Papers

We propose a nonlinear orbital magnetoelectric effect, which generates orbital magnetization quadratically in centrosymmetric materials where the linear orbital magnetoelectric effect is strictly forbidden. Using extended semiclassical formulation, we derive a gauge-invariant microscopic theory that separates intrinsic and extrinsic contributions and establishes their distinct dependence on the relaxation time, providing an experimental discriminator. In two-dimensional systems the nonlinear response is far less constrained by out-of-plane rotational symmetries than the linear orbital magnetoelectric effect, substantially enlarging the materials platform. Microscopically, the dominant contributions are governed by a Hermitian-connection structure. Finally, we estimate that the magnitude of the nonlinear orbital magnetoelectric effect lies within the sensitivity of state-of-the-art magneto-optical Kerr measurements.

We study the temperature dependence of the thermodynamic properties of spin-1/2 antiferromagnets on two-dimensional lattices. Our analysis employs the sine-square deformation (SSD), in which a real-space envelope function is applied to the Hamiltonian so that the local energy scale is smoothly reduced to zero at the system boundaries. The quantum eigenstates of the SSD Hamiltonian exhibit bulk-like behavior near the system center, effectively mimicking the thermodynamic limit even in small finite-size calculations. Using these fictitious bulk states, we compute the energy density, specific heat, and magnetic susceptibility as functions of temperature. We find that both the triangular- and kagome-lattice antiferromagnets show either a shoulder or a pronounced double-peak structure in the low-temperature specific heat, whereas the kagome case particularly shows a strong enhancement of magnetic susceptibility down to the lowest temperature range. These direct comparisons, together with the square-lattice and one-dimensional cases, reveal that although both frustrated systems retain a substantial amount of entropy, the low-energy excitations below ~ 0.5J of the kagome lattice are predominantly governed by the magnetic excited states, whereas not much for the triangular lattice.

Dirty superconducting films with magnetic impurities can exhibit nontrivial behavior in a magnetic field that polarizes the impurity spins. As predicted by Kharitonov and Feigelman (KF) [JETP Lett. 82, 421 (2005)], this polarization reduces the exchange scattering rate. Consequently, a parallel magnetic field can enhance the critical temperature $T_c$ when magnetic-field pair breaking is weak, as realized for strong spin-orbit scattering and small film thickness. Recently, Llanos et al. [Nat. Phys. 22, 856 (2026)] observed a pronounced enhancement of $T_c$ consistent with the KF theory. The same experiment also reported an enhancement of the perpendicular upper critical field $H_{c2}^{\perp}$ and a suppression of the London penetration depth $\lambda_L$ by a parallel magnetic field. These quantities were not considered in the original KF theory. To address this gap, we develop a theoretical framework based on Gor'kov's diagrammatic technique for dirty superconductors. We extend the KF theory in two experimentally relevant directions: (i) to arbitrary temperatures $T<T_c$ and several superconducting observables, and (ii) to arbitrary magnetic-field orientations. As a result, we demonstrate theoretically the suppression of $\lambda_L$ and the enhancement of $H_{c2}^{\perp}$ by a parallel magnetic field, in agreement with experiment.

Single-layer molybdenum disulfide ($MoS_2$) possesses significant potential for nanoscale optoelectronics, but achieving high-intensity, long-term-stable photoluminescence (PL) emission remains a challenge. In this work, we demonstrate a remarkably robust, more than 8-fold maximum enhancement in the PL intensity of exfoliated and CVD-grown single-layer $MoS_2$ via a non-destructive ultraviolet (UV) laser treatment method. This substantial increase in radiative efficiency is accompanied by a trion-to-neutral exciton transition in the PL signal and a corresponding blue shift of the Raman $E_{2g}^1$ and $A_{1g}$ vibrational modes, signaling successful electron depletion (p-doping) and formation of Mo-O bonds, respectively. Furthermore, we demonstrate precise spatial control over PL properties by confining PL treatment exclusively to the UV laser-treated area. Crucially, the enhanced PL performance shows exceptional longevity; the CVD sample and the exfoliated sample remained stable for the entire monitoring period (72 and 32 days, respectively) under ambient conditions. We further investigated UV laser treatment in a controlled-environment chamber under argon, nitrogen, and oxygen atmospheres, distinguishing the influence of oxygen as the PL treatment agent. These findings establish a reliable pathway for the permanent treatment of single-layer $MoS_2$ PL properties, an essential step toward practical, high-performance nanophotonic devices.

