Papers

Identifying spatial quantum correlations in mixed states is challenging because thermal mixed-state contributions obscure the entanglement encoded in subsystem entropy. Here, we introduce the entanglement projected entropy (EPE), a purification-independent Gaussian spatial filter for mixed fermionic states. By resolving subsystem entropy into Gaussian entropy channels and projecting their purification partners onto the physical complement, we obtain a closed-form expression in terms of the physical covariance matrix. In a one-dimensional free-fermion chain, it removes the volume-law mixed-state background and recovers the zero-temperature conformal scaling with the $c/3$ coefficient. In a two-dimensional half-filled $\pi$-flux model, it reveals a universal finite-temperature scaling collapse governed by a Dirac infrared length fixed by the low-energy velocity. These results establish EPE as an entropy-channel filter that exposes boundary-sensitive universal scaling hidden beneath mixed-state entropy.

The preparation of tensor network states is a fundamental prerequisite for a wide range of quantum simulation tasks. While many unitary protocols for preparing these states have been investigated, dissipative state preparation provides a powerful alternative since it can be robust to noise and initialization errors. In this paper, we construct both continuous-time and discrete-time geometrically local dissipative processes whose unique steady state is a given injective tensor network state. Our method prepares all injective matrix product states on $N$ sites to an error $\varepsilon$ in $O(\log (N/\varepsilon))$ time, yielding an exponential improvement over previously known dissipative preparation schemes. For two and higher-dimensional tensor network states, we prove that when the tensors of the state are highly injective, the constructed dissipative processes are rapid-mixing i.e., they prepare a state $\varepsilon$-close to the $N$-site target state in $O( \log (N/\varepsilon))$ time. For these states, our approach provides a polynomial speedup over known unitary methods for states defined on lattices and an exponential speedup for states on general bounded-degree graphs. We corroborate our theoretical results with numerical studies that indicate that the dissipative protocol can rapidly prepares states outside the high-injectivity assumption.

Force-matched water models provide a practical route from first-principles reference data to long classical and path-integral molecular simulations. Previous flexible four-site potentials in the spirit of q-TIP4P/F showed that fitting analytic models to density-functional-theory forces can reproduce key structural features of liquid water while retaining the efficiency required for quantum-nuclear sampling. Here we introduce two refinements aimed at making this strategy more accurate and more reproducible for molecular simulation. First, the production fit is based on PBE0 hybrid-functional reference forces and therefore includes the Hartree–Fock exact-exchange contribution in the electronic-structure target. Second, the parametrization is formulated as a separable nonlinear least-squares problem in which all linear force-field amplitudes are eliminated analytically for every trial set of nonlinear shape parameters. The resulting force-matching protocol lowers the dimension of the nonlinear search, reduces compensation between heterogeneous parameters, and enables a controlled comparison of Lennard-Jones and Buckingham oxygen–oxygen repulsion terms. Applied to CP2K reference forces for liquid water, the projected optimization reproduces target force distributions and yields radial distribution functions close to first-principles and neutron-scattering benchmarks. The Buckingham representation gives a more flexible short-range repulsive wall than a purely Lennard-Jones form, and the final flexible model remains stable in path-integral simulations. The method provides a transparent workflow for deriving simulation-ready water potentials from accurate hybrid density-functional reference forces.

One-dimensional non-Hermitian quasicrystals with parity and time-reversal (PT) symmetry can simultaneously exhibit localization-delocalization transition, topological phase transition, and PT-symmetry-breaking transition. This motivates this work to investigate how the absence of PT symmetry impacts topological metal-insulator transitions in non-Hermitian quasicrystals. We propose a non-Hermitian quasiperiodic model that generally does not preserve PT symmetry and demonstrate that, in most parameter regions, such a system supports triple phase transitions that encompass localization, topology, and degeneracy-breaking. The system may also exhibit a particular type of localization-delocalization transition analogous to the Hermitian case, namely, without activating topological phase transitions or degeneracy-breaking transitions. Our work extends the topological metal-insulator transitions previously studied in PT-symmetric systems to a more general class of non-Hermitian setting, and further reveals that non-Hermitian systems can host distinct types of localization behavior.

