Papers

Fluid-structure interaction (FSI) in viscoelastic flows past deformable structures at low Reynolds numbers remains poorly understood, despite its relevance to biological systems such as cilia and flagella, and to engineered microsystems. We investigate viscoelastic FSI in side-by-side flexible cantilever arrays using a bottom-up approach that systematically varies the number of cantilevers and the rheology of the test fluid, comparing weakly shear-thinning (WS) and highly shear-thinning (HS) polyethylene oxide solutions. For both fluids, with increasing Weissenberg number (Wi), an elongated elastic wake develops behind a single isolated cantilever. For multiple cantilevers, the WS fluid undergoes a transition at a critical Weissenberg number (Wi*) from separated to merged elastic wakes, accompanied by the emergence of a divergent flow field and coordinated inward spanwise cantilever deflection. The critical Wi* increases as the number of cantilevers in the array is increased from two to three, demonstrating a strong dependence of the onset on the array configuration. For the HS fluid, wake merger, flow divergence, and inward cantilever deflection are suppressed across the full range of Wi investigated, despite comparable elasticity and the formation of elastic wakes. This contrast shows that elasticity alone promotes wake formation but is insufficient to produce the collective instability, which instead requires both shear-thinning and interactions between neighboring cantilevers. These findings demonstrate that viscoelastic FSI in flexible arrays is governed by the combined effects of fluid elasticity, shear-thinning, and geometric configuration, with elastic wake interactions mediating the collective instability and linking local elastic wakes to array-scale structural response.

The microscopic origin of anomalous orbital polarization in a class of low-dimensional correlated oxides remains unresolved due to the competition among crystal-field effects, electronic correlations, and orbital-dependent dimensionality. Using dynamical mean-field theory, we investigate a two orbital Hubbard model with orbital-dependent dimensionality and reveal the mechanism for anomalous orbital polarization through orbital reconstruction beyond the bare crystal-field picture. We identify interorbital Coulomb interaction-induced charge competition as a key microscopic mechanism responsible for the orbital redistribution, which leads first to an orbital-polarized correlated metal and subsequently to an orbital-polarized Mott insulator. We further find that Hund's coupling acts as a filling-dependent regulator of orbital reconstruction. It weakens the correlation-induced orbital redistribution at quarter filling by competing with interorbital charge fluctuations, while at half filling it completely suppresses the orbital-polarized state by stabilizing high-spin orbital-balanced configurations. These results provide a unified picture of correlation driven orbital reconstruction and highlight the relevance of interorbital interactions and Hund's coupling for understanding orbital phenomena in low-dimensional transition-metal oxides.

Perfect deterministic distinguishing of nonorthogonal quantum states is forbidden by the linear and unitary structure of quantum mechanics. It has often been assumed that, if such distinguishing were available, it would be the resource enabling work extraction from a single heat bath. We show that this expectation identifies the wrong thermodynamic operation and prove such hypothetical operation increases, rather than decreases, the joint entropy of system and detector. The entropy-decreasing resource is instead the inverse operation, which we call nonorthogonal-state erasure. Reanalyzing a Peres-type Szilard engine, we show that the apparent extracted work $W_{\mathrm{ext}}=0.2766k_{\mathrm{B}}T$ for an equal mixture of an atomic ensemble with spin state $\left|\uparrow\right\rangle $ and $\left|\rightarrow\right\rangle $. Thus the apparent second-law violation is supplied not by nonorthogonal-state distinguishing, but by a nonorthogonal quantum state erasure.

We show that the network of quantum register states of a quantum computer (QC), coupled through residual two-body interactions between qubits, exhibits small-world properties analogous to those of complex networks found in human society. The most probable Erdős number between any two states is about 9, comparable to the six degrees of separation reported by Milgram for social networks. Using the $\mathring{A}$berg criterion, which retains only interactions exceeding the local energy spacing, we construct an effective ($\mathring{A}$berg) network and show that, above a critical coupling strength, this network percolates into a giant component spanning nearly the whole quantum register space. This percolation transition closely matches the onset of quantum chaos and dynamical thermalization established previously via costly exact diagonalization, while our approach extends the accessible system size up to $n_q=30$ qubits.

