Papers

Zinc-based alloys are promising biodegradable implants, yet the trade-off between strength, ductility, and bendability in thin-wire form remains poorly understood. To address this, pure Zn (PZ), Zn-0.15Mg (ZM), and Zn-0.8Mg-0.2Sr (ZMS) wires of ~290 um diameter were produced by direct extrusion, and their microstructure, texture, tensile, and bending behavior were characterized. Mg2Zn11 and SrZn13 phases promoted particle-stimulated nucleation, refining the grain size from 35.8 um in PZ to 6.8 and 3.3 um in ZM and ZMS, respectively, weakening the basal-fiber texture, and raising the ultimate tensile strength at 37 {\deg}C from about 119 MPa for PZ to 291 and 334 MPa for ZM and ZMS, respectively. PZ deformed through twin transmission across weakly misoriented grains, producing serrated stress drops in tensile curves, with further straining accommodated by non-basal slip. ZM showed a single, aging-sensitive stress drop from localized twin nucleation and particle cracking, while ZMS showed distributed particle cracking without associated stress drops, consistent with its higher elongation to failure of 19 % at 37 {\deg}C. Mechanical properties remained broadly stable after 20 days of aging at 37 {\deg}C, with no measurable change in the nanoscale precipitates after 7 days. All compositions, however, showed markedly poor cyclic bendability accompanied by a twinning-detwinning mechanism directly observed by EBSD for the first time in zinc. These results establish single-step direct extrusion as a viable route to strong, fine-grained biodegradable Zn wires, while identifying poor bendability as the principal obstacle to bending-critical biomedical devices.

Superfluorescence (SF), intense picosecond bursts from self-synchronizing dipoles, is promising for room-temperature quantum sources. Yet, whether this synchronization can convert weak structural anisotropies into robust macroscopic order remains an open question. Here, we demonstrate that polycrystalline ferroelectric 2D perovskite thin films exhibit strongly linearly polarized SF with a degree of polarization (DOP) up to 86%. Remarkably, macroscopic polarization emerges without external bias despite randomly oriented microscopic domains, suggesting a cooperative gain mechanism that amplifies weak local anisotropies by several orders of magnitude into coherent, linearly polarized light bursts. Our results establish cooperative superfluorescence as a general concept for translating microscopic anisotropy into robust macroscopic order, exemplified here for linear polarization, offering a sensitive probe of hidden material symmetries and a framework for polarized quantum light sources

Accurate generation of crystal structures is the foundation to the discovery of high-performance materials for extreme-environment applications, such as aerospace, additive manufacturing, and fusion energy systems. Although generative modeling has emerged as a promising approach for crystal design, its performance remains limited by the complex crystal structures and diverse chemical compositions. In this work, we develop UFO-MGen, a universal flow-based generative model that learns topological features of Wyckoff representations and leverages this information to accurately generate crystals across vast structural and chemical spaces. Compared with state-of-the-art generative models, UFO-MGen achieves the highest crystal generation success rate under a rigorous multi-stability evaluation framework, the highest SUN (stable, unique, novel) rate, and a remarkable extrapolation capability that has not been reported by previous models. Furthermore, a fine-tuning module is implemented to UFO-MGen for property-constrained crystal generation, enabling the inverse materials design toward target properties. The UFO-MGen opens a new avenue for accelerated materials discovery and providing a foundation for universal materials intelligence.

Simulating strongly correlated fermions remains a long-standing challenge due to the exponential complexity of the Hilbert space and the intricate sign structure of many-body wavefunctions. We introduce a variational framework centered on a neural network backflow transformation that combines deep learning with variational Monte Carlo. The proposed ansatz employs a multi-determinant expansion with low-rank shifts to capture non-local correlations and complex sign structures. Applied to the two-dimensional Hubbard model at both half-filling and $1/8$ doping, the method achieves energies within $0.45\%$ of auxiliary-field quantum Monte Carlo at half-filling and captures intertwined charge- and spin-density stripe patterns at $1/8$ doping. These results demonstrate the potential of this framework as a scalable and interpretable approach to variational simulations of strongly correlated fermionic systems.