We examine the thermal fluctuations of the polarization $p$, the charge density $\rho$, and the electric field $E$ in dilute electrolytes inserted between pararell metallic electrodes, where we fix the applied potential difference $\Phi_a$ between the two electrodes. If the film thickness $H$ is shorter than the Debye screening length $\kappa^{-1}$, the space correlation of the polarization $p_z$ and the electric field $E_z$ along the surface normal (in the $z$ direction) acuire global components inversely proportional to the film volume $V$, which vary slowly along the $z$ axis and are homogeneous in the $xy$ plane. The areal charge density on each electrode surface also has a component homogeneous on the surface, which produces the global electric fluctuations. On the other hand, if $H$ much exceeds $\kappa^{-1}$, the global correlations of $p_z$ and $\rho$ become small in the bulk region outside the electric double layers, but that of $E_z$ remains almost unchanged by ions in the whole cell at fixed $\Phi_a$. The dielectric constant $\epsilon_{\rm eff}$ depends on $H$ and $\kappa$ and is expressed in terms of the fluctuation variances of $p_z$ and $\rho$ and that of the noblocal surface charge density at fixed $\Phi_a$.

The two-replica Chayes-Machta-Redner (CMR) representation is one of the main proposed geometric signatures of spin-glass order in the short-range Edwards-Anderson model. Mean-field arguments and recent numerics suggest that the low-temperature phase should exhibit two macroscopic blue clusters carrying opposite overlap signs. We prove a rigorous structural constraint in this direction. For any subsequential local weak limit of the standard periodic-torus joint laws on disorder, two spin replicas, and CMR bond variables, the blue subgraph contains at most two infinite connected components; if two exist, then they lie in a common infinite grey cluster and belong to opposite overlap-parity classes. The main obstacle is that the labelled blue geometry does not permit unrestricted insertions across overlap classes, and no positive-association input is available, so the usual Burton-Keane and random-cluster arguments do not apply directly. We isolate an abstract multicolour Burton-Keane proposition based on finite-box label-class coalescence and verify its hypothesis for CMR blue bonds by resampling the full joint measure. As auxiliary input, we establish finite energy and a percolation transition for the grey subgraph via local resampling of the disorder and a parity-based Peierls estimate. These results do not prove the existence of infinite blue clusters or a spin-glass phase transition, but they give a rigorous upper bound compatible with the two-cluster picture for short-range spin glasses.

Quantum spin Hall (QSH) insulators and Mott insulators are conventionally regarded as distinct insulating phases, arising from band topology and strong Coulomb interactions, respectively. Here, we report the observation of QSH edge transport in a magnetic-field-stabilized Mott insulating state at half filling of the second moire band in a 2.29 degree twisted WSe2 device. This state exhibits a resistance plateau identical to that of the single-particle QSH state at full filling of the first moire valence band, indicating the same number of helical edge channels. Electrical transport measurements reveal nearly quantized resistance that is insensitive to vertical electric field, out-of-plane magnetic field, and temperature below 5 K. Pronounced nonlocal transport and strong negative in-plane magnetoconductance further support helical edge conduction, establishing robust edge transport in the strongly correlated regime. Temperature-dependent Hall measurements reveal a characteristic temperature scale of approximately 10 K, corresponding to an energy scale of about 1 meV. Our results demonstrate that spin-conserved QSH edge states can persist in a half filled, strongly correlated insulating phase and under external magnetic field, opening a route toward interaction-resilient topological transport in moire quantum materials.

