a central challenge in quantum many-body physics. Here, we investigate a one-dimensional Bose-Josephson junction confined in a box trap using the multiconfigurational time-dependent Hartree method for bosons (MCTDHB). By varying the interaction strength and initial population imbalance, we identify distinct dynamical regimes governed by the competition between coherence and correlation-induced fragmentation. Weak interactions support coherent Josephson oscillations, whereas increasing imbalance leads to damping. At intermediate interaction strength, varying only the initial imbalance induces a crossover from nearly pure coherent oscillations to many-body dephasing with collapse-and-revival dynamics, and ultimately to equilibration accompanied by strong fragmentation and the saturation of many-body observables. In the strongly interacting regime, the system enters a dynamical freezing regime characterized by pronounced fragmentation, well-separated particle-resolved density peaks, and strongly suppressed tunneling. A systematic comparison with the Bose-Hubbard model reveals excellent agreement in the weakly interacting regime, while progressively larger deviations emerge as higher-orbital occupations beyond the two-mode approximation become significant. These results provide a unified picture of the emergence and competition of coherence, many-body dephasing, equilibration, and dynamical freezing, while delineating the regime of validity of the Bose-Hubbard description.

General-purpose programmable photonic processors are considered a crucial technology because they combine the ultra high-speed, massive bandwidth, and energy efficiency of light-based computing with the flexibility of software-defined hardware. Unlike application-specific photonic integrated circuits (ASPIC) designed for one task, these processors use reconfigurable waveguide meshes to implement various functions, such as switching, filtering, or AI computation, on a single chip, allowing for rapid prototyping and versatile, on-demand hardware redefinition. Here we report a recirculating bricks mesh architecture that can be easily implemented in photonic neural networks. It will be shown that a single programmable optical system is capable of performing various functions depending on the requirements. In particular, we will show that the same network, after being reprogrammed, can perform many different functions, ranging from a crossbar network to optical interference circuits with variable structures, which can then be subjected to Singular Value Decomposition. Furthermore, the "bricks" mesh serves as an excellent foundation for implementing a monitoring system capable of monitoring the power in each location of the circuit and, subsequently, sel-fcalibrating and stabilizing the circuit using a feedback loop.

Ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is a powerful approach for modeling molten salts. However, standard exchange-correlation functionals often neglect dispersion interactions, introducing potential errors in property predictions. Dispersion corrections are commonly applied ad hoc to match experimental salt densities, but their systematic impact on predicting structure, thermophysical, and transport properties of salt remains unexamined. This study evaluates the impact of Grimme's DFT-D and nonlocal van der Waals (vdW-DF) corrections on molten fluorides of Group-I (LiF, NaF, KF) and Group-II (BeF$_2$, MgF$_2$, CaF$_2$), which are relevant to reactor applications. Results indicate that dispersion corrections have a minor effect on binding energies but significantly influence density predictions. Systematic benchmarking across compositions and temperatures reveals that semi-empirical dispersion models often produce more accurate densities compared to vdW-DF. Diffusion coefficients remain largely invariant to dispersion corrections at fixed densities, while coordination number distributions exhibit notable differences based on chosen dispersion. BeF$_2$, in particular, deviates from other fluorides, showing pronounced structural and dynamical differences in the absence of dispersion corrections. This highlights the necessity of dispersion effects for high-charge-density cations that promote intermediate- to long-range ordering. These findings provide a systematic framework for selecting dispersion models in molten salt simulations, improving density and structural predictions.

We study a two-species Vicsek model with intra-species alignment and asymmetric inter-species couplings, where one species aligns with the other while the latter anti-aligns. Motivated by recent results showing that globally coherent chiral motion is not a generic large-scale state of finite-range nonreciprocal flocking, we ask whether a chiral state can nevertheless be stabilized in the discrete-time, metric, nonreciprocal two-species Vicsek model, and if so, under what conditions. For equal populations and motilities, we find that the global chiral state is a long-lived finite-time state rather than an asymptotically stable phase at nonzero motility. Its breaking time increases rapidly as the self-propulsion speed is reduced or the strength of the nonreciprocal coupling is enhanced, explaining why systems with very low motility can appear stably chiral over conventional simulation timescales. This finite-time chiral regime is further limited to high-density systems and to system sizes that are small relative to the interaction range. Within this window, we also find that chirality appears primarily when aligning interactions dominate over anti-alignment, whereas stronger anti-alignment leads to species segregation and suppresses chirality. Conversely, introducing species asymmetry through population imbalance drives transitions from the finite-time chiral regime to porous parallel-flocking or anti-parallel-flocking liquids; motility imbalance induces asynchronous oscillations and, in extreme cases, leads to segregation into moving clusters of the faster species within a more dispersed background of slower particles. Overall, these results indicate that chirality in the nonreciprocal two-species Vicsek model arises within a restricted regime set by density, motility, inter-species coupling, and system size, rather than being a generic outcome of nonreciprocal interactions.