Deep generative models are widely reported to enable de novo crystal structure prediction (CSP), but their capability has not been measured consistently against template-based methods. We evaluate 12 representative generative CSP models, spanning latent-variable, diffusion, flow-matching, autoregressive, and manifold random-walk architectures, against TCSP 2.0 on 180 test structures and a leakage-controlled subset of 46. All methods use identical structure-matching, symmetry, and consensus criteria. Template retrieval is the strongest single method, reaching 68.3% top-1 success; symmetry-aware EquiCSP (66.4%) and Uni-3DAR (62.9%) form the next tier. However, comparison with TCSP 2.0 shows that most structures correctly predicted by generative models are also correctly predicted by template substitution. Thus, the set of structures uniquely reachable by generation is small, limiting its practical advantage for discovering structures outside existing prototype libraries. To test the source of this performance, we removed entire stoichiometric prototype families from the training set and retrained the strongest generative model. Accuracy declined by 50-78% across four families, establishing that performance is substantially prototype-dependent. A small minority of structures survived removal of their prototype family, demonstrating a real but limited retrieval-independent predictive capacity. Present generative CSP models therefore function largely as implicit, softer-edged prototype libraries rather than genuinely de novo predictors. Enlarging this residual capacity, rather than aggregate match rate alone, is the central open problem.

We compute universal scaling exponents and scaling functions for massless scalar field theories far from equilibrium. Using a large-$N$ expansion to next-to-leading order, we investigate the nonperturbative infrared behaviour associated with the transport of a) energy or b) effective particle number towards low frequencies $\omega$ and momenta $\bar p$. For massless dispersion $\omega = \bar{p}$, we determine the scaling form of the distribution function whose universal power-law tail $f_S(\bar p) \sim 1/\bar{p}^\kappa$ is found to be a) $\kappa = d+1$ and b) $\kappa = d$ in $d=3$ spatial dimensions. Our results establish that relativistic and nonrelativistic theories do not belong to the same universality class in this case, which opens up new applications to far-from-equilibrium phenomena in high-energy and (quantum) many-body physics with linear dispersions.

The excitonic insulator (EI) is an electronic phase of condensed excitons. However, in many prototypical materials the presence of a concurrent structural transition complicates the identification of a purely electronic origin of the ordered state. Here, we investigate Ta2Pd3Te5, in which an EI phase develops below TC ~ 100 K in the absence of a detectable structural transition. We characterize the excitonic condensate via its coherent phonon-amplitude response. Most strikingly, an extraordinarily strong lifetime enhancement of these modes sets in below TC which we establish as a new and robust fingerprint linked to exciton condensation. We discuss possibilities to capture the enhancement when taking into account a coupling of excitonic and lattice effects in line with the coupled phonon-amplitude response. Within an order-parameter polaron picture coupling to the excitonic state may dress the phonon mode and thereby suppress its relaxation and a phenomenological model combines the anharmonic phonon response and a reduced electronic scattering due to the gap opening in the EI phase.

Driven-dissipative many-body quantum systems can be exploited in quantum enhanced sensing protocols. Here, non-classical correlations may allow one to reach a measurement precision that surpasses the standard quantum limit. The relevant figure of merit for assessing such quantum advantage is the quantum Fisher information (QFI). In a many-body setting its computation is challenging as it requires knowledge of the full system-environment state. Here we make use of a field-theoretical approach, which is particularly well suited for systems with many degrees of freedom. In particular, we exploit that the QFI can be linked to a Keldysh path-integral. This route leads to a semiclassical expression of the QFI, which is obtained through a controlled expansion in the so-called quantum fields. We benchmark the framework on three systems of increasing complexity: the driven-dissipative harmonic oscillator, the boundary time crystal - which becomes semiclassical at large system sizes - and a two-dimensional array of collectively emitting atoms. We assess in which parameter regimes the semiclassical description holds and gives access to system sizes beyond the reach of exact methods.