We discuss anomalous spin polarization on the surface of nuclear pasta in a neutron star, driven by a nuclear spin-orbit interaction. We present an effective two-band model of surface-localized neutrons near the nuclear pasta. The central point is the emergence of a Rashba-type spin-orbit hybridization generated by the neutron–nucleus spin-orbit force in the presence of the strong density gradient normal to the pasta surface. Starting from a single-particle Hamiltonian with a central potential and a standard nuclear spin-orbit interaction, we show that the surface spin polarization occurs due to the thermal inhomogeneity even in the absence of a magnetic field. Our study links neutron-star physics and solid-state spintronics and would contribute to understanding the interplay between spin dynamics and strong magnetic fields.

Scan-order design in laser directed energy deposition (LDED) is a delayed, path-dependent thermo-mechanical decision problem, because sequence quality becomes observable only after the complete deposition and cooling cycle. This work formulates LDED scan-order optimisation as a finite-horizon, permutation-constrained reinforcement-learning problem and develops a bilevel finite-element-teacher-labelled AI workflow. A surrogate-assisted teacher-guided optimisation loop learns the Abaqus-labelled response landscape and provides a tractable terminal-reward environment for policy training. A frozen Maskable Proximal Policy Optimization (MaskablePPO) policy is then used to generate legal scan-order candidates, which are independently validated through Abaqus thermo-mechanical simulations. The results show bounded, N-dependent policy-generation value rather than record-level dominance over the mature surrogate-assisted optimiser. The strongest scan orders are obtained by the teacher-guided surrogate loop, whereas PPO autonomously reaches competitive regions of the native response landscape, with stronger rank concentration at smaller track counts and a clear reliability boundary at longer horizons. The teacher-labelled landscape further supports a physically gated lexicographic reward hierarchy in which warpage admissibility is the primary constraint, plastic strain acts as a safety filter and residual-stress-related improvement is pursued conditionally within the admissible region. Validated sequences also reveal an interpretable scale-separated ordering tendency that combines global spatial dispersion with local structured grouping. This workflow provides a route from fixed scan-rule selection toward finite-element-teacher-validated policy generation, while preserving independent finite-element validation as the final physical gate.

Active flows are central to mixing and transport across living systems. While Newtonian fluids remain laminar, diffusive and predictable at the microscale, living fluids like dense bacterial suspensions can exhibit highly chaotic flows like active turbulence, with anomalous transport capabilities. The underlying spatiotemporally persistent structures that drive mixing in active flows, however, remain uncharted. Using Lagrangian Coherent Structures, we now uncover networks of attracting and repelling hyperbolic surfaces. We study changes in the distribution and spectra of Finite-Time Lyapunov Exponent fields in response to increasing activity. Despite the dominance of vorticity in the flow, extreme forward and backward time chaotic mixing is found to originate from straining regions, emphasizing the role of saddles. Fractal dimensions of ridges reveal a morphological simplification of LCS networks with increasing activity, while retaining isotropic crossing. Throughout our work, we also probe a hitherto unasked question-Are signatures of Lagrangian irreversibility manifest in attracting and repelling LCS? To the contrary, we find there is a striking time-symmetry. Our work takes the first steps towards linking flow structures in active turbulence to invariant mixing surfaces. These findings will crucially help in designing activity modulation protocols to seed or inhibit flow structures, and thence mixing, in a bid to tame active turbulence for varied applications.

A GW-BSE approximation scheme is assessed by applying it to a model of asymmetric two-dimensional (2D) interacting electron system. The model is assumed to have a parabolic band characterized by two independent effective mass parameters. A perpendicular magnetic field is applied to the asymmetric 2D electron system, and the well-known Kohn's theorem is still valid, i.e., the cyclotron resonance is not affected by the electron-electron interaction. This theorem imposes a constraint on the approximation scheme employed in the treatment of electron-electron interaction. In the present study, the Green's function is calculated in the self-consistent Hartree-Fock approximation. The electron density correlation function is calculated by solving a Bethe-Salpeter equation (BSE) in the ladder diagram approximation. It is found that, the excitation frequency near the cyclotron resonance frequency approaches a value that is lower than the cyclotron resonance frequency at small wave vectors, when two effective masses are different. When two effective masses are the same, the excitation frequency approaches the cyclotron resonance frequency at small wave vectors as required. Our findings suggest that the approximation scheme used in this theoretical investigation fails to satisfy the requirement due to the Kohn's theorem, and one should go beyond this approximation scheme.