Can contagion components be identified in aggregated default counts when default probabilities fluctuate with the economic environment? We study this question as an identifiability problem for coarse-grained default-count distributions. We adopt a binomial mixture with a Probit–Normal default probability as a parsimonious null model for environmental fluctuations and test whether contagion provides additional identifiable structure. Against this null, we consider two contagion mechanisms: cumulative contagion in the Davis–Lo model and threshold-type contagion in the Torri model. The Davis–Lo extension substantially improves the likelihood and information criteria for the ALL and speculative-grade classes, whereas the Torri component is largely absorbed by environmental heterogeneity; evidence is weaker for investment-grade defaults. As a robustness test, we replace the Gaussian environmental distribution by a two-component Gaussian mixture, and the Davis–Lo advantage persists for ALL and speculative-grade defaults. These results indicate that cumulative-contagion signatures remain distinguishable from the environmental null models considered here in the ALL and speculative-grade classes, even under annual aggregation.

Magnetic switching in antiferromagnets relies on Neel spin orbit torque (NSOT), which originates from a current-induced staggered spin polarization of itinerant electrons. In collinear antiferromagnets, such a response requires the spin susceptibility to be odd under combined space-time inversion symmetry (PT), and is conventionally attributed to symmetric scattering processes. Here, we demonstrate that asymmetric impurity scattering generates an additional PT-odd spin polarization when coupled with the anomalous spin polarizability (ASP) of Bloch electrons. This extrinsic contribution arises from the interplay between antisymmetric higher-order scattering processes and band geometry, effectively converting an otherwise PT-even susceptibility into a staggered spin polarization. Using a minimal model of tetragonal CuMnAs, we show that this anomalous skew-scattering contribution can be comparable to, and with sufficient impurity density even exceed, the conventional symmetric scattering (Drude) contribution. Our results identify a new band-geometry-driven mechanism for NSOT and establish an efficient route for electrical control of antiferromagnets.

Ferroelectric higher-order topological insulators (HOTIs) exhibit tunable physical properties arising from the interplay between ferroelectric polarization and band topology. This work investigates the topological origin of two classes of two-dimensional (2D) ferroelectric HOTIs with out-of-plane or in-plane polarization, revealing their distinct orbital transport behaviors and the mechanism for engineering orbital Hall conductivity (OHC) via polarization control. Our results demonstrate the unique role of polarization in modulating both the higher-order band topology and orbital transport. A strong coupling between in-plane polarization and higher-order topology is identified, establishing in-plane polarization as an intrinsic means to reversibly switch the OHC plateau within the band gap. Using Tl$_2$S and SnS as representative models of the two HOTI types, we demonstrate persistent and electrically switchable orbital transport, respectively. Our study advances the understanding of the coupling among ferroelectricity, higher-order topology, and orbital transport, offering new avenues for controllable orbitronics.

Functional magnetic composites capable of large deformation, load bearing, and multifunctional motion are essential for next-generation adaptive soft robots. Here, we present muscle-inspired magnetic actuators (MMA), additively manufactured from a thermoplastic/permanent magnet polyurethane/Nd2Fe14B (TPU/MQP-S) composite using laser powder bed fusion (LPBF). By tuning the laser-energy scale between 1.0 and 3.0, both mechanical stiffness and magnetic response are precisely controlled: the tensile strength increases from 0.28 to 0.99 MPa while maintaining 30-45% elongation at break. This process enables the creation of 0.5 mm-thick flexural hinges, which reversibly bend and fold under moderate magnetic fields without damage. Two actuator types are reported showing the system versatility. The elongated actuator with self-weight of 1.57 g, magnetized in its contracted state, achieves linear contraction under a 500 mT field, lifting 50 g (32x its own weight) and sustaining performance over at least 50 cycles. Equipped with anisotropic frictional feet, it supports movement of a magnetic crawling robot that achieves up to 100% locomotion success on textured substrates. The expandable actuator exhibits reversible opening and closing under a 300 mT field, reliably grasping and releasing different objects, including soft berries and rigid 3D printed geometries. It can also anchor in a tube while holding suspended 50 g loads. This work demonstrates a LPBF-based strategy to program both stiffness and magnetization within a single material system, enabling remotely driven, reconfigurable, and fatigue-resistant soft actuators. The approach opens new possibilities for force controlled, multifunctional magnetic soft robots for adaptive gripping, locomotion, and minimally invasive manipulation of biomedical tools.

