Papers

Filamentous bacteriophages fd are viral particles, highly monodisperse in size, that have been widely used as a model colloidal system for studying the self-assembly of rod-shaped particles as well as a versatile template in nanoscience. In aqueous suspensions, fd viruses exhibit lyotropic behavior, forming liquid crystalline phases as their concentration increases. Here, we report a solvent-free system displaying thermotropic phase behavior, achieved through covalent coupling of low molecular weight PEG-based polymer surfactant onto the fd virus surface. Upon lyophilization of aqueous suspensions of these polymer-grafted bacteriophages and subsequent thermal annealing, a solvent-free material is obtained, exhibiting both viscoelasticity and, notably, thermotropic liquid crystalline properties. A combination of small-angle X-ray scattering and optical microscopy experiments reveals the formation of an ordered hexagonal mesophase below 30 {\deg}C, which undergoes a melting transition into an isotropic liquid at higher temperatures. Our results demonstrate an efficient approach for converting lyotropic into thermotropic phase behavior in the columnar liquid crystalline phase of filamentous fd colloids. This approach paves the way for extending such functionalization to other technologically relevant rod-like systems, such as carbon nanotubes and cellulose nanocrystals, enabling the introduction of thermotropic properties in anhydrous colloidal materials.

The anomalous Hall effect has been understood in terms of the geometric nature of Bloch bands and impurity scattering, and has been observed in a wide variety of magnetic materials such as ferromagnets and antiferromagnets. Recently, a large anomalous Hall effect was reported in the noncollinear antiferromagnetic metal NbMnP whose magnetic order is a mixture of the even-parity and the odd-parity magnetic components. Such a magnetic structure is expected to exhibit the anomalous Hall effect and the nonlinear Hall effect from the symmetry breaking of the antiferromagnet ordering. Here, we theoretically investigate the intrinsic anomalous and nonlinear Hall effect of NbMnP induced by the quantum geometry of Bloch band using the first-principles calculation and the Wannier interpolation method. We found that the intrinsic Hall response of NbMnP is predominantly governed by the strongly enhanced Berry curvature and Berry-connection-polarization dipole on a specific mirror plane. These enhanced geometric quantities originate from the spin-orbit-coupling-induced gap openings along the nodal lines. Our results indicate that NbMnP serves as a model system for investigating transport phenomena originating from nodal-lines in parity-mixed antiferromagnets.

Ion transport underlies the operation of biological ion channels and governs the performance of electrochemical energy-storage devices. A long-standing anomaly is that smaller alkali metal ions, such as Li$^+$, migrate more slowly in water than larger ions, in apparent violation of the Stokes-Einstein relation. This breakdown is conventionally attributed to dielectric friction, a collective drag force arising from electrostatic interactions between a drifting ion and its surrounding solvent. Here, combining nanopore transport measurements over electric fields spanning several orders of magnitude with molecular dynamics simulations, we show that the time-averaged electrostatic force on a migrating ion is not a drag force but a net driving force. By contrasting charged ions with neutral particles, we reveal that ionic charge introduces additional Lorentzian peaks in the frequency-dependent friction coefficient. These peaks originate predominantly from short-range Lennard-Jones (LJ) interactions within the first hydration layer and represent additional channels for energy dissipation, strongest for Li$^+$ and progressively weaker for Na$^+$ and K$^+$. Our results demonstrate that electrostatic interactions primarily act to tighten the local hydration structure, thereby amplifying short-range LJ interactions rather than directly opposing ion motion. This microscopic mechanism provides a unified physical explanation for the breakdown of the Stokes-Einstein relation in aqueous ion transport.

We establish a correspondence between the continuum descriptions of polymer solutions and active nematics, two seemingly disparate but well-studied complex fluids. Through a mapping of the governing equations, we demonstrate that polymeric fluids can be interpreted as deformable, contractile, high-temperature active nematics, with an effective activity controlled by polymer extension. Using numerical simulations under Kolmogorov flow, we construct a phase diagram connecting the nematic and polymer limits, highlighting how their one- and two-dimensional instabilities depend on deformability. This encompasses both a flow-suppressed regime, in which active stresses oppose the imposed flow, and a transverse instability that generates secondary flows and leads towards elastic turbulence. The transverse instability is facilitated by spatial gradients in activity, which arise naturally in extensible polymers through variations in chain extension. We further show that the transition into the well-known traveling arrowhead structures in elastic turbulence is marked by the emergence of charge $\pm 1/2$ topological defects in the polymer director field, indicating emergent nematic ordering of stretched polymers. Our results establish a unified framework connecting polymer and active matter hydrodynamics, with implications for low-Reynolds-number turbulence, active polymer systems, and the mechanics of deformable living matter.

