Papers

In perovskite solar cells (PSCs), voltage step-induced transient ion current (TIC) measurements, commonly referred to as bias-assisted charge extraction (BACE), are frequently used to quantify ion density. Drift-diffusion simulations predict that the ion density computed from TIC saturates once mobile ions screen the electric field in the perovskite. However, experimental studies often report ion densities orders of magnitude above this limit, whose physical origin remains incompletely explained in terms of transport layer (TL) properties. In this work, the capacitance of the TLs is identified to be the fundamental bottleneck: the maximum quantifiable ion density is limited to the charge that can accumulate at the perovskite/TL interfaces, so that TIC most often depends more strongly on TL properties than on the ionic properties of the perovskite. Experiments with systematically varied C$_{\rm 60}$ electron-TL thickness (p-i-n) and Spiro-OMeTAD hole-TL doping (n-i-p) confirm this dependence across architectures. To overcome this limitation, the importance of a correction based on the average ionic displacement is discussed, and it is shown how extrapolating the TL-thickness trend towards the TL-free situation yields the actual ionic conductivity of the absorber, alongside density and mobility, depending on the assumed ionic model. Simulations are also examined in which ion penetration into the TLs or initial accumulation under forward bias raise the capacitive limit and extend TIC sensitivity to higher ion densities. The slow release of trapped carriers is also discussed as a potential source of current which can inflate the TIC signal. Overall, the presented analysis provides important practical considerations for interpreting TIC and quantifying ionic properties in PSCs.

Nonlinear optical responses and quantum geometry are central to modern condensed matter physics and are deeply intertwined. We introduce WannierNLQG, an extensible Julia framework for computing nonlinear optical responses and quantum geometric quantities from Wannier tight-binding models. Its gauge-consistent, degeneracy-aware architecture accommodates perturbative responses at arbitrary order and additional quantum geometric quantities. The current release evaluates ordinary, spin, and photon-drag injection and shift currents, as Brillouin-zone integrals and on k slices. For shift current, a unified interface exposes four complementary formulations: the conventional method, projector trace, generalized Wilson loop, and a finite-momentum geometric loop, with the last three explicitly accommodating degenerate subspaces. The framework also provides k-resolved Berry curvature, quantum metric, their multipoles, and additional quantum geometric and mixed momentum-spin quantities. Experimental symmetry workflows additionally support symmetry-adapted Wannier construction, symmetrization of tight-binding models and real-space operators, and reduction of spatially uniform response integrals to irreducible k-point orbits and invariant tensor components. The documentation covers formula conventions, the TaskConfig interface, and auditable output formats, with GeS and bilayer MoS2 case studies illustrating method comparison, degeneracy handling, quantum geometric analysis, and photon-drag responses. Together, these capabilities enable first-principles calculations of nonlinear response and quantum geometry in realistic multiband materials, connecting quantum geometry with quantitative materials modeling for optoelectronic, spintronic, and photovoltaic applications.

A compressible, non-isothermal dilute polymer solution is formulated as a Class-II binary mixture with separate solvent and polymer mass and momentum balances and with total energy and entropy balances for the complete mixture. The entropy exploitation is sharpened by a balance-anchoring axiom introduced here: in a fixed balance representative, each selected binary dissipative product contains a constitutive factor from an unclosed balance flux or source. Polymer configuration is resolved first by a connector distribution and then by its conformation tensor. Population kinematics fix polymer transport and, by moments, yield a two-velocity upper-convected rate; objectivity verifies covariance rather than selecting it. Within the total entropy balance, configurational transport and deformation powers cancel their chemical-potential and elastic partial-stress counterparts when deformation and stress-decomposition weights match. A Gordon–Schowalter test independently requires the affine upper-convected choice for the stated dumbbell free energy and Kramers stress unless an additional reversible channel is supplied. Coordinated stress–interaction changes shift the local entropy flux/production pair by a divergence, exposing representation dependence of local mechanism-wise production. An entropy-invariant Class-II-to-Class-I reduction selects a descendant entropy flux preserving the parent production and yields thermo-chemical, configurational-stress and partial-viscous-stress diffusion terms. The omitted quadratic relative-inertia flux is paired with relative kinetic-energy storage and transport and is a reversible truncation, not missing entropy production. The resulting compressible non-isothermal Hookean stress and temperature equations reduce to Oldroyd-B/UCM only after one-velocity, incompressible and isothermal limits.