Reliable early detection of lithium-ion battery degradation requires health indicators that are physically interpretable and computable from routine cycler telemetry without access to the degradation region. We introduce CausalHealth, a framework that applies causal graph discovery and $k$-nearest-neighbour transfer entropy to per-cycle voltage, current, temperature, and resistance time series, and organises twelve resulting anomaly scores into three signal-class bundles (Magnitude-shift, Predictive-residual, Complexity-entropy) – with Isolation Forest reported separately as it falls below the bundle reliability threshold – to characterise detection sensitivity across ten commissioning fractions (5–30\,%). The Magnitude-shift class achieves 100\,% detection across all seven tested cells spanning LFP (MIT–Stanford MATR) and LCO (NASA PCoE, CALCE CS2) chemistries, with a lead time of up to 402 cycles before conventional capacity-threshold failure on gradual-fade cells. A Reliability-Weighted Master Health Index (RWMHI) – a cross-bundle fusion of five high-reliability detectors weighted by inverse coefficient of variation – improves lead time by 15–52 cycles over the class median on long-lived cells while maintaining 100\,% detection. Validation against electrochemical impedance spectroscopy on an NMC prismatic cell provides independent physical grounding: transfer entropy $\mathrm{TE}(R \!\to\! V)$ correlates with charge-transfer resistance $R_{\mathrm{ct}}$ (pooled $r = +0.990$; temperature-controlled partial $r = +0.898$), and an Arrhenius analysis of both quantities yields an activation energy consistent with published NMC charge-transfer kinetics. These results are evaluated on seven cells across three benchmark datasets.

Non-classical photon sources made of semiconductor quantum dots (QDs) emitting in the telecommunication C-band are crucial components for low-loss, long-distance photonic quantum communication networks. Here we designed and fabricated strain–free In$_{0.7}$Ga$_{0.3}$As/In$_{0.7}$Al$_{0.3}$As QDs grown on GaAs(111)A substrates working as single-photon emitters in the 1550 nm window. The QDs were grown via local droplet etching method in a molecular beam epitaxy environment, employing a thin In$_{0.7}$Al$_{0.3}$As metamorphic buffer layer with the same lattice constant of the QD material, thus allowing for a completely strain–free self-assembly of the QDs. The QDs exhibit a C$_{3v}$ symmetry with a ground state emission in the 1400–1600 nm range. The exciton lifetimes of $\approx$ 1.3–1.9 ns and linewidths as low as $\approx$ 300 $\mu$eV show the good quality of the fabricated QDs. Second-order autocorrelation measurements under pulsed excitation confirmed the single-photon purity of the emitters, yielding a $g^{(2)}(0)$ value of $0.141 \pm 0.027$

We study a generalized geometric Brownian motion framework that incorporates both entries of new units and exit mechanisms for the current population, extending earlier stochastic resetting models where these rates are treated as identical. The model captures realistic features observed in many economic observables, which can be explained as market-driven firm entries/exits, worker inflow/outflow, and income growth/loss. This model is not conservative and, despite the asymmetry in the entry and exit rates, we find that the system eventually relaxes to a stationary distribution. Moreover, our analysis reveals three distinct dynamical regimes in the moments of the distribution, arising from the interplay between volatility, drift, entry, and exit rates. We further derive the survival probability and the mean first-passage time associated with the observed variable reaching certain threshold under the competing entry-exit processes. Interestingly, we identify an optimal exit rate that minimizes the mean first-passage time, providing insights into how entry and exit policies can influence the outcome of the system. These results should be useful for understanding the long-run behavior of economic systems in which growth, volatility, entry, and exit jointly shape the evolution of heterogeneous units.

A two-phonon system with lowest-order coupling of form $Q_RQ_{IR}^2$ is studied by perturbation method, and analytic results for both phonon displacements and frequencies are obtained. The frequency renormalization of infrared (IR) active mode brings the rectification of Raman mode to saturate at high pump field. For degenerate IR mode with coupling of form $Q_R(Q_{IR,x}^2-Q_{IR,y}^2)$, the frequency of IR mode will split when resonantly pumped by elliptically or linearly polarized ultrashort mid-IR pulse, realizing Raman rectification and magnetization simultaneously. Our results reveal a dynamical effect of nonlinear phononics not captured by first-principles calculation, extend the dynamical multiferroicity to systems with coupling $Q_R(Q_{IR,x}^2-Q_{IR,y}^2)$, and the method can be readily applied to higher-order couplings. The amplitude saturation under strong pump field stimulates future researches to overcome this nonlinear effect.