We present the first application of simulation-based inference to resonant inelastic X-ray scattering spectroscopy. Using truncated marginal neural ratio estimation to efficiently restrict the prior and conditional flow matching as the joint density estimator, we infer full posteriors with a modest simulation budget for two Ni$^{2+}$ compounds—NiPS$_3$ as a representative covalent case and K$_2$NiF$_4$ as a more atomic one. We demonstrate that a vision transformer encoder whose tokenization matches the physical layout of the RIXS map yields better-covered and sharper posteriors than generic image encoders. Applying the validated method to experimental NiPS$_3$ and K$_2$NiF$_4$ data, we recover a joint posterior that reveals parameter correlations invisible to point estimators, and a posterior predictive distribution that closely matches the observed spectrum. The amortized posterior unlocks a class of analyses not previously available to the field such as nuisance-marginalized uncertainty quantification, multi-measurement posterior fusion and active experimental design.

Graphons are measurable functions used to describe the asymptotic behavior of convergent graph families. Originally motivated by problems in combinatorics and graph theory, graphons have found numerous applications in the modeling and analysis of dynamical processes on networks. In this work, we use graphons to formulate the Ising model on convergent graph sequences, which include many network topologies common in applications. We derive the mean-field limit for the resulting model and obtain exact results for phase transitions in such systems. Specifically, we show that the critical temperatures of the Ising model on graphons are determined by the eigenvalues of the Hilbert-Schmidt operator associated with the graph limit. For many important network topologies, these eigenvalues can be computed explicitly. We illustrate our results with three representative random network models: Erdős-Rényi, small-world, and power-law. In the small-world case, we demonstrate phase transitions to both ferromagnetic and antiferromagnetic phases, as well as coexistence of local minima of the free energy. The latter gives rise to multistability, as confirmed by Monte Carlo simulations. The results of this work demonstrate that the Ising model on graphons combines the analytical tractability of exactly solvable mean-field models with the ability to accommodate a broad range of network topologies. We expect that the use of graphons in spin models will lead to new insights into the statistical physics of interacting systems on complex networks.

We demonstrate programmable, frequency-tunable and size-selective drop motion on an elastic metastrip substrate with a centipede-like array of cantilever resonators. Under harmonic excitation, the strip experiences simultaneously an in-plane (IP) collective motion and out-of-plane (OOP) deformation, thus establishing a frequency-dependent IP-OOP phase landscape. This prescribes the direction of motion to the drops on the strip based on their position, causing the emergence of clustering and rarefaction regions. By tuning the tip masses of the resonators, we open reconfigurable OOP bandgaps that modify the IP-OOP phase makeup and locally suppress drop motion, contributing an additional layer of spatial selectivity. Using multi-frequency excitations, we selectively actuate drops based on their resonances, effectively filtering them by volume and position.

Topological charge pumping is paradigmatically understood through two-band systems such as the Rice-Mele model, which are intrinsically restricted to a single independent pumping channel. In this work, we introduce the Trimer Thouless Pump (TTP), a minimal three-band generalization that exhibits genuinely multigap topological transport. By subjecting a one-dimensional three-site lattice to cyclic adiabatic modulations of its hopping amplitudes and antisymmetric onsite potentials, we explore a topological regime characterized by two independent bulk gaps. We show that the quantized charge transport is driven by highly localized Berry curvature hotspots corresponding to effective two-level Dirac avoided crossings on the parameter torus. Crucially, we demonstrate that the middle energy band acts as a geometric mediator: it facilitates the exchange of quantized Berry flux between the upper and lower bands while maintaining a net zero Chern number itself. This bulk topology is corroborated by the spectral flow of boundary-localized edge states traversing multiple gaps. Furthermore, we map the topological phase diagram as a function of central-site detuning, illustrating a band-selective transfer of topological invariants across discrete phase transitions. Finally, we propose a concrete experimental protocol to realize the TTP and observe its multigap charge transport using ultracold atoms in phase-controlled optical superlattices.