We study factorizations of the single-particle spectrum of non-interacting quantum systems and their consequences for many-particle statistics. Representing the single-particle partition function by a spectral alphabet, tensor factorizations become multiplicative factorizations of the alphabet, which can be lifted to canonical Bose and Fermi partition functions using standard symmetric-function and $\lambda$-ring identities. We show how product spectra arise from different factorizations, how antisymmetrization can be assigned across an odd number of factors, and which tensor factorizations are compatible with a fixed spectrum. The paper therefore studies spectral factorization and provides a consolidated combinatorial framework for canonical Bose and Fermi partition functions.

A microscopic understanding of topological superconductivity (TSC) in real materials requires a materials-informed approach that integrates first-principles electronic structure, superconductivity, and topology within a unified computational framework. Here, we develop such an approach by combining density functional theory, Wannier-based low-energy Hamiltonians, Bogoliubov-de Gennes theory, and Matsubara Green's-function-based Chern number calculations performed directly on realistic multiorbital superconducting Hamiltonians. We apply this framework to bulk-like and monolayer FeTeSe and to FeSe/GaAs heterostructures, enabling a unified investigation of both intrinsic and proximity-induced topological superconductivity. We identify key electronic-structure ingredients that promote robust TSC, including Rashba-active states near the Fermi level, strong induced superconducting pairing, and substantial orbital hybridization between spin-orbit-active and superconducting sectors. Guided by these design principles, we predict multiple topological superconducting phases in Fe-based materials and demonstrate that FeSe/GaAs heterostructures are particularly promising, exhibiting topological transitions at experimentally accessible chemical potentials and low Zeeman fields. Complementing the theoretical predictions, we demonstrate the growth and structural characterization of FeSe/GaAs heterostructures, establishing the experimental feasibility of the proposed materials platform. Our results provide a quantitative route for engineering and screening candidate Majorana materials and heterostructures directly from realistic electronic structures and establish a foundation for future materials-by-design approaches to topological superconductivity.

Quantifying the degree of order/disorder in many-particle systems remains an outstanding problem in physics, materials science, and mathematics. To this end, we consider the translational order metric $\tau_T$, defined as the squared $L^2$ norm of the total correlation function $h(\mathbf{r})$, and introduce its bond-orientational analogue $\tau_O$, defined from the weighted total correlation function $h_{\mathbf{f}}(\mathbf{r})$ using local orientational weights (S. Torquato et al., Phys. Rev. X 16, 011042 (2026)). The pair $(\tau_T,\tau_O)$ places both forms of order on a common two-point statistical footing. We compute both metrics for two nonhyperuniform sphere-packing models in 2D and 3D as functions of packing fraction $\phi$: 1) equilibrium hard particles and 2) nonequilibrium random sequential addition (RSA) packings. For the nonhyperuniform systems, bond-orientational order remains subdominant along the equilibrium-fluid and RSA configurations; however, its magnitude relative to the translational metric increases near the upper end of the equilibrium-fluid branches, much more strongly in 2D than in 3D, and the two metrics become comparable along the sampled crystal branches. At common packing fractions, equilibrium fluids and RSA packings trace distinct $(\tau_T,\tau_O)$ trajectories, revealing preparation-dependent differences in structural order. As a representative hyperuniform family, we study 2D disordered stealthy hyperuniform (SHU) ground states for $0<\chi<1/2$, where $\chi$ is the stealthiness parameter. Within the disordered SHU phase, bond-orientational order remains subdominant, but its magnitude relative to the translational metric increases toward the disorder-to-order threshold. In all three models, $\tau_T$ and $\tau_O$ are positively correlated beyond the Poisson-reference regime: $\tau_O$ increases monotonically with $\tau_T$ across the sampled state points.