Quantum error correction (QEC) is indispensable for realizing fault-tolerant quantum computation, yet its effectiveness hinges critically on the classical decoding algorithm that interprets noisy syndrome measurements. Among all possible decoding strategies, maximum likelihood decoding (MLD) is provably optimal, since it identifies the logical group with largest likelihood by summing over all possible errors within logical class consistent with the observed syndrome. Despite its optimality, MLD is computationally intractable in general (#P-hard), motivating a rich landscape of exact and approximate algorithms. In this topical review, we provide a unified perspective on MLD by surveying recent advances through three complementary lenses: statistical mechanics, tensor networks, and artificial intelligence. From the statistical mechanics viewpoint, the MLD problem maps onto evaluating partition functions of disordered spin models, enabling exact solutions for certain codes and noise models as well as threshold estimation via phase-transition analysis. From the tensor network perspective, approximate contraction of tensor networks on the code's factor graph yields decoders that closely approach MLD accuracy with polynomial computational cost. From the artificial intelligence perspective, neural-network-based decoders, including autoregressive generative models and recurrent transformers, learn to approximate the MLD distribution from data, achieving high accuracy with the parallelism afforded by modern hardware accelerators. We discuss the connections among these three approaches, review their application to both simulated and experimental quantum hardware, and outline open challenges including real-time decoding, scalability to large code distances, and generalization to high-rate quantum low-density parity-check codes.

A major puzzle in the study of the cuprate superconductivity is the origin of the pseudogap end point. Intriguingly, such a critical doping is also where the superfluid density of the system reaches its maximum. A non-monotonic doping dependence of the superfluid density is rather unusual since the Drude weight of the cuprate system is found to increase monotonically with the doping concentration. It is generally believed that such a peculiar behavior should be attributed to both the strongly correlated nature of the cuprate system and the disorder effect. In this work, we develop a variational theory for the zero temperature superfluid density of the disordered $t-J$ model. This is achieved in two steps. First, we perform an unrestricted variational optimization of an RVB variational ground state for the disordered $t-J$ model. Second, we construct the variational state that describes the paramagnetic current response on such an RVB state. The zero temperature superfluid density $\rho_{s}(0)$ is then extracted from the curvature of the variational ground state energy of the system as a function of the external electromagnetic field. We find that $\rho_{s}(0)$ computed in this way is remarkably robust against the disorder effect. More specifically, we find that $\rho_{s}(0)$ is a monotonically increasing function of doping concentration $x$ and scales linearly with the total optical weight. This is consistent with the observation in the underdoped cuprates but is strongly at odd with the behavior in the overdoped cuprates. The strong contrast between the disorder effect in the underdoped and the overdoped regime lends strong support to our previous proposal that there exist a Mott transition between a doped-Mott-insulating metal in the underdoped regime and a fermi-liquid-like metal in the overdoped regime around the pseudogap end point.

Temperature gradients are widely used to drive and probe transport phenomena in solids, forming the basis of heat-to-charge conversion processes. In typical experiments, local heating is introduced to generate a temperature gradient, and the resulting electrical response is detected by separate electrodes. Such measurements usually regard heating purely as a source of thermal excitation. Here, we show that heating inherently generates mechanical stress through thermal expansion, which in turn produces measurable electrical signals via electromechanical coupling. Using quartz as a model piezoelectric system, we demonstrate that heat can be converted to electrical currents via thermally generated stress. The on-chip device used in our experiment enables us to probe the anisotropy of the piezoelectric tensor through the thermally generated current, exhibiting twofold and threefold responses for X-cut and Z-cut crystals, respectively. We further show that the response can be detected in both current and voltage modes. These results reveal a thermomechanical pathway for heat-to-charge conversion and establish a general platform for electrically probing thermomechanical responses in insulating materials.

Heat-to-charge conversion has traditionally been realized via the Seebeck effect in conductors and pyroelectricity in polar insulators. Here, we demonstrate that temperature gradients generate electrical polarization, namely thermopolarization, in a wide range of insulators through a thermomechanical pathway. We identify a mechanism where thermal expansion under a temperature gradient produces strain gradients that induce polarization via the flexoelectric effect. Using a device with an on-chip heater, we detect the heat-induced polarization in crystalline, polymeric, and amorphous systems, including MgO, Al$_2$O$_3$, MnO, mica, PET, PEN, polyimide, and soda-lime glass. The magnitude of the response exhibits a robust scaling with the coefficient of thermal expansion, which is reproduced by finite-element simulations. Furthermore, we identify two routes to enhance the response: reducing the sample thickness and exploiting structural instabilities such as glass and antiferromagnetic phase transitions, where more than an order-of-magnitude enhancement is observed. These results establish a symmetry-independent route for heat-to-charge conversion in insulators and provide a device-compatible platform for electrically probing lattice responses, with potential for enhancement in nanoscale systems such as two-dimensional materials.