Since the first report of a high entropy alloy (HEA) superconductor in 2014, HEAs have continued to captivate the interest of superconducting researchers. Owing to the significant degree of disorder inherent in these systems, they serve as exemplary models for examining the properties of materials that exist in states intermediate between crystalline and amorphous structures. Here we present the superconductivity properties and crystal structure of TaNbHfZrAl and TiHfNbTaAl HEAs, which both have the VEC of 4.2 and body centered cubic (BCC) structure. Through resistivity, magnetic, and specific heat measurements, we prove that both samples are the bulk type-II superconductors with a critical temperature Tc of 5.5 K for TaNbHfZrAl and Tc of 3.2 K for TiHfNbTaAl. The Tc of HEA superconductors is influenced by the VEC and the element composition. And the incorporation of Al in high disorder HEA superconductors causes a more crystallinelike Tc dependence.

This work reports rich localization-delocalization transitions in a quasiperiodic ladder, of which the two legs are subject to the same quasiperiodic onsite potential but can be shifted laterally relative to each other. It is found that the lateral shift between the two legs effectively generates a magnetic flux in the reciprocal momentum space. The lateral shift thus offers a control knob, allowing us to access and simulate rich phenomena including magnetic-flux-enhanced localization, magnetic-flux-suppressed localization, and magnetic-flux-induced reentrant localization transitions. The underlying physical mechanisms as well as the phase boundaries separating localized, mixed, and extended phases are both qualitatively and quantitatively understood, based on a band-structure analysis that employs a commensurate approximation to the quasiperiodic potential, requiring only unit cells of small to modest sizes. Our work provides a highly tunable platform for exploring localization physics with promising applications such as quantum switching, and a broadly applicable approach for understanding localization-delocalization transitions in quasiperiodic systems.

Cross-functional fine-tuning of machine-learning interatomic potentials (MLIPs) is often treated as a relabeling problem, where configurations generated at one density-functional level are relabeled using a higher-fidelity target functional. However, the resulting training data may be drawn from the wrong equilibrium distribution, because the statistical weights of configurations change across exchange–correlation functionals. Here we address this distribution mismatch using a target-distribution-guided workflow based on self-learning hybrid Monte Carlo (SLHMC), in which trial configurations are proposed by a machine-learning potential and accepted or rejected using target-functional density-functional-theory energies. Using rutile TiO$_2$ as a test system, we fine-tune the MACE-MP-0 foundation potential toward PBE, r$^2$SCAN, and HSE06 target functionals. The resulting adapted potentials reproduce target-anchored nearest-neighbor Ti–O distributions, radial distribution functions, and the NPT cell metrics examined here more accurately than the foundation-model and off-target relabeling controls considered in this work. In particular, HSE06-guided fine-tuning improves structural and thermodynamic properties that are difficult to access with direct hybrid-functional molecular dynamics because of the computational cost of exact exchange. These results indicate that target-distribution coverage is an essential component of cross-functional MLIP transfer, and that accurate target-level labels alone may be insufficient when the configurational distribution is mismatched.

Microwave-dressed polar molecules offer a route to lattice quantum simulators in which the angular form of long-range dipolar interactions, not only their overall strength, can be engineered. We study this setting in a minimal hard-core Bose lattice model on a square optical lattice, with particles interacting through a sign-changing non-axisymmetric dipolar tail \mathcal V(\mathbf r)\propto (x^2-y^2)/(x^2+y^2)^{5/2} that is repulsive along one lattice axis and attractive along the other. Using worm-algorithm path-integral quantum Monte Carlo simulations, supported by a hard-core spin mapping and a Gutzwiller soft-mode diagnostic, we find two regimes controlled by t/V: at larger t/V the system remains superfluid but develops a pronounced directional stiffness anisotropy, while at smaller t/V it forms a stripe solid selected in the (q,0) axial family, corresponding to real-space stripes parallel to y. The leading ordering wave vector remains in this axial family but reorganizes with filling, showing that the robust ordered object is a family of stripe states rather than one fixed commensurate Bragg peak. Near the closure of the stripe lobe, averaged observables can mimic a narrow supersolid signal; measurement-resolved stripe structure-factor histograms instead reveal first-order switching between superfluid and stripe-solid sectors. NaCs lattice estimates place the relevant V/t window within reach of modest effective dressed dipole moments, linking the predicted stripe-family order and its experimental diagnostics to accessible molecular quantum-simulation scales.