We numerically study the bulk correlation functions in the two-dimensional Abelian sandpile model, aiming both to compare with the predictions of logarithmic conformal field theory and to extend the analysis to lattices where analytical methods are difficult to apply. Wilson's algorithm efficiently generates large-scale uniform spanning trees in parallel, which can be mapped to independent recurrent configurations via the Majumdar–Dhar burning bijection, eliminating sample autocorrelations and yielding fast convergence. On the square (single-sublattice) and honeycomb (two-sublattice) lattices our results agree well with the analytical predictions. For the kagome lattice we provide the first systematic numerical study of the bulk correlation functions, and cross-check the bulk height-1 probability against a closed-form analytical expression that we also derive here via the lattice Green function.

We report on time- and spectrally resolved pump-probe spectroscopy measurement on the charge-density wave (CDW) state of TiSe$_2$ at helium temperatures. The non-oscillatory component of the spectrally resolved signals exhibit a sign reversal near 1.5 eV, which was interpreted as a redshift of the optical transition energy originated from the interband transition between the Se $p$-based valence band to the Ti $d$-derived conduction band. Furthermore, the oscillatory components exhibit distinct spectral dependences between the optical phonons and the CDW-derived modes, suggesting the mode-selective coupling.

The critical phase of a non-Hermitian quasicrystal can support stronger transport than its surrounding delocalized phase. We demonstrate this anomalous behavior in the one-dimensional nonreciprocal Aubry-André-Harper model through a combined study of dynamical quantum phase transitions (DQPTs) and wavepacket diffusion. Using a parity-sorted energy-spectrum classification that directly encodes the generalized $\mathcal{PT}$ symmetry, we find that DQPTs in this system are energy-resolved, in contrast to the energy-independent DQPTs of Hermitian quasicrystals. The energy-resolved features are most pronounced when the initial and final Hamiltonians belong to different phases (localized or extended), and they are tied to the even-odd index structure of the spectrum, which we exploit to organize the quench-dynamical landscape. For wavepacket dynamics after a single-site quench, the diffusion exponent $\beta$, extracted from the long-time power-law scaling of the root-mean-square displacement $\sigma(\tau)$, partitions the phase diagram into four distinct regimes. In the Hermitian limit the extended phase is ballistic ($\beta=1$), the critical phase is normally diffusive ($\beta=0.5$), and the localized phase yields $\beta\to 0$. Nonreciprocity reverses this hierarchy: the extended phase becomes normally diffusive, while the critical phase turns ballistic. We trace the anomalous $\beta=1$ at criticality to the self-similar multifractal structure of the critical eigenstates, whose nodal positions are organized by the golden ratio. A finite-size scaling ansatz built on the wave-front propagation yields $\sigma(\tau)\propto\tau$. The parity-resolved DQPTs and the $\beta$-phase diagram establish two complementary dynamical diagnostics of nonreciprocal quasicrystals, in which nonreciprocity promotes transport at the critical point and suppresses it in the delocalized phase.

Identifying ground-state and low-energy atomic configurations is a central problem in alloy design. The cluster-expansion (CE) method represents configurational energetics on a fixed lattice as an effective Hamiltonian; for binary alloys, higher-order CE models become polynomial Ising Hamiltonians. Using Au-Cu as a model binary alloy, we formulate cubic and quartic cluster-expansion Hamiltonians as penalty-augmented polynomial unconstrained binary optimization (PUBO) problems under fixed-composition constraints. We optimize these PUBO problems using SQBM+, a simulated-bifurcation-based solver that can treat higher-order polynomial binary objectives directly. This direct PUBO treatment avoids the need to construct an explicit quadratic reformulation with auxiliary variables. Composition constraints are imposed through quadratic penalty terms, whose weights are estimated from derivative coefficients of the continuous relaxation of the CE objective. Benchmark calculations for systems up to 2048 atoms show that SQBM+ robustly obtains low-energy feasible configurations for cubic CE models and remains effective for many quartic instances. Formation-energy convex hulls constructed from the optimized configurations recover the CuAu and Cu3Au ordering trends and reveal finite-size effects at off-stoichiometric compositions. These results demonstrate simulated bifurcation as a practical route to constrained higher-order CE optimization for alloy configuration search.

