Spin-orbitronics harnesses spin-orbit coupling to generate pure spin currents for energy-efficient information processing, particularly through the spin-orbit torque (SOT) that drives magnetization switching. However, achieving efficient SOT switching of perpendicular magnetization, which is crucial for high-density applications, remains challenging due to the difficulty in generating spin currents with both high conductivity and out-of-plane polarization in conventional, industry-compatible spin sources, such as heavy metals. Here, we overcome this limitation by demonstrating a noncollinear spin-orbit filtering effect at the surface termination of conventional spin sources. This effect selectively transmits electrons based on the relative alignment between their spin vectors and the interfacial Rashba-Edelstein field. By implementing this strategy in platinum (Pt) epitaxial films with (n10) orientations (n=2–4), we achieve exceptionally strong z-polarized spin currents. The resulting out-of-plane spin Hall conductivity reaches a record value of 0.75×10^{5} (ħ/2e) Ω^{−1} m^{−1}, surpassing all previous approaches, and enables robust field-free switching of perpendicular magnetization. Notably, the field-free switching is precisely controlled by engineering the crystal point group symmetry from C_{1v} to C_{4v}. Our work establishes a general framework for transforming conventional high-symmetry materials into out-of-plane spin sources, paving the way for highly efficient and scalable spintronic memory technology.
The origin of superconductivity in magic-angle twisted bilayer graphene has been a subject of intense debate. While some experimental evidence indicated an unconventional pairing mechanism which should be sensitive to Coulomb screening, experimental attempts to tune the critical temperature by screening Coulomb interactions so far have remained unsuccessful, possibly indicating a conventional phonon-mediated pairing. Here we study a double-layer electronic system consisting of two twisted graphene bilayers in immediate proximity of each other but remaining electronically decoupled. By increasing the carrier density in one bilayer, we completely suppressed both the superconductivity and the correlated-insulator state in the adjacent magic-angle graphene. The observation of such an effect from screening offers support for an unconventional mechanism of Cooper pairing in magic-angle twisted bilayer graphene, shedding new light on the underlying physics governing their properties.
We investigate how polycrystallinity evolves in homogeneously nucleated crystals in an experimental hard-sphere-like colloidal system using three-dimensional confocal microscopy. We capture the whole process of nucleation and crystal growth and use a recently developed method to resolve grain structure and misorientation between adjacent grains and nearest-neighbor clusters. Closely following nucleation, crystals are shown to give rise to grains through the emergence of stacking faults and twinning. The birth rate of new grains from a single nucleation event are measured, observed to increase, and then plateau. We visualize the spatial structure of a nucleus which comprises a highly ordered core and a radial increase in misorientation angles. Finally, we use local misorientation to formulate a measure for positional order which reveals a sharp decoupling between the development of orientational order and positional order at times significantly after nucleation.
P-type MgAgSb with excellent room-temperature thermoelectric performance holds great promise for cooling and power generation. However, its practical application has been significantly limited by phase transitions, which are long considered a polymorphic transformation where the compositions remain identical but the crystal structures differ. Here we report the non-polymorphic nature of the complex phase transitions in Mg-Ag-Sb during thermal cycling, yielding four compositionally distinct ternary compounds that crystallize in three different structures. During heating, the tetragonal α-phase MgAgSb decomposes into the tetragonal β-phase Mg_{5}Ag_{4.32}Sb_{4.5}, and subsequently into the cubic γ_{H}-phase Mg_{5}Ag_{2.11}Sb_{4}, with the progressive precipitation of Ag_{3}Sb and Sb phases. Upon cooling, the γ_{H}-phase Mg_{5}Ag_{2.11}Sb_{4} converts to the cubic γ_{L}-phase Mg_{5}Ag_{1.18}Sb_{3.63}, accompanied by the α phase with substantial Ag_{3}Sb and Sb phases. The electron localization function and bonding analysis indicate that the Mg-Ag-Sb compound is characterized by ionic interactions, wherein the Ag-Sb bond is notably weaker compared to the Mg-Sb bond. Molecular dynamics simulations reveal that significant Mg and Ag migration within the Sb sublattices underlies the phase evolution. Surprisingly, the rapid atomic diffusion also enables the restoration of the α-phase MgAgSb upon annealing at low temperatures. Successful recovery of the α phase can be realized even after 500 thermal cycles between 423–673 K, demonstrating unprecedented thermal recoverability.