Microswimmers are often found in heterogeneous and crowded environments within narrow conduits under external flow conditions, enabling them to perform interesting translational and rotational maneuvers, such as swimming in the upstream direction, following walls, and oscillatory motion. Studying such systems helps us understand the motility behaviors of microswimmers (pushers, pullers, or neutrals) and develop applications such as targeted drug delivery. To study the motion of microswimmers in a channel flow with the presence of hard, monodisperse spherical colloids, we adopted the spherical squirmer model to represent the microswimmers, along with a mesoscale simulation framework, multi-particle collision dynamics (MPCD), to represent the background fluid. In the absence of colloids, a squirmer in a microchannel flow develops an increased probability of moving away from the walls and oscillates between the walls as the flow speed increases compared to the squirmer speed, with a dominant upstream orientation near the walls. However, the presence of the colloids makes the pusher swim towards the center of the channel and upstream direction, and the puller swim away from the center of the channel at low flow speeds. At high flow speeds, the flow carries all the squirmers, resulting in a dominant upstream direction in the channel center. We observe that this leads to a decrease in the local velocity of the squirmer in the flow direction for pusher, neutral, and puller-type squirmers. We also observe that, for a constant colloidal packing fraction, the local velocity magnitude of the puller along the flow direction is less than that of the pusher.

Sodium bismuth titanate (NBT) is a promising oxide-ion conductor,but its electrical conductivity is highly sensitive to small changes in A-site stoichiometry and processing history.This sensitivity can reduce sample-to-sample reproducibility.Here we examine how precursor mixing controls structural uniformity and ionic transport in Na0.52Bi0.47TiO3 ceramics.Dry grinding,wet grinding with ethanol,and ball milling were compared by X-ray diffraction,electron microscopy,energy-dispersive spectroscopy,Eu3+ photoluminescence excitation spectroscopy,and electrochemical impedance spectroscopy.All processed powders and ceramics form the perovskite NBT phase within the detection limit of XRD.However,the microstructure,surface A-site cation ratio,Eu3+ excitation spectra,and electrical response change strongly with the mixing route.Continuous monitoring of Eu3+ excitation spectra at different emission wavelengths reveals different distributions of local Eu3+ environments.Larger spectral-shape variations are consistent with lower structural uniformity and stronger local distortion.Dry-ground samples show higher bulk conductivity than wet-ground samples,whereas wet-ground samples show much lower grain-boundary resistance.At 600 \u2103,the dry-60 min sample reaches a bulk conductivity of 13.54 mS cm-1,while wet-30 min shows the highest grain-boundary conductivity of 13.72 mS cm-1.These results suggest a processing-driven trade-off between bulk defect generation and grain-boundary blocking.Based on this processing understanding,Ca was introduced at the A site in Na0.52Bi0.47-xCaxTiO3.The x=0.04 sample reaches 8.35 mS cm-1 at 500 \u2103 and 18.98 mS cm-1 at 600 \u2103.

Cells deploy robust mechanisms to repair DNA damage, safeguarding genomic stability and cellular health, but the physical principles underlying these processes remain incompletely understood. Experiments show in vitro that upon a DNA double-strand break, a DNA–protein condensate can tether the broken DNA ends before they disperse away, a critical step for subsequent repair biochemistry. However, it remains puzzling how such condensation reliably achieves spatiotemporal localization at the break site and captures both broken ends despite intrinsic stochasticity. Here, we propose that broken DNA ends can trigger a conversion of proteins from a soluble state to a condensate-competent state. Combining this idea with Brownian dynamics simulations and theory, we propose a physical mechanism for reliable DNA-end tethering. Simulations show that such break-induced conversion can drive local DNA–protein condensation with two possible outcomes: successful or failed tethering. To rationalize this, we construct an effective free energy landscape, identify the corresponding stationary states, and demonstrate that tethering is governed by a kinetic competition between polymer relaxation and condensation dynamics. Together, our study shows that DNA end-dependent conversion, coupled with DNA–protein condensation, can reliably tether broken DNA ends.