Strong-to-weak spontaneous symmetry breaking (SWSSB) can occur in a maximally mixed charge sector even when ordinary charged correlations vanish. We study this order when a charge sector splits into dynamically disconnected fragments. In a one-dimensional $t$-$J_z$ chain with species-resolved dephasing, each fragment is labeled by a conserved spin word. A charged transfer between separated finite intervals preserves that word only if the intervening spins satisfy a periodicity constraint. In the full-state two-point sense, we use this constraint to prove the absence of long-range fidelity and Rényi-1 order in typical fixed-composition fragments, uniformly over all norm-bounded charged operators in any prescribed finite interval. A fragment with primitive spin-word period $k$ supports a $k$-particle channel with exact long-distance value $\nu^k(1-\nu)^k/k^2$, and no charged operator on fewer than $k$ sites can carry this order. A transverse exchange connects the fragments while preserving the global charges and restores the full-sector steady state at finite size. The conserved spin word therefore selects the local channels that can support mixed-state order.

Water's anomalous properties have been hypothesized to originate from a liquid-liquid critical point in the supercooled regime, separating high- and low-density liquid states. Experimental verification remains challenging due to rapid crystallization under these conditions. A recent study reported evidence for such a transition, based primarily on a pronounced increase in the heat capacity of rapidly heated low-density amorphous ice. Here, we show that this heat capacity increase can be explained without invoking a liquid-liquid transition. By combining simulations using a machine-learning potential trained on the state-of-the-art MB-pol water model, combined with the Tool-Narayanaswamy-Moynihan (TNM) model of the glass transition, we demonstrate that the observed signal can arise instead from a dynamical effect induced by the mobilization of rotational and translational molecular degrees of freedom during ultrafast heating. We further show that our findings are fully consistent with recent electron diffraction measurements showing structural arrest of supercooled water close to our predicted glass-transition temperature. These results provide an alternative interpretation of the experimental observations and highlight the importance of nonequilibrium glassy dynamics in the interpretation of the behavior of supercooled water on ultra-short time scales.

Active microrheology is an effective tool to determine the rheological properties of viscous, viscoelastic, or elastic materials on microscopic length scales. The positional response of an embedded probe particle to an externally applied oscillating driving force allows to indirectly characterize the properties of the surrounding media. We aim to explore the linear and nonlinear response of probe particles in a microrheological setup of planar geometry. For this purpose, we extend the computational method of fluid particle dynamics from viscous fluid-like to viscoelastic and elastic media, including nonlinear regimes. We consider a system confined by solid walls. In this case, we validate the approach by quantifying the linear response in terms of a Jeffreys model. Increasing the amplitude of the driving force, we observe distinct nonlinear effects. They include distorted stress-strain curves and a gradual net drift of probe particles initially positioned close to a wall. This drift vanishes in the viscous fluid-like and elastic solid-like limits, but is manifest for intermediate viscoelastic systems. We further address a setup of two probe particles in the absence of walls. They experience reciprocal pairwise oscillatory forcing. Here, nonlinearities in viscoelastic systems induce a net drift gradually moving the particles further apart from each other. Comparing with real setups, our implementation of the driving force is in line with experimental setups of optical tweezers or active magnetic microrheology.

The Nernst effect, which converts a heat current into a charge current in the orthogonal direction, is generally classified into the ordinary effect due to an external magnetic field applied to conductors and the anomalous effect due to static magnetization in magnetic materials. Thus, one expects that the anomalous Nernst effect disappears at the Curie temperature following the magnetization. Here, we observe the Nernst effect which rather manifests around the Curie temperature. In a series of ferromagnetic (Mn,Cr)Sb samples, the Nernst coefficients exhibit finite values even across the Curie temperatures, whereas the anomalous Hall resistivity disappears, suggesting the breakdown of the Mott relation. These surviving Nernst and vanishing Hall effects result in a peak behavior of the transverse thermoelectric conductivity around the Curie temperature, which we theoretically reproduce by introducing longitudinal fluctuations of spins without accounting for an exotic spin texture, a non-trivial electronic structure, or magnon- and phonon-drag effects. The Nernst effect driven by the longitudinal spin fluctuations reveals that not only the static magnetic order but also the spin fluctuations can be a driving force of the Nernst effect in magnetic materials, opening another way for boosting transverse thermoelectric conversion.