Deep learning models of nanocrystal synthesis enable the prediction of size and shape by encoding precursors and reaction conditions. However, their black-box nature hinders gaining deep insights into the underlying synthetic mechanisms. Here, we develop the Nanocrystal Equation Learner (NanoEQL), a fully white-box neural network to unravel the size determination mechanisms of nanocrystal synthesis. Building on the EQL architecture, eight operators are introduced to replace standard activation functions to fit the mathematical equations in nanocrystal synthesis. Among these operators, three smoothed operators address the gradient explosion of singular operators at zero. To evaluate the weights of different precursors, we develop a temperature-gated attention pooling strategy that encodes concentration-driven and reactivity-driven chemical synthesis mechanisms into the temperature gate. The NanoEQL model illustrates that the final nanocrystal size can be described by a linear equation composed of three scalars representing nanocrystallization capability (-Zp), growth capability (Zrea), and external input potential (-Zops). These interpretable scalars not only advance the rational design of nanocrystal synthesis but also establish a generalizable paradigm for deciphering chemical reaction mechanisms through white-box machine learning.

Physical entropy-driven true random number generators are essential for emerging probabilistic computing paradigms, but conventional implementations based on magnetic tunneling junctions have reached their performance plateaus limited by inherent tradeoffs and weak tunability. Here, autferroics, a sister branch of multiferroics, is proposed to construct true random number generators. Benefiting from its unique energy landscape due to strong seesaw-type magnetoelectricity, the performance of true random number generation can be significantly enhanced in autferroic tunneling junctions, verified by passing standard statistical tests. Furthermore, autferroics-based random number generators can exhibit multi-field tunability and intrinsic multi-state randomness, opening an avenue for efficient hardware realization of the complex-number arithmetic and simulation of probability distributions of quantum mixed states within stochastic circuits.

Transverse thermoelectric conversion using artificially tilted multilayers (ATMLs) offers a versatile device architecture that circumvents the structural limitations of conventional longitudinal thermoelectrics. However, achieving competitive room-temperature thermoelectric performance without an external magnetic field remains a critical challenge. Here, we report a record-high transverse thermoelectric figure of merit $z_{yx}T$ of 0.36 in Ni$_{50}$Fe$_{50}$/Bi$_{0.2}$Sb$_{1.8}$Te$_{3}$-based ATML at room temperature without an external magnetic field. Leveraging the longitudinal high power factor in a Ni$_{50}$Fe$_{50}$ alloy and the sharp contrast in electrical and thermal transport properties between $n$-type Ni$_{50}$Fe$_{50}$ and $p$-type Bi$_{0.2}$Sb$_{1.8}$Te$_{3}$, we engineer an anisotropic structure that simultaneously exploits high electrical conductivity, large transverse thermopower, and low thermal conductivity to maximize $z_{yx}T$ in ATML. Through the direct measurements of these thermoelectric transport parameters, we obtained $z_{yx}T$ of 0.36 in Ni$_{50}$Fe$_{50}$/Bi$_{0.2}$Sb$_{1.8}$Te$_{3}$-based ATML, which is in excellent agreement with the analytical prediction of 0.36 owing to the low interfacial electrical and thermal resistances at the Ni$_{50}$Fe$_{50}$/Bi$_{0.2}$Sb$_{1.8}$Te$_{3}$ junctions. These results pave the way for the practical implementation of transverse thermoelectric materials around room temperature.

Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predictions. Although attention mechanisms are frequently treated as inherently explainable, unregularized attention can yield unstable, fragmented, or intensity driven attribution patterns that obscure the physical origin of these relationships. Here we introduce SPEAR (Structure Property Explainability with Attention Regularization), a framework that constrains attention distributions during training to improve their stability, selectivity, and physical interpretability. SPEAR augments attention based regression with a learnable temperature that controls attention concentration and a smoothness penalty that enforces coherence across neighboring spectral positions, treating attention as a learnable explanatory object rather than a post hoc visualization. Using synthetic spectral benchmarks with known generative structure, we show that attention regularization produces smooth, contiguous attribution profiles aligned with causal features while preserving predictive accuracy. Applied to experimental X ray diffraction data from a combinatorial rare earth zirconate thin film library, the regularized model selectively emphasizes physically relevant diffraction features and decouples feature importance from raw peak intensity. The reflection it identified prompted a reassessment of our earlier structural analysis, revealing a correlation between the 220 peak position, the tetragonal distortion that accommodates cation size disorder, and the local thermal conductivity. Attention regularization therefore provides a principled training constraint for explainable structure property regression, yielding mechanistically meaningful explanations without sacrificing predictive performance.

Self-organized spatial patterns are central to nonequilibrium physics and cell biology, yet locating instabilities in multi-component, reaction-diffusion networks remains challenging because standard eigenvalue analyses scale with the number of biochemical states and rely on reaction kinetics often poorly constrained by experiments. Exploiting the common mass-conserving structure of protein reaction kinetics and the fact that nonlinear feedback is typically confined to membrane reactions while lateral membrane diffusion is negligible, we develop a geometric classification that predicts stationary, and approximately also oscillatory, pattern-forming instabilities from reactive equilibria. The developed criteria reduce the stability analysis from the full component space to the space of conserved species. On this reduced space, slope matrices—describing the change of equilibrium cytosolic densities with respect to total species densities—govern onset. Based on densities in chemical equilibrium, this approach eliminates the requirement of comprehensive kinetic knowledge frequently lacking in experimental systems. Thus, a broad range of instabilities can be understood as mass-redistribution instabilities—self-amplifying mass redistribution caused by shifting local equilibria. We apply these criteria to models for the Escherichia coli Min system and the Caenorhabditis elegans polarity system and show that the reduction extends to the mixed-dimensional dynamics in systems coupling bulk cytosolic dynamics with membrane dynamics on the boundary. Together, these results provide an interpretable, broadly applicable, and experimentally accessible framework for diagnosing and designing pattern formation in multicomponent nonequilibrium systems on the basis of conservation laws.

Electrons in two-dimensional electron gases in the presence of an out-of-plane magnetic field propagate along the edge with a high velocity of the order of the Fermi velocity. Under microwave and terahertz radiation, photon absorption by these electrons provides a pathway to realising sensitive radiation detection. Here, we develop a detailed quantum theory of the photocurrent generated in such a system by an incident electromagnetic wave and propose an experimental geometry for observing the predicted phenomenon. By using suitably arranged radiation confinement structures, a strong photocurrent generation efficiency can be obtained. We also demonstrate that the resulting photoresponse in III-V semiconductor structures can be orders of magnitude higher than that measured in graphene.

We report a systematic study of the correlations among electrochemical processing conditions and the morphological, chemical, and optical properties of porous silicon (pSi). Twenty pSi samples were fabricated from boron-doped (100) crystalline silicon by varying current density, etching time, and hydrofluoric-acid concentration. Scanning electron microscopy, Fourier-transform infrared spectroscopy, and photoluminescence were used to characterize the resulting structures. The two-dimensional porosity varied only moderately, whereas porous-layer thickness increased with etching time and current density. The silicon oxide to silicon hydride ratio showed comparatively small variations. Corrected photoluminescence spectra were fitted with a four-component quantum-wire/quantum-dot model including localized surface-state transitions. The extracted quantum-wire and quantum-dot dimensions decreased systematically with increasing current density, while the photoluminescence maximum under 375 nm excitation showed no clear monotonic dependence on processing conditions. The results demonstrate that no single descriptor adequately captures the multiscale behavior of porous silicon.