Gallium selenide (GaSe) is a layered III-VI semiconductor whose bulk crystal serves as the essential energetic reference for modeling the isolated monolayer. We present a systematic density-functional-theory (DFT) study of the centrosymmetric $\beta$(2H)-GaSe polymorph, employing the GGA-PBE functional supplemented with a semiempirical Grimme DFT-D2 dispersion correction to accurately capture the weak van der Waals interlayer coupling. The optimized in-plane lattice parameter agrees well with experimental values (within about 1.3%), while the out-of-plane parameter c is overestimated by the DFT-D2 correction relative to experiment, a known limitation of this dispersion scheme when the pairwise $C_6$ coefficients are not specifically fitted for the compound class under study. Electronic structure calculations confirm a non-magnetic, direct-gap profile (1.12 eV). Although this magnitude reflects the well-known underestimation of semilocal functionals, the qualitative band topology accurately captures the intrinsic two-dimensional carrier confinement within the bulk material. Consequently, this optimized three-dimensional framework provides a consistent energetic baseline for quantifying exfoliation processes, offering a reliable starting point for future theoretical explorations of the two-dimensional limit, such as surface functionalization for potential spintronic applications.

Agitated filter dryers (AFDs) are a crucial unit operation in small molecule manufacturing that enables simultaneous filtration, washing, and drying of active pharmaceutical ingredients. One of the key challenges in AFDs is associated with undesired agglomeration, where the presence of hard agglomerates results in off-spec products, equipment damage, and additional downstream processing. This article presents a novel mechanistic model that describes the formation of soft and hard agglomerates during agitated filter drying. By integrating population balance and multiphysics modeling, the model can accurately predict the evolution of the product temperature, moisture content, and particle size distribution, and hence quantify the extend and impact of undesired agglomeration across various operating conditions. Our proposed model-based framework enables the rational design and operation of AFDs for improving the product quality and process reliability.

We compare three particle-scale measures of rearrangement in highly polydisperse materials under driven flow: (a) nonaffine motion defined relative to the time-averaged mean flow, (b) changes in nearest-neighbor connectivity, and (c) $D^2_{\min}$, which measures nonaffine motion relative to an affine deformation fitted locally in space and time [Falk and Langer, Phys. Rev. E 57, 7192 (1998)]. We apply these measures to previously published two-dimensional simulations [Jiang, Sussman, and Weeks, Phys. Rev. E 108, 054605 (2023)] and granular-flow experiments [Illing and Weeks, Phys. Rev. E 111, 045422 (2025)] with polydispersities up to $\delta\approx0.50$. Changes in connectivity and $D^2_{\min}$ both require a definition of neighboring particles, making the choice of neighborhood nontrivial in highly polydisperse systems. For detecting changes in connectivity, we recommend radical Delaunay triangulation, which provides a size-aware topological definition of neighbors. For calculating $D^2_{\min}$, we recommend a size-aware pairwise cutoff distance method. We further show that changing the neighborhood definition can reverse the apparent dependence of $D^2_{\min}$ on particle size in experimental data. Thus, trends in $D^2_{\min}$ cannot be interpreted independently of the neighborhood used to calculate it. Overall, the three measures presented provide complementary information about rearrangements in highly polydisperse systems.

We present a comprehensive description of the network of Italian Business-to-Business commercial relationships, based on the universe of electronic invoices collected by the Italian Tax Office. The firm-to-firm detail of the data is exploited to describe the distribution of the numbers of buyers (customers) and sellers (suppliers) per firm, the centrality of each firm in the production network, and the average distance between firms. We characterize for the first time the firm-to-firm network describing the Italian production system, and present its geographic and sectoral breakdown. The analysis reveals heavy tailed distributions for the numbers of buyers and sellers per firm, implying a scale-free structure of the network. The distributions of centrality values display heavy tails as well, indicating a strong concentration of importance in a relatively small number of firms. We estimate the tail exponents for all these distributions, finding in all cases lower exponents for downstream (buyer-side) distributions than for the upstream (seller-side) ones.