Discovering materials that combine topological phenomena with correlated electron behavior is a central pursuit in quantum materials research. Monolayer TaIrTe_{4} has recently emerged as a promising platform in this context, hosting robust quantum spin Hall insulator (QSHI) phases both within a single-particle gap and within a correlation-induced gap arising from van Hove singularities (vHSs), accessed via electrostatic doping. Its intrinsic monolayer nature offers exceptional tunability and the potential to realize a rich variety of interaction-driven topological phases. In this work, we combine theory and experiment to map the phase landscape of monolayer TaIrTe_{4}. Using Hartree-Fock calculations, we investigate the interaction-driven phase diagram near the vHSs under commensurate filling conditions. By systematically tuning the dielectric screening and strain, we uncover a rich set of ground states—including QSHI, trivial insulator, higher-order topological insulator, and metallic phase—among which are interaction-driven topological phase transitions. Experimentally, we perform local and nonlocal transport measurements across a broad set of devices. Because of unavoidable strain variations during fabrication, the devices exhibit several distinct transport regimes, whose evolution is consistent with the theoretically predicted phase diagram. Finally, band-projected exact diagonalization together with insulating transport features at fractional fillings provide preliminary signatures consistent with a time-reversal-symmetric fractional QSHI in this system. Together, our results lay the groundwork for understanding correlation-driven topological phenomena in TaIrTe_{4} and open new directions for engineering exotic quantum phases in low-dimensional materials beyond the limitations of moiré superlattices.
Robust inference for stochastic dynamical systems is often hampered by sparse sampling and the absence of closed-form likelihoods. We introduce a Monte Carlo path-inference framework that leverages full-path statistics and bridge processes to deliver reliable parameter estimation and model selection from coarsely sampled time series, without requiring analytical solutions. Crucially, we couple mechanistic stochastic models with their inference procedures to quantify how experimental design—specifically, sampling frequency and dataset size—governs estimator precision and model distinguishability. This analysis reveals optimal sampling regimes and sharp, resolution-dependent limits beyond which competing models become empirically indistinguishable. We validate the approach across four disparate systems—trajectories of optically trapped particles, human microbiome dynamics, social-media topic mentions, and forest population time series—recovering parameters and identifying when inference is fundamentally constrained by measurement resolution, thereby clarifying ongoing debates about dominant noise sources in these systems. Together, these results establish path-based Monte Carlo as a practical, general tool for inference and model discrimination in complex systems and provide principled guidelines for designing measurements that maximize information under real-world constraints.
In nonlinear transport, the quantum-geometric effects can generate higher-harmonic voltages in response to a driving current, which has defined a fast-moving field of intense interest. However, in realistic materials where disorder scattering also contributes to nonlinear transport, identifying the geometric mechanisms remains a challenge. In particular, a theoretical framework for data analysis is still lacking for nonlinear transport at any order. Here, we develop a mechanism-resolved and symmetry-guided framework for identifying mechanisms of third-order nonlinear transport in disordered materials. We find a total of 20 mechanisms of third-order nonlinear transport, by treating quantum-geometric and disorder-mediated mechanisms on an equal footing. More importantly, we propose a protocol of data analysis that combines symmetry diagnosis of magnetic point groups and scaling law of relation between the third-order nonlinear Hall conductivity and linear longitudinal conductivity. We identify characteristic fingerprints in the scaling-law weights, which allow the mechanisms to be quantitatively distinguished in experiments. We have applied the protocol to identify the geometric mechanisms in materials with and without time-reversal symmetry, including 2D materials, topological materials, and altermagnets. The theory can be generalized to arbitrary orders of nonlinear transport, further promoting nonlinear transport as a probe of geometric effects and phase transitions in quantum materials.