Inelastic neutron scattering on a single crystal resolves how the intensity of a magnetic transition of a molecular spin cluster varies with the momentum-transfer vector $\mathbf{Q}$. The point group fixes that dependence completely when a single symmetry species mediates the transition and the local spin operators contain only one occurrence of that species. Functions for such universal $\mathbf{Q}$ dependences have been tabulated. As we show here, even when these conditions are not fulfilled, the point group still fixes, at a given geometry, momentum transfers at which the intensity vanishes for every Hamiltonian that has the symmetry of the cluster. We call such momentum transfers extinctions and derive an equation whose every zero is an extinction, built from the symmetry species of the two levels and from the positions and scattering amplitudes of the magnetic sites. The extinctions follow in closed form in two cases: when each orbit of symmetry-equivalent sites contributes a single occurrence of the mediating species, and when the site phases recur under a subgroup up to one common factor.

Kirigami, the art of cutting sheets, offers unique characteristics such as shape morphing, stretchability, and adaptability, with applications in deployable and reconfigurable structures. While Finite Element (FE) approaches are widely used to analyze kirigami structures, they are computationally intensive and prone to convergence issues. On the other hand, existing simplified and theoretical models for evaluating kirigami are typically restricted to specific designs and analytical scenarios. This paper proposes a generalized, computationally efficient reduced-order model based on the bar and hinge approach for analyzing any kirigami system. The model represents the entire kirigami structure using truss bars, bending hinges, and torsional springs, capturing both the deformation and internal forces of the system. We derive stiffness expressions for kirigami structures that experience stretching and out-of-plane bending deformations, as well as for structures that experience only in-plane stretching and rotations. The accuracy and robustness of the model are validated through comparisons with FE analysis and experimental data on both single-cut kirigami designs and more complex designs with multiple cuts. Our results demonstrate less than 5% difference in deformation predictions and achieve at least a tenfold computational speedup compared to FE simulations. Additionally, we demonstrate the model's capabilities through simulating complex scenarios, including kirigami-skinned crawlers, high-throughput property-space exploration, and in-plane kirigami-inspired metamaterials. These examples demonstrate computational capabilities that enable large-deformation structural simulations and high-throughput design exploration beyond the practical limits of conventional FE methods.

Drying particle suspensions is widely used to assemble particles and to fabricate porous functional layers for various applications, in which microstructural properties critically influence the overall device performance. Understanding the mechanisms governing drying-induced microstructure formation is therefore essential for predictive control of the resulting structures. In this work, we numerically investigate the evolution of microstructures during the drying of particle suspension films, with a particular focus on the role of particle-particle interactions. For weakly interacting particles, the particles assemble into hexagonal structures at the interface, and upon drying, trigger subsequent layer-by-layer assembly. We present a simple theoretical model to predict the time evolution of the layer thickness, validated against our simulation results. With strong particle interactions, the particles aggregate and form a network-like structure during drying, leading to a porous deposit. The porosity of the structure follows a power-law relationship with a dimensionless adhesion parameter that characterizes the particle-particle interaction force relative to the capillary force. By systematically varying the adhesion parameter, three packing regimes of the final structure are identified: hexagonal close packing, random close packing, and adhesive packing. Overall, our results demonstrate that particle-particle interactions play a decisive role in determining the final porous structure, providing practical guidance for tailoring functional layers through controlled manipulation of particle interactions.

The dependence of ion pairing on osmotic stress may strongly affect the performance of ionic materials and membranes whose interior is often osmotically stresses, yet quantitative understanding of this dependence and, specifically, the effects of water and ion activities is limited. Motivated by this gap, we analyze the enhancement of ion pairing with osmotic pressure for concentrated aqueous KCl, NaCl, and LiCl solutions using molecular dynamics simulations. Based on rigorous thermodynamic relations, we separate the contributions of ion non-ideality to the pairing constant, varying with osmotic pressure, from other effects including water release and type of ion-pair. Our analysis reveals that ion non-ideality indirectly generates a stronger effect on pairing than the direct one of water release. However, its effect is moderated and may even be reversed for more hydrated pairs by a similarly large and opposite effect of ion-pair non-ideality assigned to varying dielectric properties of the solution and water restructuring upon pairing. The interplay between these contributions, including large and pair type-specific hydration effects on the cost of pairing, explains the observed opposing trends: pairing decreases with osmotic pressure for more hydrated solvent-separated pair types while increasing for contact pairs. The trend becomes more pronounced for more hydrated smaller cations, but was fairly independent of the water model used. The results further suggest that dielectric effects enhanced in ionic materials-and, as a result, larger variations of ion-pair non-ideality, compared with aqueous solutions, should have a more significant impact on pairing than water release.