We formulate a quasiclassical theory of the Edelstein effect in superconductors that incorporates both intraband and interband contributions. To describe the interband contribution, which is absent from the conventional leading-order quasiclassical formulation, we derive augmented Eilenberger equations in the presence of antisymmetric spin-orbit coupling. The intraband contribution is evaluated using multiband Eilenberger equations. We apply these formulations to supercurrent-induced surface spin magnetization in $s$-wave Rashba superconductors and investigate its dependence on temperature, distance from the surface, spin-orbit coupling strength, and supercurrent. The intraband contribution originates from a supercurrent-induced asymmetry of quasiparticles with opposite momenta and spin polarizations, whereas the interband contribution arises from the anomalous-velocity term generated by the momentum derivative of the Rashba spin-orbit potential. In the helicity basis, this anomalous-velocity term is expressed in terms of the Berry connection associated with the momentum dependence of the Rashba eigenstates. The intraband contribution increases linearly with the spin-orbit coupling strength, whereas the interband contribution exhibits a nonmonotonic dependence and is maximized when the Rashba spin splitting is comparable to the superconducting gap. Moreover, within the clean $s$-wave Rashba model considered here, we find that the nonlinear dependence of magnetization on the supercurrent arises solely from the interband contribution. Thus, although the intraband contribution dominates the linear Edelstein effect, the nonlinear Edelstein effect can serve as a useful probe of the interband contribution originating from quantum geometry.

We investigate the validity of the superposition principle and impedance additivity in AC circuits containing a memristive device connected in series with a resistor, capacitor, or inductor. While the series association of impedances is a cornerstone of linear circuit theory, its applicability to memory-bearing nonlinear systems remains largely unexplored. Using a state-dependent memristive model, we numerically analyze the stationary current response under sinusoidal excitation and characterize the resulting harmonic spectra, Bode diagrams, and Nyquist plots. To assess whether the fundamental response can still be interpreted through an equivalent-circuit framework, we introduce the concept of an apparent memristor, whose effective parameters are extracted directly from the composite impedance. We show that, although the fundamental harmonic can be accurately reproduced by an apparent equivalent circuit over selected parameter ranges, the effective parameters differ substantially from those of the isolated memristor, revealing a renormalization induced by the coupling to the passive element. More importantly, we identify parameter regimes in which the apparent-circuit description breaks down altogether, particularly for capacitor and inductor-coupled systems, demonstrating that the composite impedance cannot generally be expressed as the sum of independent impedances. These results establish boundaries for the use of equivalent-circuit models in memory-enabled electronic systems and provide practical guidelines for the interpretation of impedance spectroscopy in nonlinear devices exhibiting memory.

The cluster expansion is a widely used approach for predicting the finite-temperature thermodynamics of alloys from zero-kelvin first-principles calculations, but its conventional formulation becomes intractable for materials with more than three or four chemical species. High-entropy alloys have therefore remained largely out of reach. We present pyeCE, an open-source Python library that implements the embedded cluster expansion (eCE), in which machine learning maps many chemical species onto a smaller set of effective species and thereby limits the growth in the number of cluster functions. pyeCE provides the complete modeling workflow, including the construction of symmetry-adapted descriptors with a learnable per-sublattice chemical embedding, a neural-network energy model, ladder-based training, uncertainty quantification, and finite-temperature simulations through Monte Carlo sampling. Built on the PyTorch and pymatgen libraries, it supports systems with multiple species on multiple sublattices and runs on graphics processing units. We demonstrate the package on two material systems. In the first, a single model spanning the full composition space of a 9-component refractory alloy resolves short-range order and order–disorder behavior. This model enables rapid screening for compositions with strong Cr clustering, a feature linked to the formation of a continuous, corrosion-resistant oxide scale. In the second, a model of hydrogen dissolution in a Mo–Nb–W alloy reproduces the composition dependence of hydrogen uptake and resolves the interstitial environments that hydrogen occupies. The modular design of pyeCE allows it to be extended to problems beyond alloy thermodynamics, including kinetics, defect energetics, and the coupling of chemical order to magnetic and vibrational degrees of freedom.