Bell or Clauser-Horne-Shimony-Holt (CHSH) tests on superconducting quantum processors are commonly interpreted under the assumption that repeated circuit executions sample a single, stationary preparation ensemble. Here we show that this assumption can be violated on contemporary hardware, with direct implications for the interpretation of observed Bell violations. We introduce an ensemble-divergence framework in which slow temporal drift of the preparation process induces context-dependent effective ensembles, even when measurement independence and locality are preserved. This leads to a relaxed Bell bound $|S| \le 2 + 6\delta_{\mathrm{ens}}$, where $\delta_{\mathrm{ens}}$ quantifies preparation nonstationarity. Because $\delta_{\mathrm{ens}}$ is not directly observable, we develop an operational witness $\delta_{\mathrm{op}}$ based on bin-resolved outcome statistics for fixed measurement channels. Using Pauli-axis measurements on IBM superconducting processors, we observe statistically significant operational drift that persists after full two-qubit readout mitigation, ruling out measurement artifacts. In contrast, drift extracted from CHSH-optimal measurements is eliminated by mitigation, demonstrating that such settings are unsuitable for diagnosing preparation nonstationarity. We further show that the observed Bell violations imply only modest ensemble divergences, comparable in scale to those required in Hall-type measurement-dependence models, but arising here solely from preparation drift combined with experimental scheduling. Our results identify a preparation-dependent loophole relevant to Bell tests on noisy intermediate-scale quantum devices and highlight the necessity of drift-aware protocols for reliable quantum certification.

The family of transition metal dichalcogenides (TMDCs) has been regarded as promising candidates for future electronics, valleytronics, spintronics, and optoelectronics. While most of TMDCs are intrinsic n-type semiconductors due to electron donation from chalcogen vacancies, realizing intrinsic p-type TMDCs and achieving precise control over their electronic properties remain challenging. In this work, we introduce a powerful approach to obtain intrinsic hole doping by substituting Ta atom into 2H-WSe2. A combining study of molecular beam epitaxy growth and in-situ angle-resolved photoemission spectroscopy characterization clearly reveals that Ta substitution induces a significant hole doping and provides a possible way of a semiconductor-to-metal transition in 2H-WSe2.

Topology provides a unifying framework for understanding robust transport through protected edge states arising from nontrivial wavenumber topology. Extending these concepts to active matter, however, remains largely unexplored experimentally, with realizations limited to systems composed of chiral active particles. Here, we realize topological edge states in dense bacterial suspension, which represents a prototypical active matter system, using microfabricated geometrical structures with nontrivial wavenumber topology. Inspired by previous theoretical studies, we constructed a directional kagome network composed of ratchet-shaped channels that induce unidirectional bacterial flow. In this network, we found clear edge localization of bacterial density. A steady-state analysis based on the bacterial transport model and experimentally measured velocity field reveals how the characteristic collective flow generates edge localization. The model also uncovers the topological origin of the observed edge states. By tuning the geometry of the microfabricated networks, we identified directional channel design and network chirality as the key design features essential for the emergence of the edge state. Our results pave the way for establishing a control and design principle of topological transport in such active matter systems.

Kagome magnets exhibit a range of novel and nontrivial topological properties due to the strong interplay between topology and magnetism, which also extends to their thermoelectric applications. Recent advances in the study of magnetic topological materials have highlighted their intriguing anomalous Hall and thermoelectric effects, arising primarily from large intrinsic Berry curvature. Here, we report observation of a large room-temperature (RT) anomalous Nernst effects (ANE) of S_xy^A ~ 1.3 {\mu}V K^(-1) in the kagome antiferromagnet (AFM) ErMn6Sn6, which is comparable to the largest signals observed in known magnetic materials. Surprisingly, we further found that a significant topological Nernst signal at RT and peaking a maximum of approximately 0.2 {\mu}V K^(-1) at 180 K, exactly coupling with ANE in the spiral AFM state, originates from the real-space nonzero spin chirality caused by incommensurate spin structure. This study demonstrates a potential room-temperature thermoelectric application platform based on Nernst effect, and provides insights for discovering significant anomalous and topological transverse transport effects in the incommensurate AFM system.