Materials informatics is increasingly used to support modelling, analysis and design across the length scales of materials science, from atomistic simulations to microstructural characterisation and continuum descriptions. Despite rapid progress, the reliability and transferability of these approaches vary strongly with scale. Here we survey data-driven methods at the nanoscale, mesoscale, and micro-to-continuum levels, highlighting established capabilities as well as unresolved challenges. Machine-learning interatomic potentials, mesoscale surrogate and operator-learning models, and learning-based analysis of experimental microstructures are discussed, with emphasis on data quality, uncertainty, interpretability, and cross-scale consistency. We further examine the role of data standards, ontologies, and emerging tools, such as autonomous laboratories, where they directly affect multiscale workflows. This perspective clarifies what can be considered reliable today and identifies key obstacles to the broader integration of materials informatics across scales.

Thermodiffusion in aqueous electrolyte solutions exhibits complex dependencies on temperature, concentration, and salt composition, yet its microscopic origins remain incompletely understood. Here, we employ non-equilibrium molecular dynamics (NEMD) simulations to investigate thermal transport and thermodiffusion in aqueous alkali halide solutions over the temperature range 240-300 K at concentrations of 1 m and 4 m. Building on previous studies of NaCl and LiCl, we extend the analysis to systems containing K$^+$ and I$^-$ ions to assess ion-specific effects. Across all systems studied, the thermal conductivity decreases upon cooling and is generally reduced at higher salt concentration. The Soret coefficient generally increases with temperature, shifting the solutions from thermophilic behavior at low temperature toward more thermophobic behavior at high temperature. Clear ion-dependent trends are observed, with Na$^+$ and K$^+$ salts generally showing stronger thermophobic responses than Li$^+$ salts, especially in iodide solutions. We estimate that the shift in the inversion temperatures of the iodide salts relative to experiment corresponds to a small local offset of the effective heat of transport, 4-5 kJ/mol, showing that small changes in hydration thermodynamics or heat-mass coupling can strongly affect the sign change of the Soret coefficient. Structural analyses indicate that lower temperatures and lower concentrations favor more tetrahedrally ordered, LDL-like water environments, which are associated with enhanced thermophilicity. Analysis of inversion temperatures and mass effects further suggests that the heat of transport contains both structural and kinetic contributions. These findings provide molecular-level insight into the interplay between hydration structure, ionic mass, and thermodiffusive transport in aqueous electrolytes.

Ferrimagnetic skyrmions offer enhanced tunability due to antiferromagnetically coupled sublattices and reduced net magnetization. In chiral magnetic films at zero magnetic field, skyrmion stability is commonly characterized by a dimensionless parameter $\kappa$, yet its applicability to ferrimagnetic systems remains unclear, as most studies assume a fixed, strong inter-sublattice exchange coupling $J$. Here we investigate how variations in $J$ govern relaxed stable and metastable ferrimagnetic skyrmion configurations and introduce a dimensionless parameter $\zeta_{eff}$ to characterize the crossover between strong and weak inter-sublattice locking. In the strong-coupling regime, inter-sublattice locking enables stabilization of skyrmion in a sublattice where intrinsic Dzyaloshinskii-Moriya interaction is absent while the other sublattice has finite DMI, yielding a sublattice with DMI-free ferrimagnetic skyrmions. As $J$ decreases, this locking breaks down, leading to independent sublattice behavior and the failure of an effective $\kappa$-based description. Our results establish a unified framework linking inter-sublattice exchange and skyrmion phase stability in ferrimagnetic systems.