Heavy polar molecules are sensitive probes of physics beyond the standard model. However, uncontrolled external electromagnetic fields pose challenges to achieving precise and accurate measurements. Minimizing susceptibility to these fields is therefore critical and has played an important role in all precision experiments of this type. Here we devise and demonstrate clock transitions engineered to realize robust symmetry violation searches in the polyatomic molecule YbOH. Sensitivities to external fields can be suppressed by orders of magnitude while preserving high sensitivity to the electron electric dipole moment. We perform Ramsey measurements on these clock transitions and observe suppression of electric and magnetic sensitivities by at least factors of 700 and 200, respectively, and demonstrate the robustness of their spin coherence against large electromagnetic-field fluctuations. We further identify and employ selected quantum states to make sensitive measurements of external magnetic and electric fields, another critical feature for highly accurate measurements. This approach of molecular engineering is broadly applicable to diverse molecular species and states, including those with complex nuclei and those that are compatible with state-of-the-art cooling and trapping techniques, thereby offering the potential to significantly improve experimental sensitivity to a wide range of new physics while expanding the chemical design space for molecular quantum science.
The discovery of superconductivity in bulk bilayer nickelates under high pressure, and its subsequent stabilization in compressively strained thin films at ambient pressure, has established a new platform for exploring high-T_{c} superconductivity beyond the cuprates. Central to this development is the prominent role of the 3d_{z}^{2} orbital in shaping the low-energy electronic structure, imposing constraints on microscopic theories and fueling debate over the superconducting mechanism. Here we report a systematic in situ angle-resolved photoemission spectroscopy study of compressively strained bilayer nickelate thin films spanning Ca doping, oxygen stoichiometry and film thickness. Despite variations in oxygen-vacancy disorder and surface termination, the electronic structure remains robust and exhibits a systematic strain-driven evolution consistent with an intermediate-correlation regime. In particular, we demonstrate a Ca-doping-induced electronic structure evolution that is consistent with Fermi-level crossing of the γ band and is decoupled from the presence of superconductivity, suggesting that, while important for the γ band to be near the Fermi level, the debated γ crossing and the resulting Fermi pocket may not be a prerequisite for superconductivity. Together, our results establish key spectroscopic constraints on the minimal fermiology and correlation strength relevant to superconductivity in bilayer nickelates, providing an experimental foundation for microscopic theories of their pairing mechanism.
Solving large-scale quadratic unconstrained binary optimization (QUBO) problems is critical in various fields, including physics, finance, and engineering. However, these problems remain intractable on conventional computing architectures. Alternative solvers, such as Ising machines (IMs) based on networks of coupled electronic, mechanical, or photonic parametric oscillators (POs), have recently been developed. PO-based IMs aim to find the ground state of an Ising Hamiltonian, which encodes the solution to a QUBO problem. However, their analog nature and their energy-minimization process based on gradient descent make PO-based IMs inherently susceptible to identifying inaccurate solutions. In this work, we introduce and validate a QUBO solver—the analog Floquet solver (AFS)—which enhances the dynamics of PO-based IMs by leveraging Floquet states that emerge spontaneously in POs coupled to high-quality-factor resonances. These states enable the AFS to embed periodic time modulation into its energy-minimization process, allowing it to escape local minima during the search for QUBO problem solutions. As a result, the AFS significantly increases the likelihood of identifying accurate solutions compared to conventional PO-based IMs. More generally, this work defines a new paradigm in analog computing—spanning both classical and quantum realms—that can be physically realized with existing technologies across diverse physical domains.
Soft solids and their surface deformations control the response of many natural and artificial systems. Yet, their underlying properties are vigorously debated, particularly for polymer networks. While molecular-scale theories predict no interfacial changes with macroscopic deformation, multiple experiments suggest otherwise. To settle this issue, we measure displacement fields near the interface of a silicone gel, in the limit of small deformations. We discover an unexpected multiscale response. The shear modulus decreases smoothly by half with 20 μm of the interface. At the same time we observe a surface excess elasticity, that depends on history and outer medium composition. These results reveal the fundamentally multiscale nature of polymeric surfaces, and call for further experimental and theoretical investigations into the basic understanding of soft solid interfaces.