We investigate holographic superconductors using modern optimisation techniques inspired by machine learning. The critical temperature is obtained by minimising the variational functional for the eigenvalue $\lambda^2$ with two complementary trial functions: a simple cosine ansatz $F(z)=\cos(a z)$ and a flexible exponential polynomial $F(z)=\exp(\sum_{n=2}^{N} a_n z^n)$, both of which automatically satisfy the standard boundary conditions. For the cosine ansatz, we perform a one-parameter minimisation and obtain $\lambda^2(\Delta)$ and $T_c/\sqrt{\rho}$ over a wide range of $\Delta$, including the exact values at $\Delta=1$ and $\Delta=2$ to high accuracy. The exponential polynomial ansatz, with up to 19 coefficients, is optimised using a multi-start L-BFGS-B algorithm with warm-starting, yielding even better agreement with known exact results. Our numerical data for $\lambda^2(\Delta)$ and $T_c/\sqrt{\rho}$ match the analytical predictions from the literature, confirming the robustness of the variational approach. This work; therefore, demonstrates that a combination of analytic trial functions and modern numerical optimisation provides a powerful, flexible, and efficient tool for exploring holographic superconductors, and can be readily extended to include backreaction or other sectors in this field.

Collective cell migration emerges from the interplay between motility, deformability and mechanical interactions, yet incorporating these ingredients into computationally efficient tissue models remains challenging. Here, we benchmark self-aligning deformable active membranes in a confined-flow geometry that mimics epithelial monolayer migration around a circular obstacle. In this model, cells are represented as deformable, adhesive membranes whose self-propulsion direction relaxes towards their velocity. By systematically varying the self-alignment timescale, cell-cell adhesion and inlet forcing, we characterize the resulting flows through collective alignment, relative density, neighbor rearrangements and spatial velocity fields. The model captures a broad spectrum of tissue behaviors, ranging from disordered, liquid-like flows to highly aligned, solid-like states. Compared with a related multiparticle model, self-aligning active membranes achieve stronger collective alignment, exhibit a more systematic density response and access states closer to both limits of the solid-liquid spectrum. We further show that increasing the target shape index promotes cell elongation and accelerates tissue flow, directly linking cell-scale deformability to tissue-scale transport. Finally, we compare simulated velocity profiles with experimental measurements from in vitro migrating MDCK epithelial cell monolayers and find qualitative agreement across multiple horizontal and vertical transects around the obstacle. These results establish self-aligning deformable active membranes as a versatile framework for connecting cell mechanics, shape adaptation and self-alignment to collective tissue migration in confined geometries.