Molten iron chlorides are central to emerging energy technologies, including electrochemical iron production and redox flow batteries. Optimizing their electrochemical performance and transport properties requires atomic-scale structural understanding, yet detailed data for molten FeCl2 and its differences from FeCl3 remain scarce. Here, we determined the structures of molten FeCl2 and FeCl3 using High Energy X-ray diffraction (HEXRD), Empirical Potential Structure Refinement (EPSR), and molecular dynamics (MD) simulations with machine learning interatomic potentials (MLIPs). HEXRD measurements provided structure factors and total radial distribution functions (RDFs), which were quantitatively reproduced through EPSR refinement directly constrained by experimental data. MD simulations using MACE foundation and fine-tuned models reproduced experimental structure factors as well as total and partial RDFs, capturing key structural differences between the melts. The models resolved the octahedral to tetrahedral coordination transition of Fe upon melting in FeCl3 and predicted a similar transition in FeCl2. Analysis of MD trajectories quantified coordination environments, bridging Cl populations, bond-angle distributions, and connectivity patterns, revealing distinct degrees of polymerization and local geometry. Polymer chain statistics further showed that, contrary to prior reports, both liquids predominantly consist of extended chains containing six or more Fe centers rather than discrete Fe2Cl6 units. Finally, diffusion coefficients of the two melts calculated from the MACE-MD simulations were compared. Together, these results establish atomic-scale structural benchmarks for molten FeCl2 and FeCl3 and demonstrate the reliability of MACE-based MLIPs for predictive modeling of high-temperature molten salts, while providing practical guidance for MLIP development in complex ionic liquids.

Additively manufactured (AM) alloys have heterogeneous microstructures with broad grain size distributions and highly anisotropic and/or non-convex grain shapes. AM components can have complex geometries and porosity which may affect the local microstructure. Currently there is no electron backscatter diffraction (EBSD)-based grain size measurement standard suitable for typical AM materials. An interlaboratory comparison study was conducted to find out what grain size metrics and summary statistics are currently used to describe average grain size. Participants were asked to measure and report the average grain size from the same EBSD map dataset. Detailed reports have been published in Reference [1]. Based on these results, we have tested and propose recommendations for a new standard for measuring average grain size in AM materials. The present work demonstrates the suitability and limitations of the proposal across several different Ni and Al AM components.

Memory systems can store vastly different amounts of information despite similar hardware constraints. Here, we show that superior spatial memory emerges from a discrete stiffening of hippocampal population geometry-a transition from disorganized to crystalline collective coding. Comparing food-caching chickadees to non-caching zebra finches, we found that the caching hippocampus maintains a topologically rigid, "crystalline" geometry with significantly higher geometric stability (Shesha 0.245 v 0.166) and nearly two-fold greater temporal coherence (Shesha 0.393 v 0.209), while the non-caching hippocampus resembles a disorganized "mist." This stability is actively constructed by synergistic circuit dynamics: excitatory neurons form the spatial scaffold while inhibitory populations contribute orthogonal decorrelation, a circuit motif in which excitatory and inhibitory populations occupy largely non-overlapping representational subspaces. A double dissociation with Valiant's Stable Memory Allocator, a model predicting that dedicated neuron ensembles underlie each memory, confirms this advantage reflects continuous topological organization rather than discrete neuron allocation: caching networks exhibit near-zero split-half allocation reliability despite their geometric superiority. Computational modeling across 10k configurations reveals topological rigidity as the mathematical prerequisite for scale: crystalline codes sustain high-fidelity readout beyond M=1k locations while mist codes fail below M=10, a >100-fold capacity advantage. This capacity requires a 169fold representational redundancy: a "geometric tax" stabilizing the manifold against biological noise. These results establish geometric stability as a candidate organizing principle of biological memory: evolution achieves high-capacity memory not by proliferating neurons, but by engineering the geometry of the neural code itself.