The organization of multiple knots along a polymer contour can depend sensitively on the topology of the constituent knots. Using coarse-grained Brownian-dynamics simulations, we investigate topology-dependent mixing of two knots in a flexible polymer at controlled normalized wall separation. We compare homogeneous ($3_1\otimes 3_1$) and heterogeneous ($3_1\otimes 5_1$) knot pairs using a contour-based coordinate that distinguishes mixed and demixed configurations. Starting from partially overlapping configurations, the $3_1\otimes 5_1$ pair is observed in mixed configurations more frequently over the simulation time window than the $3_1\otimes 3_1$ pair at intermediate wall separation. We further find that, over the intermediate range of normalized wall separation, mixing of the $3_1\otimes5_1$ pair produces a composite knotted region that occupies substantially less polymer contour than the two constituent knots collectively occupy in demixed configurations. The corresponding reduction in contour occupancy is much smaller for the $3_1\otimes3_1$ pair. This indicates that the heterogeneous constituent topologies can be accommodated within a smaller portion of the polymer contour in the composite configuration. These results establish that constituent topology can strongly influence multiple-knot organization in flexible polymers.

We demonstrate that designing a neural quantum state to be an exact eigenstate of the Hamiltonian's symmetries significantly improves both training speed and final variational energy. For the 2D electron gas, we design TorFormer, a neural network wavefunction which is an exact eigenstate of the total momentum. TorFormer describes both the Fermi liquid and Wigner crystal with no supervision and significantly outperforms Psiformer-based references up to large system sizes. For $r_s = 30.0$ and $40.0$ at $N=91$, we compare TorFormer trained for $8\mathrm{K}$ steps against the previous best NQS, which required $100\mathrm{K}$ training steps. Our improvement to the total energy at $r_s = 40.0$, excluding the trivial Madelung part, is $0.12\%$—enormous compared to the tiny differences separating phases. Relative to Slater-Jastrow-backflow diffusion Monte Carlo, TorFormer's energy decrease is roughly $9.8$ times that of the previous best NQS. Our work demonstrates that neural quantum states can both accurately and efficiently solve large-scale problems.

We investigate a generalized inhomogeneous two-dimensional Ising model on the kagomé lattice with two alternating couplings $J_i$ and $J'_i$, $i=1,2,3$, along the three lattice directions. The local Boltzmann weights are mapped onto a non-symmetric eight-vertex $R$-matrix satisfying the free-fermion condition for arbitrary values of the six couplings. We analyze the Yang–Baxter structure for the cases $J'_i=J_i$ and $J'_i=-J_i$ and derive the corresponding one-dimensional quantum spin chains in the anisotropic limit. The first case yields a transverse-field Ising-type Hamiltonian, while the second leads to a modified chain with a graded permutation structure and shifted partition-function zeros. For a partially anisotropic model, we also obtain the free energy and specific heat.

Microwave Kinetic Inductance Detectors are superconducting resonators capable of single-photon counting with energy resolving capability at visible and near-infrared wavelengths. Their zero read noise, extremely low dark counts, and compatibility with frequency-division multiplexing make them well suited for large imaging arrays. The detector yield of these arrays is often limited by the fabrication-induced frequency scatter leading to resonator collisions. We find that the tight packing of the pixels can add significant frequency scatter as well. We developed a post-fabrication correction method that is suitable for tightly packed arrays with a pixel pitch of $150\ \mathrm{\mu m}$. We demonstrate this method on a 1024-pixel array multiplexed on a single octave of readout bandwidth and show a reduction in frequency scatter from $1.1\times 10^{-2}$ to $3.5\times 10^{-4}$ and an increase in yield from $76\%$ to $94\%$.