Quantum gases confined in reduced dimensions have enabled the observation of many exotic quantum phenomena. While existing experiments primarily focus on homonuclear systems, we report here on the efficient preparation of a quasi-two-dimensional (2D) heteronuclear quantum degenerate mixture of ultracold $^{23}$Na and $^{87}$Rb. We describe the design of the vacuum system and detail the experimental procedures for preparing the 2D quantum mixture. The designed apparatus has several unique features, including compact and modular 2D-MOT sources, a science chamber that accommodates various lattice geometries, a precision in-vacuum electrode assembly, and high-resolution imaging for both atomic species. After loading the dual-species condensate into a single layer of a vertical optical lattice, we prepare a 2D gas mixture and observe quantum immiscibility in the in-situ equilibrium density profiles. The observed density profiles agree well with mean-field theories. The apparatus provides a versatile platform for investigating several interesting problems, including quantum impurities, quantum droplets, or polar molecules in low dimensions.

We introduce a parameter estimation method that utilizes microscopic data, specifically averages and correlations of selected microscopic observables, to determine the parameters of a stochastic differential equation governing coarse-grained degrees of freedom. The method is not limited to static parameters found in the reversible part of the coarse-grained dynamics, such as those in the free energy function or potential of mean force, but also extends to dynamic parameters, including friction coefficients. The method couples the stochastic differential equation with free parameters to dynamic equations for the parameters. The coupled system self-averages, according to Anosov-Kifer's theorem, in such a way that the final state of the parameters gives coincidence between the microscopic and mesoscopic averages and correlations of selected observables. The method is validated in two examples: a Brownian particle in a harmonic potential, and a set of Brownian particles interacting hydrodynamically with the Rotne-Prager-Yamakawa mobility tensor. This latter case illustrates how the method can be used not only to determine coefficients but also state dependent transport properties - in this case, the position dependent form of the mobility tensor. The parameter estimation for these two models yields excellent results. Subsequently we use the methodology to study a bimodal-mass Lennard-Jones fluid for which we infer both the potential of mean force between the heavy particles and its hydrodynamic mobility tensor.

Magneto-active elastomers (MAEs) are polymer composites consisting of magnetic microparticles embedded in an elastomeric matrix. These materials exhibit strong magneto-mechanical coupling under external magnetic fields, resulting in tunable stiffness, reversible shape changes, and nonlinear magnetic responses. This study presents a multiscale theoretical framework to investigate the origin of magnetic hysteresis in MAEs, with emphasis on the evolution of the internal microstructure during magnetization and demagnetization. The total energy of the system is formulated as the sum of magnetic and micromechanical contributions, while macroscopic deformation of a cylindrical MAE sample is fully constrained. Particle interactions are modeled first via pure dipole-dipole interactions and then extended to include higher-order near-field effects at close particle separations. The results show that hysteresis in MAEs with magnetically soft particles primarily arises from trapped microstructural rearrangements, leading to distinct particle configurations under increasing and decreasing magnetic fields. Parametric studies demonstrate that particle volume fraction, sample aspect ratio, and matrix stiffness strongly influence the microstructure evolution and the width of resulting hysteresis loops. The proposed framework provides a solid foundation for modeling magnetic hysteresis, which is essential for the design and optimization of MAEs in practical applications.

Impurities embedded in a Bose-Einstein Condensate (BEC) of 39K atoms are investigated with a pump-probe ejection spectroscopy sequence. The spectroscopic signal exhibits a strong feature corresponding to a Bose polaron in agreement with prior injection spectroscopy and theory. In addition, significant spectral weight at energies well below the energy of the polaron is observed, which is absent in injection spectroscopy. The energy and spectral weight of this signal are measured as a function of interaction strength and evolution time between the pump and probe pulses. We tentatively compare these results to two different theoretical models: a low-energy impurity state dressed by many bosonic excitations and a bipolaron state formed by two polarons due to attractive interactions mediated by the BEC. Such states can exist due to the large compressibility of the weakly interacting BEC. Both theories predict ejection spectra consistent with the low-energy signal, but only the bipolaron model is compatible with its spectral weight. These results indicate that lowenergy states below the usual polaron exist for strong interactions, calling for further experimental investigations

The inverse Potts problem for estimating evolutionary single-site fields and pairwise couplings in homologous protein sequences from their single-site and pairwise amino acid frequencies observed in their multiple sequence alignment would be still one of useful methods in the studies of protein structure and evolution. Since the reproducibility of fields and couplings are the most important, the Boltzmann machine method is employed here, although it is computationally intensive. In order to reduce computational time required for the Boltzmann machine, parallel, persistent Markov chain Monte Carlo method is employed to estimate the single-site and pairwise marginal distributions in each learning step. Also, stochastic gradient descent methods are used to reduce computational time for each learning. Another problem is how to adjust the values of hyperparameters; there are two regularization parameters for evolutionary fields and couplings. The precision of contact residue pair prediction is often used to adjust the hyperparameters. However, it is not sensitive to these regularization parameters. Here, they are adjusted for the fields and couplings to satisfy a specific condition that is appropriate for protein conformations. This method has been applied to eight protein families.