We investigate the renormalization group (RG) domain walls interpolating between the $\mathbb{Z}_N$ parafermion theory (the critical $N$-state Potts model) and the Virasoro minimal model $\mathcal{M}_{N+1}$. These flows are genuinely non-perturbative and an explicit construction of Gaiotto type RG domain wall remains elusive. We bypass this limitation by employing a bottom-up approach centered on the emergence of “phantom currents". By tracking the preserved non-invertible symmetries ($\mathfrak{so}(3)_N$) along the flow, we extract the exact spectrum of these currents localized on the defect. We demonstrate that the presence of a spin-1 phantom current allows the interface to be marginally deformed, dynamically generating a continuous defect conformal manifold. Furthermore, we show that an extra spin-2 operator, crucially as a $W^{(3)}$-algebra descendant of the spin-1 phantom current, rigidly constrains the UV-IR stress tensor mixing via the cluster decomposition principle. This algebraic framework enables the exact computation of the parameter-dependent transmission rate across the conformal manifold, which we observe strictly vanishes in the large-$N$ limit as a consequence of macroscopic target space collapse.

An efficient cooling effect is put forward, by means of external electric or magnetic fields along hysteresis loops. A simplified model of hysteresis is used for numerical illustration. The model is based upon a second-order expansion of the energy in powers of polarization and external field. The electrocaloric effect along hysteresis loops is discussed for comparison.

We investigate the strong correlation effect in the spinless electron-hole two-band Hubbard model using the dynamical mean-field theory. At half filling, both the renormalization factor $Z$ and the number of conduction electrons (valence holes) $n_c$ decrease with increasing the interband Coulomb interaction $U$ down to $Z\sim 0.15$ and $n_c\sim 0.1$ for $U_c \sim \mbox{bandwidth}$ at which the first-order Lifshitz transition occurs from a correlated semimetal with a large effective mass $m^*/m=Z^{-1}$ to a band insulator with a finite gap due to the Hartree shift. A slight hole doping $x$ in the band insulator with a large $U>U_c$ yields a remarkable correlated semimetal with $Z\sim 0.1$ at $x\sim 0.01$, where in-gap states emerge above the valence band top and those weights increase with increasing $x$ together with the increase in $Z$ similar to the in-gap states observed in doped Mott insulators.

We investigate the transport of elastic active filaments in two-dimensional turbulence, focusing on how propulsion geometry and elasticity determine vortex trapping and transport. Using a bead-spring model with activity applied at the filament head, we compare propulsion that follows the instantaneous filament conformation with propulsion imposed along a fixed external direction. We find that activity does not generically enhance transport: when propulsion remains coupled to the filament backbone, vortex trapping remains dominant and motion stays effectively diffusive, whereas fixed-direction propulsion enables persistent excursions across flow structures and leads to superdiffusive transport. In both cases, activity shifts filament conformations toward more extended states, effectively opposing elastic relaxation without eliminating preferential sampling of coherent vortical regions. At low Weissenberg number, this conformational change is amplified: activity cooperates with elasticity to enhance preferential sampling of vortical regions and strengthen vortex trapping. Transport therefore emerges from a competition between activity, elasticity, and flow-induced deformation, with elasticity determining how effectively activity-induced extensions can persist against turbulent trapping. These results establish propulsion geometry as the key control parameter for transport, with elasticity and activity acting cooperatively rather than independently to shape filament dynamics in turbulent flows.

Charge transfer in concentrated alloys governs their structural stability and functional response, and can be strongly perturbed by lattice defects. In high-entropy alloys, the interaction between edge dislocations and volume misfit plays a central role in solid-solution strengthening models; however, the influence of dislocations on the local charge transfer has not been explicitly investigated. In this work, large-scale ab initio calculations are employed to examine the dislocation-mediated charge transfer in CoNi, CoCrNi and CoCrFeMnNi alloys. The calculations reveal an anomalous charge redistribution near edge dislocation cores, including deviation from the conventional electronegativity trend. The observed behavior is shown to originate from collective electronegativity equalisation effects rather than simple pairwise atomic interactions. Furthermore, the asymmetric atomic volume response within the compressive and tensile regions of the dislocation field is rationalised in terms of anomalous magneto-volume fluctuations. These results establish a direct coupling between dislocation-induced electronic redistribution and local volumetric response in chemically complex alloys. The demonstrated coupling between dislocation-mediated charge transfer and atomic volume fluctuations provides a pathway toward electronically informed solid-solutions strengthening models and defect-aware alloy design strategy for chemically complex alloys. These findings further suggest that local electronic redistribution near dislocation-cores can play a critical role in governing the deformation behavior and defect-enbergetics in high-entropy alloys.