Tensile twinning plays a central role in accommodating <c>-axis plasticity in Mg. In bulk Mg, it typically shows a relatively deterministic response with a low critical stress, whereas in confined volumes it exhibits broad yield-stress distributions that complicate the prediction of small-scale mechanical behavior. Here, site-specific compression tests are performed on 4 $\mu$m-diameter pillars fabricated in a parent Mg crystal and an adjacent {10-12} twin. The two regions share the same [11-20] compression axis but experienced different prior deformation histories, allowing the influence of the residual microstructural state to be examined at fixed crystallographic orientation. Among 27 pillars, most parent-region pillars yield near 300 MPa, whereas pillars from the twin region span approximately 30 to 300 MPa. Interrupted tests combined with cross-sectional EBSD link individual load drops to discrete twin formation and further show that a pillar containing a pre-existing twin yields at approximately 80 MPa through the migration of the existing twin boundary. Molecular dynamics simulations of 30 nm-diameter pillars resolve possible atomistic pathways at the nanoscale. The simulations illustrate how contact geometry and pre-existing twin embryos alter event selection, and how an activated twin advances rapidly, while coherent twin boundary migration proceeds through disconnection motion accompanied by crystallographically required atomic shuffles. The results attribute the experimental scatter to the local availability of embryos and mobile interfaces, such that the first plastic event is governed by the twinning pathway accessible from the local microstructural state rather than by a single characteristic critical stress. Deformation history can therefore strongly modify the distribution of first plastic events even when the loading orientation is fixed.

Thermomagnetic instabilities in superconducting films can trigger flux avalanches that disrupt the critical state and impair the performance of superconducting devices. Here we investigate the stabilization of Nb thin films subjected to a perpendicular magnetic field by two complementary approaches: the addition of normal-metal overlayers and Ar ion irradiation. Magnetization measurements and magneto-optical imaging show that the metallic layers suppress the onset of avalanches at low applied fields, whereas ion irradiation reduces the high-field portion of the instability region by shifting the upper threshold for avalanche activity to lower fields. When combined in the same sample, these two partial stabilization effects lead to complete suppression of thermomagnetic instabilities. In particular, a Nb film coated with a 1~\(\mu\)m-thick Cu layer after irradiation at a fluence of \(5\times10^{16}\)~ions/cm\(^2\) exhibits smooth magnetization curves and avalanche-free flux penetration. This combined treatment restores stable critical-state behavior and provides a practical route for stabilizing Nb superconducting films in thin-film superconducting technologies.

Secondary-electron (SE) emission in the helium ion microscope (HIM) becomes sensitive to membrane thickness when the sample is thin enough for He-ion transmission and when the SEs emitted from the bottom surface are collected. We correlated the total SE intensity of suspended, nanometer-thick MoS$_2$ flakes on lacey carbon with thickness measured independently by electron energy-loss spectroscopy. The response peaks at 40-55 nm, with an apparent back-to-front SE signal ratio reaching 4.7. The peaked, thickness-dependent component is attributed primarily to SE emission at the bottom surface of the flake, rather than to transmitted ions striking instrument surfaces. Applying SRIM ionization profiles, an asymmetric SE-escape model with a longer escape depth on the exit side reproduces the response. We find an effective exit-side escape depth of approximately 10 nm, five times the assumed 2 nm entrance value, suggesting that deposited energy reaches the exit surface far more efficiently than the entrance surface or that the SRIM model's energy deposition profile is shifted by an effect such as channeling. The correlation provides a rapid thickness screen for suspended membranes and a route to testing low-energy ion-solid interaction models in thin materials.

We identify a broad class of nonthermal phases generated by the interplay of continuous symmetry breaking and weak nonequilibrium driving. Extending Kardar-Parisi-Zhang (KPZ) physics beyond the single $SO(2)$ chronon associated with periodically broken time translations, we construct nonequilibrium nonlinear sigma models for $SO(2)\times O(N)$ symmetry, describing non-Abelian time crystals with coexisting temporal and internal order. This symmetry structure arises naturally in driven quantum materials, active matter, and optically induced periodic states. For rotating and oscillating phases, we derive the Goldstone theories and show that chronon-$O(N)$ couplings remain finite deep in the ordered regime. One-loop renormalization group analysis reveals a KPZ-like dimensional structure: in $d=1,2$, arbitrarily weak nonequilibrium perturbations destabilize the equilibrium fixed point and generate strongly coupled nonthermal fixed points, realizing emergent equilibrium breaking. By contrast, for $d > 2$, weak perturbations are irrelevant and effective equilibrium is restored. A central result is unconventional weak dynamic scaling in the rotating phase: strongly coupled Goldstone sectors acquire distinct universal dynamical exponents despite belonging to the same order parameter. We characterize this scaling analytically and corroborate it through direct simulations in $1+1$ dimensions. In the oscillating phase, we recover and extend weak-scaling regimes known from drifting polymers. Finally, compactness and topological defects ultimately destroy long-range order but leave experimentally accessible nonthermal scaling windows. Together, these results extend KPZ universality to non-Abelian symmetry breaking.