Neutron stars make a unique astrophysical test bench for our understanding of quantum physics at kilometre scales. The rotation of a neutron star features glitches, sudden spin-ups that interrupt the otherwise regular stellar spin-down, which are often attributed to the dynamics of pinned quantised vortices in one or several of the superfluid phases inside the star. Laboratory experiments probing superfluid vortices have inspired neutron star theory and simulations from the beginning. Here we argue that vortex experiments in superfluids contained in aerogels show phenomenology that offers a highly appealing but vastly unexplored analogue for neutron star physics. We build a point-vortex simulation that allows analysing experiments in a crust-like and a core-like aerogel, extracting two different regimes of pinned vortex (non-)dynamics and validating a microscopic picture of very strong vortex pinning. In the crust-like aerogel, vortices get depinned once the ambient superflow is fast enough, while in the core-like aerogel pinned vortices are never released and rotational velocity changes are accommodated by the avalanche-like production of new vortices instead. Finally, we show that these concepts should apply also in neutron stars and may thus revolutionise the analysis of neutron star observations.

Self-organizing memristive networks are physical circuits that dynamically reconfigure their circuitry in response to external input signals. Their adaptive behavior arises from intrinsic neuro-synaptic dynamics combined with a heterogeneous network topology. In this work, we demonstrate that such networks naturally generate neuronal population spiking dynamics similar to those observed in biological neuronal systems. This study investigates the intrinsic and emergent dynamics of memristive networks mathematically and numerically for both DC and AC input signals. Nonlinear spike-like features are maximized when the frequency of the input driving signal matches the network's intrinsic dynamical timescale, where nonlinear resonance is observed. Furthermore, the optimal frequency for computation is found to be the maximal frequency before the onset of resonance.

Magnetic materials hosting topological band structures have attracted intense interest due to the interplay between magnetism and spin-orbit coupling (SOC). Here, using temperature- and polarization-dependent angle-resolved photoemission spectroscopy, we investigate the surface ferromagnet GdAg$_2$/Ag(111), a two-dimensional system with Weyl-nodal-line-like band crossings. We find that spin fluctuations preserve the nodal-line-derived band crossings even above the Curie temperature, while SOC-induced hybridization develops only at low temperatures, as evidenced by spectral-weight redistribution. The suppression of the hybridization at high temperature is attributed to spin decoherence and band-dependent scattering, captured by an effective non-Hermitian framework. Our results establish magnetic fluctuations as a control knob for SOC-induced hybridization and associated Berry curvature, and highlight magnetic systems as a platform for exploring non-Hermitian band physics.

Grain boundary plane distributions are widely used to infer the mechanisms governing grain boundary formation in polycrystalline materials. We show that such interpretations are inherently ambiguous. Using a unified eight-parameter boundary distribution framework, we derive both the grain boundary character distribution (GBCD) and the grain boundary normal distribution (GBND) and identify two limiting cases of boundary network formation. We show that in macroscopically driven networks, the crystal-frame GBND is given by a convolution of the specimen GBND with the orientation distribution function (ODF), whereas in crystallographically driven networks the specimen GBND is obtained by convolution of the crystal GBND with the ODF. This duality implies that anisotropy in the GBND may arise from macroscopic alignment effects rather than intrinsic crystallographic selection. Conversely, this relationship may be used to identify the dominant formation process in the measured mcirostructures. Evaluation of a wide variety of simulated microstructures confirm the theoretically predicted relationships between texture, GBND and GBCD. In particular, our examples confirm that the GBND or GBCD alone are not sufficient for identifying grain boundary formation mechanisms.