We investigate the vibrational, optical, and electronic properties of insulating YbN thin films using Raman spectroscopy, Fourier-transform infrared spectroscopy, and electrical transport measurements, supported by density functional theory. Raman spectra reveal the LO(${\Gamma}$) phonon and a cation-vacancy mode, while the optical conductivity identifies the TO phonon and an absorption edge corresponding to a 1.7 eV N 2p{$\rightarrow$}Yb 5d transition. The films exhibit thermally activated resistivity consistent with an insulating ground state. An additional defect induced absorption tail below the intrinsic band gap is observed, which in combination with the electrical measurements indicates the Fermi energy resides in a disordered conduction band minimum.

The dominant paradigm in computational materials discovery relies on heavily parameterized deep architectures, including message-passing graph networks and equivariant models, that require millions of DFT-labeled training structures and produce non-convex latent representations that complicate continuous optimization for inverse design. These architectures are impractical in data-scarce regimes, which is the typical case in molecular screening, and exhibit well-documented limitations in capturing chemically disordered configurations and chiral geometries. This review presents feature engineering based on spatial statistics as a physically rigorous and immediately deployable alternative. Molecular structures are encoded as voxelized scalar fields, and two-point auto- and cross-correlations are evaluated deterministically via Fast Fourier Transforms, explicitly transferring the burden of spatial pattern recognition from the learning algorithm to a closed-form, physics-informed operation. Principal component analysis of the resulting correlation maps yields low-dimensional, strictly convex representations that support lean neural networks (<100k trainable parameters) and non-parametric surrogate models, achieving sub-2% prediction error with as few as 10 training samples. Demonstrated across periodic crystals, chemically disordered high-entropy alloys, and non-periodic organic molecules, this framework enables Bayesian active learning and zero-shot extrapolation on commodity hardware, which current large-scale architectures cannot replicate at equivalent data budgets.

Neural-network quantum states offer a flexible route to compact many-electron wave functions, but their practical accuracy depends strongly on how fermionic antisymmetry, electron correlation, and optimization noise are treated. Here we combine an antisymmetrized geminal power (AGP) determinant with feed-forward neural networks that replace conventional basis-set expansions in the geminal and in two Jastrow-factor constructions. The resulting basis-free Jastrow–AGP ansatz is optimized by variational Monte Carlo and is designed to separate two tasks: the AGP part defines the nodal surface, while the neural-network Jastrow factor recovers dynamical correlation at fixed nodes. This separation makes it possible to distinguish errors associated with dynamical correlation from those caused by static, multireference correlation. Applications to the hydrogen molecule and the rectangular hydrogen tetramer show sub-millihartree accuracy when the AGP nodes are adequate, and expose the residual nodal limitation near the large-radius square geometry of the hydrogen tetramer. These results clarify where neural-network building blocks can improve a compact geminal ansatz and where additional nodal flexibility is required.

Fluctuation relations imply the second-law inequality $\langle\Sigma_T\rangle\ge0$, but path extrema can also constrain how large the mean entropy production can be. For steady-state processes with entropy-production martingale $M_t=e^{-\Sigma_t}$, we show that knowing only the positive running maximum of $\Sigma_t$ gives no improvement over the trivial endpoint bound: rare negative entropy-production excursions can still carry the exponential weight required by the fluctuation relation. Using the running extrema $L_T=\inf M_t$ and $H_T=\sup M_t$, we derive a path-extrema upper envelope $\mathcal{U}_{\rm ext}$. The relaxed envelope problem ranks realized intervals by the entropy gain per martingale cost, $\ln(H_T/L_T)/(H_T-L_T)$, giving a continuous knapsack problem. The actual mean satisfies the exact identity $\langle\Sigma_T\rangle=\mathcal{U}_{\rm ext}-\mathcal{A}-\mathcal{C}$, where $\mathcal{A}$ is an allocation gap across realized envelopes and $\mathcal{C}$ is a curvature gap within each envelope. Thus path extrema set the upper envelope, while the two gaps quantify how actual dynamics allocate terminal outcomes across envelope classes and place terminal values within each realized envelope. This turns path-extrema information into a quantitative upper-bound theory for entropy production, complementary to the usual lower-bound role of fluctuation relations.