Synchronization is a hallmark of collective behavior that emerges when nonlinear systems interact, spanning scales from mechanical oscillators to planetary orbits. As a universal phenomenon, it underpins the study of complex systems and has far-reaching technological implications. While classical synchronization has a long and rich history, it has not been observed experimentally between multiple quantum limit-cycle oscillators despite a decade of theoretical investigations. We realize synchronization between two quantum van der Pol oscillators by engineering dissipation in a mixed-isotope trapped-ion quantum simulator. The synchronized state is encoded in a fixed relative phase between the oscillators that is inaccessible to individual measurements and revealed only through joint readout of both oscillators, in stark contrast to the system in the (deterministic) classical limit where synchronization can be observed via individual phase measurements. We further show that the relative phase can be precisely controlled and that the chain of two oscillators can synchronize to an external field, suggesting applications in sensing. Our results provide a promising pathway for studying more complex synchronized quantum dynamics beyond two oscillators, where a theoretical treatment becomes increasingly challenging, and it remains to be understood whether genuinely quantum features persist in such cases.
The relaxation dynamics of glass-forming liquids shows at a temperature T_{c}, somewhat above the glass-transition temperature T_{g}, a crossover, indicating the conjunction of two different dynamical regimes. For temperatures slightly above T_{c}, experiments and computer simulations have extensively probed this dynamics on the particle level and identified several universal scaling laws that are often compatible with the predictions of ideal mode coupling theory, while for temperatures below T_{c}, the nature of the relaxation dynamics, and hence the reason for the crossover, has so far remained elusive. Here we use large-scale computer simulations to investigate how in the low-temperature regime the relaxation mechanism differs from the one at higher temperatures. Our analysis reveals unexpected scaling laws that allow us to give a surprisingly simple description of the relaxation dynamics at very low T’s. Space-time correlation functions show that the cage-escape process involves rare but large particle displacements. These displacements give rise to distinctive subdiffusive power laws in the mean squared displacement and the intermediate scattering function, and also rationalize the origin of the universal excess wing found in the dynamic susceptibility. This insight advances our understanding on the dynamics of glass-forming systems at temperatures that are close to the experimental glass transition.
Abrupt learning, long performance plateaus followed by rapid convergence, is a common phenomenon in recurrent neural networks (RNNs) trained on working-memory tasks. In such cases, the networks develop transient slow regions in state space that extend the effective timescales of computation. However, the mechanisms driving sudden performance improvements and their causal role remain unclear, largely because we lack an analytical dynamical-systems framework. To address this gap, we introduce the ghost mechanism, a general process by which finite-dimensional continuous-time dynamical systems exhibit transient slowdown near the remnant of a saddle-node bifurcation. By reducing the high-dimensional dynamics near ghost points, we derive a one-dimensional canonical form that analytically captures learning as a process controlled by a single scale parameter. Using this model, we study a form of abrupt learning emerging from ghost points and identify a critical learning rate that scales as an inverse power law with the timescale of the learned computation. Beyond this rate, learning collapses through two interacting modes: (i) vanishing gradients and (ii) oscillatory gradients near minima. These features can lock the system into high confidence but incorrect predictions when parameter updates trigger a no-learning zone, a region of parameter space where gradients vanish. We validate these predictions in low-rank RNNs, where ghost points precede abrupt transitions and further demonstrate their generality in full-rank RNNs trained on canonical working-memory tasks. Our theory offers two approaches to address these learning difficulties: Increasing trainable ranks stabilizes learning trajectories, while reducing output confidence mitigates entrapment in no-learning zones. Overall, the ghost mechanism reveals how the computational demands of a task constrain the optimization landscape, demonstrating that well-known learning difficulties in RNNs partly arise from the dynamical systems they must learn to implement.