Novelty in materials discovery requires candidates to be distinct, non-redundant, and thermodynamically plausible. While crystallographic databases continue to expand in both size and complexity, making efficient and reliable novelty assessment has become increasingly difficult. This becomes particularly acute when crystallographic disorder is involved, as partial occupancies greatly enlarge the structure-composition space and obscure the identification of genuinely distinct structures. Here, we introduce SWORD, a symmetry-aware, Wyckoff-based string representation compatible with both ordered and disordered crystals. SWORD provides (i) standardization of symmetry-equivalent structural descriptions into a consistent label, (ii) explicitly represents co-occupying species on partially occupied sites, and (iii) quantifies complex disorder through a degree of mixing descriptor that captures continuous variation in site stoichiometry. These features enable efficient structure grouping, duplicate identification, and finer refinement of disordered structures. Benchmarking against existing fingerprint and structure-matching methods shows that SWORD remains invariant under identity-preserving transformations while retaining interpretable sensitivity to structural perturbations. In addition, SWORD shows competitive performance in associating unrelaxed and intermediate configurations with their final relaxed states along relaxation trajectories. This feature could enable more reliable novelty assessment directly from partially relaxed or even unrelaxed generated structures. Finally, SWORD was used to showcase its capability of disorder-aware database-scale deduplication and curation for the Inorganic Crystal Structure Database (ICSD). The curated ICSD would serve as the basis for the materials informatics and data-driven materials design in the era of artificial intelligence.

Magnetic solitons are nonlinear, local excitations in magnetic systems. In this study, we theoretically and numerically investigate the properties and generation of one-dimensional (1D) topologically trivial magnetic solitons in ferromagnetic nanowires. An approximate analytical soliton solution described by two free parameters is validated by comparing with the micromagnetic simulation. Across an interface between two media of different anisotropy, the reflection and refraction of a soliton are highly nonlinear that are different from the linear spin waves. A pair of magnetic solitons that propagate in opposite directions can be generated by alternately applying magnetic field or spin-polarized current pulses of opposite directions to at least two successive regions. Each soliton falls into a soliton solution that can be controlled by the generation process. These magnetic solitons can be used to drive domain wall motion over a certain distance determined by the soliton magnitude, allowing for discrete manipulation of domain walls compatible with the digital nature of information technology. Our findings pave the way for the application of topologically trivial solitons in spintronics.

The anomalous Nernst effect (ANE), generating a voltage perpendicular to a temperature gradient due to magnetization, is closely linked to the Berry curvature (BC) near the Fermi energy in topological magnets. We report an enhanced spontaneous ANE in the ferromagnetic Kondo lattice CeCo2As2, which features Kondo-screened cerium-based 4f moments embedded in a ferromagnetic d-electron framework. The observed large anomalous Nernst coefficient, greater than the Seebeck coefficient, is attributed to the strong BC present in the f-orbital-dominated flat bands. The enhanced ANE in CeCo2As2 serves as a signature of the Fermi energy pinning within the topological flat band, highlighting the correlation-driven topology in the Kondo lattice.

We study the transport properties of non-Hermitian magnetic Josephson junctions, considering a superconductor-quantum dot-superconductor device coupled to a ferromagnetic metallic reservoir in the presence of an external magnetic field. We focus on the $0-\pi$ transitions that occur when the equilibrium phase difference between the superconductors shifts from $\phi=0$ to $\phi=\pi$ upon increasing the magnetic field amplitude. The coupling to the environment induces spin-dependent dissipation and leads to the broadening of the junction Andreev levels. By combining Green's function calculations with an effective non-Hermitian description restricted to the sub-gap Andreev quasi-bound states, we show that dissipation shifts the $0-\pi$ transition to higher magnetic fields. Remarkably, also the relative angle between the applied field and the reservoir magnetization can be used to drive the transition, at fixed field magnitude. We demonstrate that this effect can be entirely ascribed to the behavior of the complex eigenvalues of the non-Hermitian Hamiltonian. These findings highlight non-Hermiticity as a resource that can introduce new control knobs for engineering the current-phase relation in superconducting junctions.

We study the phase transition of the classical $XY$ model on a diamond lattice by Monte Carlo simulations using the Wolff cluster algorithm. Finite-size scaling (FSS) analysis of the Binder cumulant and the second-moment correlation length ratio $\xi_{2\rm nd}/L$ yields $T_c = 1.30036(1)$ and $\nu = 0.671(6)$. Data collapse of both quantities confirms the three-dimensional $XY$ universality class.