Hydrogen-related fracture in body-centered cubic Fe and ferritic steels often appears as transgranular quasi-cleavage rather than purely intergranular failure, especially at low to moderate hydrogen contents. Fractography has suggested that hydrogen may change the dominant cleavage faceting from {100} toward {110}, but atomic-scale evidence for this possible crack-plane transition remains unclear. Here we construct an efficient neural-network potential for {\alpha}-Fe/H and combine large-scale, three-dimensional molecular dynamics with grand-canonical Monte Carlo (GCMC), allowing the near-tip crack-surface region and crack tip within a defined GCMC domain to exchange hydrogen with a reservoir at fixed chemical potential. A comparison of four crack systems identifies the controlling response: (100)[010], (100)[011], and (110)[001] remain cleavage-dominated, whereas the (110)[1-10] crack changes from dislocation emission in pure Fe to cleavage under hydrogen charging. The energetic origin is twofold. Hydrogen lowers the Griffith cleavage threshold of the {110} cleavage-plane family more strongly than that of {100}, and, for the controlling crack, a Rice-type energetic descriptor indicates that the surface-energy-controlled cleavage resistance decreases faster than the unstable-stacking-fault-controlled emission resistance, consistent with a weakened dislocation-emission shield. These results provide a thermodynamically consistent atomistic basis for a hydrogen-induced transgranular crack-plane transition in Fe.

Determining the spin Hamiltonian of a magnetic compound is crucial for understanding its magnetic properties. A standard approach is to derive model parameters from ab initio calculations based on the crystal structure. However, the resulting Hamiltonian can depend sensitively on methodological details of the ab initio procedure. This issue is particularly evident in $\alpha$-RuCl$_3$, a candidate Kitaev material. Here, we present an alternative, data-driven approach to determine the spin Hamiltonian parameters of $\alpha$-RuCl$_3$ by Bayesian optimization of experimental magnetization curves along the $b$- and $c$-axis directions. We optimize five parameters, namely the Kitaev interaction $K$, off-diagonal interactions $\Gamma$ and $\Gamma'$, the Heisenberg interaction $J$, and the $c$-axis $g$-factor $g_c$. The parameter set that minimizes the cost function is $(K,\Gamma,\Gamma',J,g_c)=(-6.0, 7.5, -0.3, -1.75, 2.3)$, where the exchange couplings are in meV. We find that the cost function is insensitive to the absolute value of the Kitaev coupling $K$. Thus, the magnetization data alone do not determine its energy scale. The cost function also depends only weakly on $\Gamma'$ and $J$, while the optimization favors a large positive $\Gamma$. By computing the static spin structure factor, magnetic susceptibility, and specific heat, we show that these quantities favor the large-$\Gamma$ scenario over the small-$g_c$ scenario and that the parameter set that minimizes the cost function yields good agreement with the experimental structure factor and susceptibility, and qualitative agreement with the specific heat. The combination of Bayesian optimization and accurate low-energy solvers provides an effective approach for determining parameters of spin Hamiltonians. This methodology opens a systematic route to determining spin Hamiltonians in quantum magnets from experimental data.

We present a comprehensive theoretical study of strain-engineered quantum transport in monolayer tungsten diselenide (WSe$_2$) in the presence of an electrostatic scalar potential. By incorporating strain effects within a low-energy Dirac framework, we analyze their impact on spin- and valley-resolved transmission, conductance, and polarization. The applied potential barrier partitions the system into three distinct regions, allowing for an analytical derivation of the wave functions in each domain. Enforcing continuity conditions at the interfaces yields exact expressions for the transmission and reflection amplitudes. The transmission probability is evaluated from the corresponding current densities, while the conductance is obtained using the Landauer-Büttiker formalism, enabling a quantitative determination of spin and valley polarizations. Our numerical analysis reveals that strain acts as a powerful tuning parameter that reshapes the electronic dispersion and strongly modifies transport characteristics. In particular, the transmission and conductance exhibit pronounced oscillatory behavior driven by quantum interference and resonant tunneling mechanisms. More importantly, both spin and valley polarizations display substantial and highly controllable variations as functions of strain, barrier height, and incident energy. These results demonstrate that strain and electrostatic engineering provide an efficient and versatile platform for manipulating spin-valley degrees of freedom in WSe$_2$. The ability to tailor polarization and interference effects suggests promising opportunities for the design of next-generation spintronic, valleytronic, and optoelectronic devices based on two-dimensional transition-metal dichalcogenides.