Nonequilibrium dynamics of quantum many-body systems is challenging for classical computing, providing opportunities for demonstrating practical quantum computational advantage with analog quantum simulators. Owing to the intimate connection with a random matrix ensemble, it is proposed to be classically intractable to sample the driven thermalized many-body states of a Bose-Hubbard system and further extract multipoint correlations from the output strings for characterizing quantum systems. Here, leveraging dedicated precise manipulations and atom-number-resolved detection through a quantum gas microscope with bichromatic superlattices, we perform sampling of the driven Hubbard chains and two-leg ladders in the thermalized phase involving up to 64 sites with 20 atoms, yielding a Hilbert space dimension of 10^{19} and outpacing the most powerful supercomputer in terms of sampling rate by 3 orders of magnitude. The volume law scaling of the Rényi entanglement entropy in the thermalized phase is observed, which hinders efficient classical simulation for large systems. We employ the Bayesian tests to verify that our prepared systems operate in the driven thermalized phase. Multipoint correlations of up to 14th-order extracted from the experimental samples offer clear distinctions between the thermalized and many-body-localized phases, where classical computations such as tensor network fail to give accurate and faithful predictions within a reasonable time cost. Our work demonstrates the sampling of an interacting chaotic system performed on a quantum processor of ultracold atoms and opens the door of utilizable quantum computational advantage in simulating Floquet dynamics of many-body systems.
Recently, a host of exciting magnetic textures such as topologically protected skyrmion lattices has been discovered in several bulk metallic lanthanide compounds. In addition to hosting skyrmion phases, a hallmark of this class of materials is the appearance of numerous spin textures characterized by superposition of multiple magnetic modulations: spin moire superlattices. In order to understand the multitude of complex phases often present in these materials, we require a general-purpose experimental and theoretical framework. Here, we demonstrate such an approach in EuAg4Sb2 by comprehensively characterizing and modeling its three complex zero-field magnetic textures. Systematic symmetry-breaking experiments using uniaxial strain determine that the ground-state incommensurate magnetic phase (ICM1) is single q, meaning the magnetic moments modulate along one magnetic propagation vector. In contrast, ICM2 and ICM3 are both double q, meaning they are formed from the superposition of two sinusoidal spin modulations, i.e., spin moire superlattices. Further, through application of polarized small-angle neutron scattering and spherical neutron polarimetry, we demonstrate that ICM1 is a single-q cycloid and ICM2 and ICM3 are double-q vortex lattices. Despite the quasi-two-dimensional nature of EuAg4Sb2, the modulations propagate out of the ab plane, leading to a shift of the spin texture between triangular lattice planes. Further, the ICM3 to ICM2 transition includes an unusual 45 degrees rotation of the magnetic vortex lattice. Motivated by the coexistence of such drastically different phases in this compound, we conclude by developing a phenomenological model that sheds light on the energetic origins of these varied phases. Our experimental probes and theoretical modeling definitively characterize three different and tunable phases in one material and provide insight for the design of topological spin-texture materials.
Collision-resistant hash functions are a fundamental cryptographic primitive that rely on the computational hardness of finding two inputs that produce the same output. Motivated by this problem, we study the complexity of finding collisions in a family of neural networks with oscillating activation functions. A neural network trained on a classification task is specified by a set of weights assigning a label to each data point, and a collision is defined as two distinct weight configurations that produce the same labeling. We show that, within this class of neural networks, the space of collisions exhibits an overlap gap property, whereby certain overlap values between distinct solutions are forbidden. This property is a geometric feature of the solution landscape in high-dimensional random constraint satisfaction problems that has recently emerged as a powerful indicator of algorithmic barriers. Our analysis predicts a regime in which efficient algorithms fail to find collisions. This prediction is supported by numerical experiments using approximate message passing algorithms, which cease to return collisions well below the threshold predicted by theory. Neural networks, therefore, provide a class of candidate collision-resistant functions that, for suitable parameter choices, depart from existing constructions based on lattices. Beyond their cryptographic relevance, our results reveal forms of computational hardness in large neural networks that may be of independent interest.