We implement manifestly microscopic many-body methods to study the dynamics of atomic impurities in a host quantum fluid, specifically $^4$He. Our investigations are motivated by experiments of muonium atoms within $^4$He with the goal of testing the universality of free fall by neutral bound states using unstable particles. Structure calculations are performed using standard semi-analytic methods; we extend here the calculations of our previos work (Journal of Low Temperature Physics 93, 415-449 (1993)) to muonic \he4, antiprotonic \he4 and mounium atoms within \he4. We find that the muonium impurity probes, due to its large zero-point motion, the atomic interaction at rather short distances. Its chemical potential is estimated to be about 19 meV. Antiprotonic \he4 has, on the other hand, a negative chemical potential. Dynamics is treated by making all correlation functions time-dependent. In analogy to the derivation of the dynamics of the background liquid, we derive a working formula for the impurity self-energy that includes the most relevant physical effects. Results for the effective mass of \he3 and hydrogen atoms agree well with available experiments. The dispersion relations of muonic \he4 and antiprotonic \he4 pass through under the roton minimum.

In the recent years it has been possible to achieve diode-like, non-reciprocal current-voltage response in Josephson junctions, despite the intrinsic symmetry of the Josephson effect itself. This is typically achieved by incorporating Rashba spin-orbit coupling into the Josephson junction as a strong inversion symmetry breaking component, and external magnetic field as a tuneable time-reversal symmetry breaking component. However, the efficiencies of the external field tuneable Josephson-diodes have remained limited to less than 10 %, often measured below 100 mK temperature. In this work we take a new approach where non-reciprocity is induced by intrinsic SOC in a heavy metal Josephson barrier via the predicted supercurrent spin-Hall effect. By measuring a series of Nb-Pt-Nb nanopillar junctions we demonstrated field tuneable Josephson diode efficiencies as high as 17%, measured above liquid Helium temperature. This was possible by the realization of a net non-equilibrium spin segregation in the Pt barrier, due to the supercurrent spin-Hall effect in the Pt barrier, analogous to the normal spin-Hall effect. As the direction of the induced spin moment is determined by the bias current, an external magnetic field causes the associated phases to add with opposite signs for opposite current directions, resulting in a nonreciprocal supercurrent across the junction.

We establish an exact Bogoliubov sum rule for any Hermitian single-particle operator $O$: at each momentum, its particle-hole and particle-particle matrix-element weights sum to the single-particle trace $\mathrm{Tr}_{s}(O^{2})$. The result follows solely from Hilbert–Schmidt-norm invariance under a canonical Bogoliubov transformation and contains neither excitation-energy denominators nor occupations; physical response identities therefore require additional assumptions. At zero field, a fully gapped helicity-diagonal state with both helicity sheets present and a spin-orbit splitting asymptotically larger than the gap obeys $\chi_{\mu\nu}(0)/\chi_N=\delta_{\mu\nu}-\Pi_{\mu\nu}+o(1)$, where $\chi_N$ is the normal-state Pauli susceptibility and $\Pi=\langle\hat{\mathbf n}_{\mathbf k}\hat{\mathbf n}_{\mathbf k}\rangle_{\rm FS}$ is the Fermi-surface average of the unit spin-locking texture. The eigenvalues of $\Pi$ form a simplex, while the normalized spin Knight-shift tensor defines an ellipsoid whose semi-axes are the residual principal responses. Full cubic invariance of both the superconducting state and locking texture fixes $\Pi=\mathbb I/3$ and hence $\chi(0)/\chi_N=2\mathbb I/3+o(1)$; cubic crystal symmetry alone does not. For zero-field $s$-wave pairing in the reference-Fermi-surface regime, we obtain the exact closed-form kernel $F_s(\lambda)=1-\operatorname{asinh}\lambda/[\lambda\sqrt{1+\lambda^2}]$, valid for arbitrary $\lambda=|\mathbf g|/\Delta$. In a finite Zeeman field, the zero-field helicity reduction generally fails, so the equilibrium magnetization and differential response require a self-consistent BdG calculation rather than a field-dependent locking-tensor substitution. Applied to the $^{75}$As data on K$_2$Cr$_3$As$_3$, the framework identifies a field-dependent axial suppression pattern at $8$–$16$~T.