Conventional wisdom in deep learning holds that overparameterization—having more parameters $p$ than training samples $n$—is benign: larger models generalize better and, even without regularization, interpolating models generalize well, the test error following a double-descent curve. One might expect the same benign overfitting for diffusion models, whose training reduces to regression, i.e. to minimizing a quadratic score-matching loss. Yet the opposite is observed: overfitting here is catastrophic, driving the model into a memorization regime. We resolve this paradox by combining experiments on U-Nets trained on CelebA with a random-features model for which we derive closed-form learning curves. We show that with a fixed number $m$ of noise realizations per training sample, an interpolation peak does occur, but at $p\sim nm$ rather than at $p\sim n$ as in standard regression. The rise of the test loss, however, sets in much earlier, at $p\sim n$, independently of $m$. This overfitting is malign because, although the implicit regularization of training is fully at work, it drives the model toward the empirical score, which memorizes the training set, rather than toward the true score. A bias-variance decomposition pinpoints the mechanism: the bias of the score estimator starts to grow at $p\sim n$; past the peak the variance decays, as in regression, whereas the bias keeps growing and both saturate at a large value. Since diffusion models are trained with $m\gg1$, the peak is pushed to very large model sizes, and therefore sit on the rising branch that precedes it, where malign overfitting is already in play. Nevertheless, overparameterization remains beneficial when paired with regularization: in the random-features theory and in U-Net experiments, optimally regularized large models—via a ridge penalty or early stopping, respectively—outperform any unregularized models.

Topological spin textures have attracted considerable attention through their emergent electromagnetic properties, including the topological Hall effect and the topological Nernst effect. Among them, magnetic hedgehog lattices (HLs) are three-dimensional topological spin textures that host emergent magnetic monopoles and antimonopoles. HLs have been identified in metallic compounds, such as $\mathrm{MnSi}_{1-x}\mathrm{Ge}_x$ and $\mathrm{SrFeO_3}$. Various theoretical models have been proposed, with either short-range or long-range interactions, which are typically regarded as effective descriptions of insulating and metallic systems, respectively. However, the role of the interaction range in stabilizing the HLs remains only partially understood. To address this gap, we investigate the stability of HLs by systematically varying the range and spatial decay of the exchange interactions, thereby interpolating between interaction profiles commonly associated with localized-spin insulating models and itinerant-electron metallic models. Based on extensive Monte Carlo simulations, we find that HLs are stabilized over a broad interaction range. Intriguingly, among the interaction profiles examined, intermediate-range interactions are particularly favorable for stabilizing HLs. They give rise to two distinct types of $3Q$-HLs that differ in the number of monopole-antimonopole pairs per magnetic unit cell, and their relative stability depends sensitively on the interaction range. Our results provide a unified framework for tuning phase diagrams from microscopic interaction models, serving as a guideline for interpreting experiments in candidate materials.

Simulating the dynamics of dissipative quantum many-body systems governed by local Lindbladians is a fundamental task in quantum computation. While the near-optimal gate count has been achieved for Hamiltonian simulation, comparable results for Lindbladian simulation remain elusive. In this work, we develop quantum algorithms for lattice Lindbladians by exploiting their locality. First, we consider sparsely dissipative systems, in which the dissipation is sparsely located, including boundary-driven systems. We establish a near-optimal quantum algorithm for their dynamics with gate count $O(Nt \, \mathrm{polylog}(Nt/\varepsilon))$, where $N$ is the system size, $t$ is the evolution time, and $\varepsilon$ is the allowable error. We then consider generic lattice Lindbladians with finite-range interactions and dissipation, and develop an algorithm for simulating time-evolved observables with gate count $O((Nt)^{4/3} \, \mathrm{polylog}(Nt/\varepsilon))$ per sample with the sampling complexity $\Theta(\varepsilon^{-2})$. The gate count has the smallest known dependence on the system size among algorithms retaining polylogarithmic dependence on $1/\varepsilon$. Our algorithms are based on two techniques that exploit locality: patching and merging. Patching decomposes dissipative dynamics into dynamics on subsystems with exponentially small error, generalizing a key idea underlying the Haah-Hastings-Kothari-Low algorithm for near-optimal Hamiltonian simulation. Merging absorbs reversed dissipative dynamics into other parts of the evolution, substantially reducing the overhead associated with quasi-probabilistic sampling. These results demonstrate that locality can be fully exploited to achieve fast quantum simulation of dissipative many-body dynamics, opening the way to applications such as predicting nonequilibrium phenomena and preparing desirable quantum states.