Red persistent phosphors remain less mature than green and blue-green systems because their afterglow is often weak and decays rapidly. Here, CaTiO3:0.3%Pr3+,0.3%Al3+ phosphors were prepared by a high-temperature solid-state route under either air or vacuum-sealed quartz-tube (QT) conditions. The effects of processing atmosphere and sintering temperature on phase structure, microstructure, steady-state photoluminescence, afterglow, thermoluminescence, and excited-state decay were examined. X-ray diffraction and Raman spectra show that all samples retain the orthorhombic CaTiO3 perovskite phase, with no detectable secondary phase. SEM observations show particle coarsening at higher QT temperatures, while EDS mapping indicates a homogeneous distribution of Ca, Ti, O, Pr, and Al within the examined region. The QT-treated samples exhibit stronger Pr3+ red emission near 612 nm and markedly improved afterglow compared with the air-treated sample. The QT-1300 sample shows the best afterglow among the present samples, with a reported 3.6-fold higher intensity at 1200 s than Air-1200{\deg}C. Thermoluminescence results indicate that QT treatment increases the population of thermally active traps and enhances the deeper trap component. These results suggest that a low-oxygen sealed environment regulates defect-related traps, most likely involving oxygen-vacancy-associated centers, and improves carrier storage and release through the Pr3+ and Ti4+ intervalence charge-transfer pathway. This work provides a practical processing strategy for improving CaTiO3-based red persistent phosphors and offers insight into trap-state regulation under low-oxygen thermal treatment.

Kagome superconductors featuring topologically nontrivial band structures have attracted extensive research interest. FeSn and CoSn is a new kind of kagome material with intrinsic magnetism, which suppresses the emergence of superconductivity. Here, we theoretically predict a new kind of 1:1 kagome MSn (M=transition metal), which exhibit intrinsic superconductivity and nontrivial band topology by first-principles calculations. Among twenty-seven candidates, MSn (M= Mo, Hf, Nb, Ta, W, Ti) are theoretically identified as both dynamically and thermodynamical stable. And, five non-magnetic MSn (M= Mo, Hf, Nb, Ta, W) exhibit phonon-mediated superconductivity. Especially, the d orbitals bands display Dirac points and van Hove singularities near the Fermi level, which contribute to the emergence of topology and the electron-phonon coupling (EPC). More interestingly, MoSn, HfSn and NbSn show nontrivial topological band structure at the Fermi level. Thus, the predicted MSn establish a platform integrating superconductivity and topological order.

Information compression plays a central role in diverse fields of modern science and technology, from communication theory to machine learning. In condensed-matter physics, the intermediate representation (IR) basis has recently been developed as an efficient method for compressing imaginary-time Green functions, which are fundamental quantities for describing quantum many-body systems. This compression relies on the rapid decay of the singular values with the basis index and the unusually weak growth of the effective rank with inverse temperature. Because of these useful features, the IR basis is now widely used as a standard method in quantum many-body calculations. However, the analytic origin of its compression capability has remained unclear. Here we uncover a finite-Laplace-transform structure underlying the IR kernel, which reveals that the eigenfunctions of the IR kernel admit a natural expansion in terms of classical special functions, the oblate spheroidal wave functions. This finite-Laplace-transform structure also enables us to analytically clarify the compression mechanism of the IR basis. Our results provide a mathematical foundation for the compression of imaginary-time Green functions, connecting quantum many-body physics with theories of information compression and finite integral transforms.

We perform hybrid density functional calculation to study the energetics, electronic properties, optical transitions, and migration barriers of native defects in CaWO$_4$. Oxygen and calcium vacancies are most likely to form in the absence of doping, but interstitials could also incorporate. Tungsten-related defects are unlikely to be present. The positively charged $V_{\rm O}$ and the negatively charged $V_{\rm Ca}$ are likely to form complexes. Calculated optical transition levels indicate that experimentally observed absorption and emission peaks can be attributed mainly to oxygen-related defects. Calculations of migration barriers allow us to conclude that Ca$_i^{2+}$, $V_{\rm O}^{2+}$ and O$_i^{2-}$ are highly mobile, even below room temperature. We have also examined Er dopants, finding that erbium easily substitutes on the Ca site in a positive charge state. Erbium can form complexes with $V_{\rm Ca}$ and O$_i$, which would deactivate the Er. If Er is introduced by implantation, Er interstitials are likely present, which will produce emission that is prone to spectral diffusion and blinking. Our calculated properties of Er$_i$ explain why annealing at modest temperatures allows the interstitials to move into substitutional sites and point defects to move away, resulting in stable emission.