Open system probes can provide an efficient means to characterize quantum many-body systems by employing them as engineered environments. The key idea is to map long-range spatial correlations of the environment onto dynamical correlations in the evolution of a simple quantum probe. Using the example of a qubit coupled to a transverse-field Ising model, we show how the non-Markovian rate or spectral flow can be used to identify stable and unstable fixed points, infer scaling dimensions of relevant fields, and deduce the renormalization group flow induced by deformations around any fixed point.

We uncover a mechanism for far-from-equilibrium topological phase transitions, via a one-dimensional compact phase model evolving deterministically from random initial conditions. It rests on topology and symmetry rather than on phenomenological postulates: phase compactness permits vortices, and a homogeneous fixed point suppresses their nucleation, with the fixed point itself implied by phase-shift symmetry. The competition between vortex-induced disordering and relaxation toward homogeneity drives a continuous nonequilibrium transition, whose universality class we identify as directed percolation (DP). We demonstrate this by constructing the corresponding effective field theory and numerically confirming DP critical scaling through dynamical-scaling analysis.

Periodic driving of spatially periodic quantum systems generates band structures that are absent in static crystals. We present a quantum geometric theory to characterize the Floquet-Bloch states at stroboscopic times and during micromotion on equal footing. Our framework builds upon time-evolved Floquet projectors that connect static quantum geometry, micromotion-operator geometry, and Floquet topology. To illustrate the formalism, we introduce the Floquet-projector quantum metric, which we employ to characterize the real-space breathing of localized states in a driven chiral-symmetric integrable spin chain. The Floquet-projector quantum metric, integrated over the Brillouin zone, captures the oscillatory variance during micromotion and, at symmetry-selected times, is bounded below by Floquet topological invariants. We further describe how the Floquet projector geometry enables a systematic investigation of micromotion dynamics in periodically driven lattice systems.

Bipartite qubit entanglement distillation is the process of converting noisy ebits into pure ebits using only local operations and classical communication. This is a core operation for quantum repeaters, enabling such crucial tasks as long-distance quantum communication and distributed quantum computing. In this work, we introduce a method for searching for entanglement distillation protocols and, using this technique, distil qubit Werner states at a higher rate than could be achieved using previously discovered protocols. In particular, we demonstrate the advantage of our new distillation strategy by improving the best-known lower bound for the two-way-assisted quantum capacity of the qubit depolarising channel across a wide range of channel parameters, making progress in one of the long-standing problems of quantum information theory.

The orbital angular momentum (OAM) of light is a discrete, unbounded degree of freedom that underpins mode-multiplexed communications and high-dimensional quantum photonics. Yet, dynamic OAM control remains dependent on bulky free-space optics or cascaded architectures that separate switching from wavefront shaping, hindering nanoscale integration. Here, we engineer artificial van der Waals crystals from rhombohedrally stacked (3R) MoS$_2$, in which spatial control of the local crystal orientation imprints a nonlinear geometric phase onto the second-harmonic (SH) field, enabling background-free generation of SH vortex beams in an ultrathin (46 nm) van der Waals platform. Leveraging the C$_{3v}$ symmetry of 3R-MoS$_2$, we demonstrate monolithic, all-optical switching with sub-optical-cycle precision between Hermite-Gauss-like and Laguerre-Gaussian vortex SH beams with opposite topological charges ($l=\pm1$). Our results establish artificial 3R-MoS$_2$ crystals as a monolithic platform for the generation and all-optical reconfiguration of nonlinear structured light at the nanoscale, advancing active nanophotonic sources for integrated classical and quantum photonic technologies.