Entanglement embezzlement asks how much entanglement can be borrowed from a many-body state by local operations and classical communication while returning that state with a small error. We uncover a conservation-law mechanism that redistributes the dominant probability mass of the Schmidt spectrum in the logarithmic Schmidt-rank coordinate and thereby controls this operational resource. For typical random pure states at fixed U(1) charge, we prove a finite-error conversion law: away from half filling, the charge bias converts $O(\sqrt L)$ charge fluctuations into $O(\sqrt L)$ borrowable entanglement, whereas particle–hole symmetry at half filling removes this contribution entirely. We then show how the resource develops dynamically in charge-conserving random circuits. Combining hydrodynamic analysis with large-scale replica tensor-network calculations, we find that diffusion broadens the operationally relevant distribution in the logarithmic Schmidt-rank coordinate on the scale $t^{1/4}$ and increases the amount of entanglement that can be borrowed. Charge transport therefore continues to reorganize the entanglement spectrum and activate embezzlement after the leading volume-law entropy has saturated.

We study the thermal equilibrium of a classical system of identical point charges moving between two walls with parallel surfaces at a distance $d$, charged symmetrically with position-dependent surface charge densities. Specifically, we study the effect of surface charge modulation on the effective force (pressure) between the walls in the Poisson-Boltzmann limit. Based on Monte Carlo simulations at high temperatures, it is predicted that surface charge modulation reduces the pressure between the walls compared to uniformly charged wall surfaces with the same average surface charge density. We restrict ourselves to surface charge modulations in only one direction and, using a general solution of the two-dimensional Liouville equation, we construct exact solutions for the electrostatic potential. The surface charge density on the walls is generated inversely from this potential, which means that our exact results apply to a limited set of models with a specific variation of the surface charge density with distance $d$. Explicit analytical results show that surface charge modulation can both increase (small distances $d$) and decrease (large $d$) the pressure between the walls.

The phenomenon of bias-driven circular charge and spin currents in a ring nanojunction is investigated in the presence of electron-electron (e-e) and electron-phonon (e-ph) interactions within a tight-binding framework based on the non-equilibrium Green's function formalism. The Lang-Firsov transformation is employed to map the interacting system onto an effective electronic model, which is subsequently treated within the Hartree-Fock mean-field scheme. By exploring the interplay among e-e interaction, e-ph coupling, and electrode-ring interface sensitivity, several intriguing features emerge in both circular charge and spin currents that, to the best of our knowledge, have not been reported previously. In addition to bias-driven circular currents, charge and spin-dependent junction currents through the nanojunction are also analyzed. Selective spin transport is achieved, leading to a high degree of spin polarization. All four current components, two associated with circular currents and two with transport currents, are systematically inspected over a wide range of parameter regimes to assess the sensitivity of the results to the relevant system parameters. Our findings provide useful insights into charge and spin transport phenomena in interacting nanojunctions with single- and multi-loop geometries.

Symmetry breaking at crystalline boundaries can enable electronic responses that are forbidden in the bulk. Here, we investigate the Berry curvature dipole (BCD) at GaAs(110) surfaces using first-principles calculations. While the BCD vanishes in bulk GaAs due to symmetry constraints, the reduced surface symmetry permits a finite dipole. We show that surface relaxation promotes hybridization between bulk and surface states, which strongly influences the surface BCD. Specifically, in unrelaxed slabs, the BCD arises predominantly from interband coupling among exponentially localized surface states lying within the projected bulk band gap. Surface relaxation shifts these states into the energy range of the bulk continuum, transforming them into surface resonances and promoting hybridization with bulk-like states. This hybridization generates pronounced Berry-curvature hot spots, resulting in an overall enhancement of the BCD. Our results highlight the importance of surface relaxation for accurately describing surface–bulk hybridization, which in turn plays a key role in Berry-curvature-driven responses at crystalline boundaries.