BaTiO3 is a model ferroelectric perovskite whose properties are highly sensitive to local structure, defect chemistry, and dopant distribution. However, conventional diffraction mainly probes the average lattice and can miss subtle changes in the local coordination environment. Here we use Eu3+ photoluminescence as a local optical probe for BaTiO3 ceramics prepared at 1250, 1300, and 1350 {\deg}C. X-ray diffraction and Raman spectra show that all samples retain the tetragonal BaTiO3 phase within the detection limits of these techniques. Electron microscopy reveals a porous ceramic microstructure with temperature-dependent grain growth, and elemental mapping confirms a spatially distributed Eu signal. The Eu3+ excitation and emission spectra show strong sensitivity to the processing temperature. The sample sintered at 1250 {\deg}C gives the highest emission intensity, while higher sintering temperatures change the relative intensity of the charge-transfer band and the 4f-4f transitions. Most importantly, the 5D0 to 7F0 emission contains two reproducible components near 579.5 nm and 582.2 nm. Their relative weights vary with sintering temperature, and double-exponential decay at 612 nm further supports the presence of multiple Eu-related local environments. These results show that Eu3+ luminescence provides a sensitive route to track local structural heterogeneity in BaTiO3 ceramics.

Si/SiGe quantum wells that incorporate Ge concentration oscillations, known as long-period Wiggle Wells, have been shown to enhance the Dresselhaus spin-orbit coupling of conduction-band electrons. Such intrinsic spin-orbit coupling is desirable when performing spin-qubit gate operations based on electric dipole spin resonance (EDSR) because it eliminates the need for external micromagnets. However, random-alloy disorder plays a key role in this materials system by spatially randomizing the valley splitting and the valley phase $\phi_{s,s}$, and it has not been fully accounted for in recent EDSR analyses. Here, we show that alloy disorder affects EDSR in two main ways. First, the Rabi frequency $\Omega$ acquires a dependence on the valley phase, given by $\cos\phi_{s,s}$, which causes spatial randomization of $\Omega$. Despite this variability, we show that fast EDSR can be achieved at most locations across a given sample. Second, a new Rabi driving mechanism emerges, enabled by valley dipoles and the hybridization of ground and excited valley states, which arise from alloy disorder and EDSR driving, respectively. This mechanism is dominant in regions of low valley splitting. Alloy disorder can therefore strengthen EDSR, but it can also cause gradients in $\Omega$ that lead to dephasing in the rotating frame. We explore this problem by first locating "sweet spots," where EDSR is relatively insensitive to electric-field fluctuations. We then show that high-fidelity Rabi oscillations can be achieved in the presence of realistic charge noise. These results suggest that Wiggle Wells are a promising platform for high-quality, micromagnet-free gate operations.

We present a probabilistic modeling framework for incorporating small-scale spatial heterogeneity into macroscopic descriptions of material behavior for polycrystalline metallic materials. Spatially heterogeneous material state fields are represented using probability density functions (PDFs), providing a principled statistical description of microstructural variability and state evolution across different computational polycrystalline realizations. The framework is built on the inverse identification of a probabilistic transport model, formulated as a Liouville equation with an unknown drift term. To enable accurate, stable, and interpretable inference of this drift field in high-dimensional, transport-dominated settings, we develop a Separable Probability Learning Technique via Physics-Informed Neural Networks (SPLIT-PINN). This method incorporates a marginal-correction drift decomposition, orthogonality constraints, and residual-based adaptive training to enhance well-posedness, numerical stability, and physical consistency without imposing restrictive parametric assumptions. Using SPLIT-PINN, the drift field governing the temporal evolution of joint state PDFs is inferred directly from data. After benchmark validation, the framework is applied to physical computational datasets describing the evolution of polycrystalline microstructural states, including von Mises stress, dislocation density, and equivalent plastic strain rate. The learned Liouville model, trained on a single dataset, is subsequently used in forward predictions of the temporal evolution of joint and marginal PDFs for multiple unseen polycrystal realizations. Quantitative comparisons with reference PDFs demonstrate that the proposed framework yields accurate and robust probabilistic predictions and generalizes effectively across datasets.