We obtain the exact many-body density operator of a boundary-driven XXZ quantum circuit via a spatially inhomogeneous matrix product Ansatz. The Ansatz has formally infinite bond-dimension and generalizes authors' previous construction \cite{2025XXZcircuit} for the XXZ interactions. The boundary qubits are coupled to reset quantum channels that project them toward arbitrary pure target states. We find and describe a family of relatively robust separable chiral nonequilibrium steady states (NESS), which are elliptic analogs of spin helices for the circuit, and which are particularly attractive from an experimental perspective.

Scintillators convert X-ray energy into visible or near-visible photons, enabling applications in high-energy particle detection and X-ray imaging. Increasing scintillator thickness improves X-ray absorption but degrades spatial resolution due to diffraction-induced lateral spreading of emitted light, resulting in a fundamental trade-off between detection efficiency and image resolution. Here, we propose a class of three-dimensional photonic crystal scintillators that overcomes this limitation through supercollimation, in which light propagates with suppressed diffraction. We develop a multiscale modeling framework that integrates nanophotonic band-structure simulations with Monte Carlo particle transport to quantitatively evaluate the performance of such scintillators. Our results show that supercollimating photonic crystal scintillators can enhance spatial resolution by up to an order of magnitude relative to conventional bulk scintillators of equal thickness. This improvement leads to a substantial increase in detector quantum efficiency (DQE), particularly at high spatial frequencies, enabling fine features to be preserved at reduced X-ray dose. We further demonstrate that comparable image quality can be achieved with approximately an order-of-magnitude lower X-ray dose. By directly engineering light transport within the bulk of the scintillator, this work establishes a nanophotonic route to simultaneously improving resolution and dose efficiency in X-ray imaging.

Network science is a powerful framework allowing to model complex systems, it is capable to describe and take into account the intricate web of connections existing among the constituting basic element of the system. Recently scholars have brought to the fore the relevance of higher-order networks, namely structures allowing to encode for many-body interaction, differently from the pairwise case handled by networks. This novel research field opens new avenues of research with applications ranging from neurosciences to social sciences; there is thus a need for generative models of higher-order network capable to reproduce features present in empirical data. In this work we present a model for growing simplicial complex rooted on a preferential attachment process acting dimension-wise, i.e., returning a power law distribution for the generalized degree of simplexes of different dimension.

Elastic neutron scattering experiments showed incommensurate antiferromagnetic peaks in 5% Fe-substituted Bi-2201 cuprate in the non-superconducting heavily overdoped regime. The incommensurability delta~0.21 is comparable to that observed in Fe-substituted Bi-2201 in the overdoped regime. [Hiraka et al., Phys. Rev. B 81, 144501 (2010).] The magnetic correlation length is comparable between the overdoped and non-superconducting heavily overdoped regimes. It is plausible that incommensurate antiferromagnetic order is induced and stabilized by Fe in the heavily overdoped regime, which suggests a robust antiferromagnetic correlation beyond the superconducting dome in the phase diagram.

We study the percolation of strongly connected clusters (SCCs), in which sites are mutually reachable through directed paths, in systems with randomly oriented bonds by extensive simulations on hypercubic lattices from dimension $d=2$ to $7$ and complete graphs. Below the upper critical dimension $d_u=6$, the critical SCCs exhibit nontrivial fractal dimension $d_{\rm SCC}$, and the size distribution scales as $\sim s^{-\tau_{\rm SCC}}$ with the hyperscaling relation $\tau_{\rm SCC}=1+d/d_{\rm SCC}$. For $d \ge d_u$, mean-field behavior is recovered with $d_{\rm SCC}/d=1/3$, consistent with complete-graph results. However, in contrast to hypercubic lattices, complete graphs exhibit a double-scaling structure in the SCC size distribution: large SCCs are governed by mean-field value $\tau_{\rm SCC}=4$, while small SCCs follow a distinct power law with exponent $\tau'=1$. At criticality, the giant in- and out-clusters are also fractal, sharing the same dimension as standard percolation clusters. These results show that critical SCCs remain well-defined fractal objects across dimensions, while their approach to the mean-field limit involves nontrivial changes in cluster statistics.