We construct the first type of non-Euclidean non-autoregressive neural quantum state (NQS) in the form of the hyperbolic Restricted Boltzmann Machine (HRBM), which is studied in the variational Monte-Carlo (VMC) setting of the Quantum Sherrington-Kirkpatrick (QSK) model whose ground state exhibits volume-law entanglement. Across a 512-fold increase in the Hilbert space dimension corresponding to a system size increase from $N=14$ to $N=24$, HRBM NQS robustly outperforms its Euclidean version, the RBM NQS, in terms of better ground state energy optimization as well as lower Renyi-2 $S_2$ and von Neumann $S_{vN}$ absolute entanglement entropy reconstruction errors. More importantly, for all tested QSK system sizes, HRBM NQS demonstrates a superior expressivity in faithfully reproducing the entire entanglement spectrum of the QSK model from the top eigenvalues down to the tail end across 15 orders of magnitude, while RBM NQS consistently overestimates the sub-dominant modes. This work furnishes a proof-of-concept demonstrating that hyperbolic non-autoregressive NQS ansatze, thanks to the exponential volume of the hyperbolic geometry underlying their constructions, might be more natural at representing volume-law quantum systems than conventional Euclidean NQS. Furthermore, an interesting byproduct of this work is the polynomial scaling result of RBM-type NQS ansatze in the QSK volume-law system as the Hilbert space dimension increases exponentially.

The debate over pairwise and higher-order models is often framed as a contest of expressive power. This framing is misleading. A graph with arbitrary multivariate node functions can reproduce the node-level dynamics of many hypergraph models, but the hypergraph's grouping information does not thereby disappear: it may simply move from the structure to the rule. We separate four notions frequently conflated here: structural representation, functional representability, statistical identifiability, and mechanistic adequacy. We then prove that interaction order, the number of variables that must act jointly in some term of any additive decomposition of a node's update, is the same in every exact representation: no change of structural language can lower it. As a description-length problem, at the unrestricted algorithmic level a fixed compiler redistributes information between structure and rule at constant overhead, so expressiveness alone cannot privilege either language. Preferences arise only relative to explicit model classes, code families, and data. This yields an operational minimum-description-length criterion combining structural cost, conditional rule cost, and imperfect fit. Within it, for $M$ disjoint groups of size $k$, the clique projection's edge list is asymptotically $k-1$ times longer than the hyperedge list it replaces: the projection encodes the same grouping at higher cost. Examples from diffusion, Boolean dynamics, ecology, and ambiguous projections give graph-preferred, hypergraph-preferred, and unresolved cases; for bipartite and multilayer lifts, cost, fit, and identifiability tie, and the choice turns on which entities are posited as primitive. The position is symmetric: higher-order structure should not be inferred from phenomenology alone, nor does a graph's ability to emulate a system make it the most parsimonious or adequate description.

Conventional ferroelectrics exhibit well-defined polarization states linked through electric switching. In fluorite-structured ferroelectrics like hafnia, though, switching and oxygen diffusion seem to coexist, enriching the nature of ferroelectricity. Here we address the intrinsic, room-temperature switching and diffusion kinetics of hafnia ferroelectrics using machine-learning molecular dynamics. We identify two distinct switching mechanisms that are both active at realistic time scales. Critically, our simulations reveal that, because the lattice does not dissipate fast enough the heat originating from localized switching events, these two processes concatenate in an avalanche-like manner leading to oxygen conduction. Our results thus show that intrinsic switching leads to breakdown in hafnia ferroelectrics. They also suggest how this outcome might be avoided through suitably designed field pulses.

Understanding how electrolyte composition influences charge storage in Prussian blue analogues (PBAs) requires clarifying the coupled effects of structural disorder and ion transport. Here, we investigate Fe-, Co-, and Ni-based hexacyanoferrate thin films electrodeposited from KCl, NaCl, NH4Cl, and LiCl electrolytes under identical electrochemical conditions. Although all films retain the cubic PBA structure, electrolyte identity produces pronounced differences in lattice parameters, defect concentration, and local coordination environments. Na+ promotes lattice expansion accompanied by increased vacancy formation, microstrain, and structural heterogeneity, whereas K+ and NH4+ yield more structurally coherent frameworks. Raman spectroscopy shows that increasing structural disorder broadens the distribution of local coordination environments and correlates with increasingly dispersed electrochemical behavior. Electrochemical impedance spectroscopy further reveals coupled ion-electron transport, with impedance increasing by more than one order of magnitude from FeHCF to NiHCF. Notably, the CPE exponent decreases systematically with Raman band broadening, establishing a direct correlation between structural disorder and transport dispersion. These results demonstrate that electrolyte identity controls not only lattice dimensions but also the organization and connectivity of defect networks, highlighting structural coherence and accessible transport pathways as key factors governing electrochemical performance in PBA thin films.