Spin Hall nano-oscillators (SHNOs) are nanoscale microwave sources for wireless communication, neuromorphic computing and oscillator-based Ising machines, but conventional devices require a global magnetic bias. Here we replace this bias through crystallographic anisotropy in epitaxial Co/Pt. Growth of hcp Co with its c-axis in the film plane produces an anisotropy field of about 0.36 T and enables field-free auto-oscillations above 10 GHz in nanoconstriction SHNOs. The active current polarity is selected by the remanent magnetization, providing nonvolatile reconfiguration of the oscillation state. Micro-focused Brillouin light scattering confirms that the nonlinear response is confined to the nanoconstriction region. Lithographic control of the angle between the current and anisotropy axes tunes the excitation threshold and drives two spectral branches from separated modes to a dominant single branch, consistent with mutual synchronization. These results establish epitaxial crystallographic anisotropy as a route to reconfigurable field-free spintronic oscillators and oscillator networks.

We study additive observables of a non-confined Feller process characterized by a non- normalizable stationary probability density and return times with infinite mean. The process is equivalent, after a deterministic rescaling, to a squared Bessel process, but we focus here on a question: the spatial structure of its local-time field. We show that the local time admits a fac- torization in the long time limit. Its spatial profile is governed by the non-normalizable stationary density, whereas its temporal fluctuations are controlled by a single Mittag-Leffler random ampli- tude. As a consequence, normalized spatial correlations of the local time converge to a universal constant independent of the two observation levels. Occupation times of finite intervals follow as corollaries and display Darling-Kac fluctuations. In contrast, non-integrable power observables exhibit self-similar squared-Bessel-type limits rather than Mittag-Leffler statistics. This spatial- field perspective provides a unifying framework for infinite ergodic theory under state-dependent multiplicative noise.

Topological materials that simultaneously host robust high-temperature skyrmions and nontrivial electronic band structures have attracted tremendous interest owing to their distinctive advantages for both fundamental research and prospective technological applications. Here, we report the observation of robust skyrmions in the Dirac kagome magnet Fe3Ge, which exhibits a high Curie temperature of ~ 650 K. At room temperature, Fe3Ge shows a large intrinsic anomalous Hall conductivity of ~ 380 {\Omega}-1cm-1, originating from its nontrivial electronic band topology. Systematic magnetization measurements reveal a spin reorientation transition at ~ 375 K, indicating a crossover from easy-plane to easy-axis magnetic anisotropy. Below the spin reorientation temperature, a large topological Hall effect is observed, arising from microscopic noncoplanar spin structures. Lorentz transmission electron microscopy shows that mesoscopic skyrmions are stabilized in the easy-axis magnetic anisotropy regime and persist over an exceptionally wide temperature window of 375-650 K, far exceeding that of most previously reported skyrmion-hosting materials. These results establish Fe3Ge as a promising platform for exploring diverse topological properties, with strong potential for advancing future high-temperature spintronic applications, ranging from next-generation information storage to logic computing devices.

Stabilizing superconductivity beyond its equilibrium regime has been one of the quests of nonequilibrium state engineering. We report the presence of nonthermal pairing gap in BCS superconductors coupled to two thermal baths, where one of the baths can be at a temperature well above the equilibrium $T_c$. Superconducting state is enabled by a highly non-equilibrium steady state stabilized by weak coupling to baths that break detailed balance condition. The steady-state is well described by a generalized Gibbs ensemble parametrized with chemical potentials for associated Bogoliubov quasiparticle. A strong, non-perturbative effect is reported for both $s$-wave and $d$-wave superconductors, and its possible realization in cavity-based experiments is discussed.

We map the transport regimes of a wave packet evolving under the one-dimensional discrete nonlinear Schrödinger (Gross-Pitaevskii) equation with a quasiperiodic Aubry-André potential and a static (Stark) field, as a function of self-interaction $g$, field $F$, and quasiperiodic strength $\lambda$, for two initial states (delta and Gaussian). Instead of one expensive long-time simulation per grid point, we run 3600 short simulations, reduce each to eight dynamical features, and cluster parameter space with a Gaussian mixture model. Clustering finds the boundaries between regimes unsupervised; each cluster's name is then assigned by a hand-calibrated rule, and an ablation shows clustering measurably smooths those boundaries rather than merely relabeling them. The five resulting regimes (ballistic, localized, oscillatory-localized, subdiffusive, and self-trapped) are validated against 100 long runs on a lattice eight times larger than the short sweep, so the validation window is longer than the training window even for the fastest-spreading regime. The short-time clusters predict the long-time asymptotic spreading exponent ($\alpha_\infty = 2.01\pm0.07$ ballistic, $0.28\pm0.16$ subdiffusive, consistent with the weak-chaos prediction $\alpha=1/3$) and, independently, a long-time retention $\Pi_0=0.72\pm0.32$ for the self-trapped cluster, whose exponent alone is not diagnostic. The initial-state contrast is striking: the delta state develops a broad self-trapping wedge (onset at $g=3.907\pm0.017$ at $\lambda=0$) that invades the ballistic and localized regions as $g$ grows, while the Gaussian state shows no self-trapping, instead opening a subdiffusive corridor along the Aubry-André critical line that widens with $g$. The scheme yields a full $15\times15$ phase map in minutes per slice, where direct asymptotic simulation ($t\gtrsim10^6$ per point) is prohibitive.

We studied the 2-Temperature induced phase separation (2-TIPS) in a system of soft repulsive sphe rocylinders (SRS), confined on the surface of a sphere. We used two different anchoring conditions for the SRS on the spherical surface- free and tangential. For the free anchoring, increasing either the packing fraction or the rod aspect ratio suppresses phase separation between the active and passive subsystems, and the system corresponds to a dense-dilute phase co-existence. These phenomena can be explained via the trapping effect and are emergent due to the confinement. For the tangential anchoring, the system phase separates from its initial isotropic state into a locally ordered dense region and a disordered dilute region. Our work provides useful insights into phase separation in binary mixtures under spherical confinement.

Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influences the ability of drug candidates to reach their target sites within the brain. In this study, an explainable machine learning framework was developed to predict BBB permeability using molecular descriptors generated from the MoleculeNet BBBP dataset with the RDKit cheminformatics toolkit. Fifteen physicochemical descriptors extracted from 2,039 compounds were used to train four supervised machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Hyperparameter optimization was performed using GridSearchCV, while model interpretability was investigated using SHapley Additive exPlanations (SHAP). Among the evaluated models, the optimized XGBoost classifier achieved the best predictive performance, with an accuracy of 88.97%, a precision of 88.92%, a recall of 97.76%, an F1-score of 93.13%, and a ROC-AUC of 0.9282. Stratified five-fold cross-validation further demonstrated the robustness of the proposed model, yielding a mean ROC-AUC of 0.8982 +/- 0.0130. Feature importance and SHAP analyses consistently identified TPSA, HBD, and LogP as the most influential molecular descriptors governing BBB permeability prediction. Overall, the proposed framework provides an accurate, interpretable, and computationally efficient approach for BBB permeability prediction and may serve as a valuable tool for the early-stage screening of CNS drug candidates.

The three-dimensional electron gas is a fundamental model in condensed matter physics and quantum chemistry, and the exchange-correlation energy derived from it serves as the starting point of ab initio computations of materials. However, the wave-vector-dependent response, and hence the static local field factor $G(q)$, has remained without accurate ground-state benchmark in the strongly coupled regime for three decades. Using diffusion Monte Carlo, we calculate $G(q)$ and the static density-density response function across the liquid phase and find a pronounced structure in $G(q)$ at intermediate wave vectors, already visible at metallic densities and growing with increasing interaction strength. We identify it as a fingerprint of short-range order by showing that it is required to reproduce the static structure factor. Our parametrization, valid in the entire liquid phase, predicts a low-energy resonant mode inside the particle-hole continuum.

Symmetry plays a central role throughout physics, from Fourier analysis on discrete lattices to the classification of elementary particles in the standard model. In finite quantum systems, symmetries are often discrete, such as reflection and rotational symmetries. Here, we present the SIM-GRAPH method (Symmetric Ising Models - Graph Reduction And Projected Hamiltonians), which uses such symmetries to efficiently calculate ground-state observables of interacting quantum systems. By projecting all interactions onto a symmetric subset of the system, the method effectively reduces the number of sites, thus providing an exponential speedup over standard exact diagonalization. Furthermore, we introduce two extensions that broaden the applicability of the method and allow for a controlled trade-off between computational cost and accuracy.