When ferromagnets are brought into contact with a superconductor, superconducting proximity effects give rise to a variety of interesting phenomena, including oscillatory singlet Cooper-pair amplitudes and long-ranged odd-frequency triplet correlations induced by the exchange interactions in the ferromagnets. From an electrodynamic perspective, however, it is equally important to understand when and how spontaneous currents can emerge. To investigate the interplay between electromagnetic and conventional superconducting proximity effects, we study clean ferromagnet/ferromagnet/superconductor spin-valve heterostructures in which the relative angle between the two ferromagnetic layers can be tuned. Our approach is based on a self-consistent numerical solution of the coupled Bogoliubov-de Gennes and Maxwell equations, providing a microscopic description capable of resolving physics on atomic length scales. Several notable features emerge. In both the weak- and strong-exchange-field regimes, the central ferromagnetic layer plays a dominant role in generating sizable spontaneous currents when its exchange-field strength is varied. We further find that the resulting electromagnetic response extends across the entire superconducting layer, in sharp contrast to the short-ranged inverse proximity effect. In noncollinear configurations, the electromagnetic proximity effect also reconfigures the local magnetic-field orientation and reduces the angular mismatch between the fields in the two ferromagnetic layers. The long-ranged odd-frequency triplet amplitudes are consequently modified by the orbital response, which alters the underlying quasiparticle states by changing their momentum-space structure. Finally, our framework can be naturally generalized to other superconducting spintronic systems, possibly including Josephson junctions and altermagnet/superconductor heterostructures.

Recent first-principles searches predict that a titanium vacancy in rocksalt TiC is not an empty octahedral site but can reconstruct through the formation of carbon Frenkel pairs and short C-C bonds. We determine the C 1s signatures of these local structures and assess whether they remain specific in the presence of disordered carbon. Site-resolved binding energies were calculated with a self-consistent core-hole method for an unreconstructed vacancy and four representative C-C-bonded reconstructions. The unreconstructed vacancy shifts its nearest-neighbour C 1s level by only -0.36 eV. In contrast, the largest positive shift in each reconstructed model ranges from 2.34 eV to 2.80 eV. The large shifts occur at carbon atoms participating in the reconstructed C-C network and correlate with markedly less negative Mulliken charges, providing candidate local fingerprints of vacancy reconstruction. However, an exploratory TiC/amorphous-C model shows that disordered carbon environments can extend into the same high-binding-energy range. Overall, the calculated shifts provide reference energies for experimental searches for reconstructed Ti vacancies in TiC.

We study the equilibrium statistical mechanics of one-dimensional classical Machian fractons: particles whose dynamics conserves a global dipole moment and whose Hamiltonian couples momentum differences through a position-dependent pair-inertia kernel. Compactly supported interaction kernels have a divergence in the Gibbs partition function, and have been shown to break ergodicity and symmetry by forming non-equilibrium steady states with particle clusters, evading the Hohenberg-Mermin-Wagner-Coleman theorem. In this paper, we consider kernels with non-compact support and study their ergodic properties. For exponentially decaying kernels, graph-Laplacian and matrix-tree bounds provide an extensive free energy suggesting that a putative statistical mechanical description is valid. Similarly, uniform non-local kernels have a super-extensive free energy and require a Kac rescaling. A generalized Hohenberg–Mermin–Wagner–Coleman argument, supported by finite-size scaling, implies symmetry-breaking density order parameter vanishes at all wave vectors melting the long-range translation-breaking density order of compact kernels. To study the resulting equilibrium ensemble, we construct a nonreversible event-chain Monte Carlo (ECMC) algorithm that samples the coupled position-momentum phase space while preserving the dipole moment and total momentum. The ECMC sampling is shown to quantitatively match long time-averaged quantites in Hamiltonian dynamics. The equilibrium liquid exhibits preferred short-range clustering and strongly non-Gaussian single-particle momentum tails associated with the correlated nature of positions and momenta. This paper provides a detailed investigation into the equilibrium liquid properties of the non-compact regime, whilst the companion paper investigates the mechanisms that relax the liquid.

Understanding how growth conditions govern structural order and ion transport in Prussian blue analogues (PBAs) thin films is essential for optimizing their electrochemical performance. Here, cobalt and nickel hexacyanoferrate (CoHCF and NiHCF) thin films were electrodeposited potentiostatically at temperatures between 20 and 60 degrees C. A combination of cyclic voltammetry, scanning electron microscopy, X-ray diffraction, and Raman spectroscopy was employed to elucidate the interplay between composition, crystallinity, and chemical structure. Under identical conditions, CoHCF exhibits a maximum current density approximately 2.2 times higher than NiHCF, indicating significantly faster electrochemical kinetics. X-ray diffraction reveals temperature-dependent lattice expansion without phase transitions, with a maximum near 40 degrees C, associated with structural relaxation and compositional variations. Raman spectroscopy further reveals temperature-dependent local structural evolution, where cyanide band narrowing at intermediate temperatures indicates improved short-range order, while band broadening at higher temperatures reflects increased defect density. These findings demonstrate that temperature-controlled defect redistribution governs both short- and long-range structural order in PBA thin films, directly influencing ion transport and electrochemical response. This work provides new insights into structure-property relationships in PBAs and establishes deposition temperature as a key parameter for tuning electrochemical functionality in hexacyanoferrate-based electrodes.

We consider the simultaneous estimation of multiple parameters in Hamiltonians of the form $H_{\vec{\theta}}= H_{\vec{\theta}}^P + H^C$, where $H_{\vec{\theta}}^P$ encodes the unknown parameters and $H^C$ is a control term. We consider three basic paradigmatic frameworks for metrology: (i) dynamical, where the parameters are encoded unitarily, (ii) thermal, and (iii) ground state. We generalize previous bounds for single-parameter estimation to simultaneous multiparameter estimation for all three metrological settings and establish the conditions required for their saturability. We then focus on vector magnetometry, deriving tight bounds for the simultaneous estimation of magnetic-field components and identifying optimal many-body control Hamiltonians $H^C$ that saturate them. These results establish the fundamental limits of vector magnetometry for unitary dynamics, thermal equilibrium states, and ground states.

The probability that several independent random walks avoid one another decays algebraically with the number of steps $N\to\infty$ in two and three dimensions, $P_N\sim N^{-\xi/2}$, where the intersection exponent $\xi$ depends on the number of random walks. More generally, one may consider several groups of independent random walks, with intersections allowed within each group and forbidden between different groups. We first study intersection exponents in two dimensions, where our results agree with the known exact formulas and test the numerical approach. In three dimensions, where no general exact expression is known, we determine $\xi$ for a range of cases. For two groups containing $k$ and $m$ random walks, respectively, we obtain the exponents $\xi(k,m)$ for $k=1,2$ and $m=1,2,\ldots,7$. We further extend $m$ to a continuous parameter $\lambda$, allowing us to obtain numerical values for $\xi(k,\lambda)$ for $k=1,2,3$ and $0.25\leq\lambda\leq3$. Away from the smallest moment orders, these functions increase with $\lambda$ with decreasing slopes, as expected from the strict concavity of Brownian intersection exponents. We also investigate three-group configurations for several representative cases. The resulting integer and continuous exponents supply numerical values for analytical studies of non-intersecting random paths.

The antiferromagnetic metal CuMnAs has become a workhorse of antiferromagnetic spintronics. A broad range of phenomena has been demonstrated in this material, most notably current-induced switching and heat-driven quench switching, both first realized in CuMnAs. CuMnAs belongs to a much larger family of intermetallic compounds sharing the tetragonal Cu$_2$Sb-type structure, yet the magnetic properties of most of its members, and their potential for spintronics, have not been studied. Here we report a systematic density functional theory (DFT) study of the magnetic and electronic structure of more than 50 compounds of this family, intended to guide future theoretical and experimental work toward spintronic applications. We find a wide range of magnetic ground states, among them over 20 new antiferromagnetic candidates.