The microscopic origin of soft magnetic properties in amorphous alloys is fundamentally linked to the interplay between local topological disorder and magnetic exchange interactions. In this work, we employ a multiscale Spin-Lattice Dynamics (SLD) approach to investigate the magnetostructural correlations in amorphous Co$_{x}$Fe$_{1-x}$ nanodisks ($x=35, 50, 65$). By integrating classical molecular dynamics with a generalized magnetic Hamiltonian, we capture the dynamic feedback loop between lattice vibrations and spin precession. Topological analysis via Voronoi tessellation reveals a persistent species-dependent structural heterogeneity: Cobalt atoms preferentially adopt "solid-like" icosahedral packing, forming a rigid structural backbone, whereas Iron atoms exhibit a higher propensity for "liquid-like" disordered environments. We demonstrate that this topological disparity dictates the macroscopic magnetic response. The Cobalt-driven structural stiffness preserves a robust exchange network that maximizes saturation magnetization, while the local disorder inherent to Iron-rich regions introduces exchange fluctuations that act as an intrinsic damping mechanism, delaying magnetic relaxation. These findings provide an atomistic explanation for the stability of ferromagnetic order in Co-Fe metallic glasses and offer a pathway for tuning damping parameters in amorphous spintronic devices through stoichiometric control.

The Fe-Mn-based alloys are receiving immense attention due to their applications in the third generation of advanced high-strength steels, owing to their high strength and ductility. A detailed in situ high-pressure structural phase transformation and microstructural evolution in nanograined $\text{Fe}\text{-}7\%\text{Mn}$ alloy has been performed using the axial synchrotron X-ray diffraction technique. The ambient BCC phase of $\text{Fe}\text{-}7\%\text{Mn}$ undergoes pressure-driven structural PT to the HCP phase at 11.4 GPa. Both BCC and HCP phases coexist up to 15.9 GPa; thereafter, they transform into a pure HCP phase, which remains stable up to the maximum pressure of 30.3 GPa. The XRD study reveals that the $(110)_{\mathrm{b}}$ dense crystallographic plane of the BCC lattice transforms into a densely packed $(002)_{\mathrm{h}}$ peak of the HCP lattice following the orientational relationship $(110)_{\mathrm{b}} \parallel (0001)_{\mathrm{h}}$ via diffusionless $ \mathrm{Burger's} $ martensitic crystallographic PT pathway. The evolution of crystallite size and microstrain with pressure shows a distinct change during the structural PT. The microstrain exhibits a sharp anomaly at around 10 GPa, suggesting that the microstructural changes precede the structural PT.

Field-angle-resolved specific-heat measurements were performed on a clean single crystal of a spin-triplet superconductor UTe$_2$ with $T_{\rm c}=2.1$ K and a low residual electronic specific heat. At low temperatures, the specific heat exhibits a linear dependence on the magnetic field when the field is applied precisely along the $b$ axis, in stark contrast to its rapid increase at low fields for other orientations. This pronounced anisotropy suggests the presence of nodal quasiparticle excitations with the Fermi velocity predominantly aligned along the $b$ axis. Considering the characteristic field-angle dependences of both the specific heat and the upper critical field, these observations are broadly compatible with theoretical models that assume a superconducting gap structure featuring either point nodes consistent with $B_{\rm 2u}$ symmetry, allowed in the infinitely strong spin-orbit coupling scheme, or line nodes confined to flat regions of the quasi-two-dimensional Fermi surface, consistent with $^3B_{\rm 3u}$ symmetry in the finite spin-orbit classification scheme. These results yield crucial hints for resolving the pairing symmetry of UTe$_2$, paving the way for a deeper understanding of its spin-triplet superconductivity.

Emerging theoretical frameworks suggest that physical properties of matter can be altered within an optical cavity by harnessing quantum vacuum electromagnetic fluctuations, even in the total absence of external driving fields. Among the most intriguing predictions is the potential to noninvasively manipulate superconductivity. Here, we experimentally observe modified superconductivity in niobium nitride (NbN) thin films within high-quality-factor ($Q$) terahertz cavities. Using terahertz time-domain spectroscopy, we characterize the NbN response both in free space and within a high-$Q$ photonic-crystal cavity. Our analysis reveals significant cavity-induced modifications to the optical conductivity. A theoretical model indicates that these changes originate from a substantial ($\sim13\,\%$) reduction in the superfluid density and a minor ($\sim2\,\%$) reduction in the superconducting gap, driven by cavity vacuum fluctuations. These results demonstrate a platform for engineering ground states via vacuum–matter coupling, opening frontiers in cavity materials science.

Autonomous experimentation holds the potential to accelerate materials development by combining artificial intelligence (AI) with modular robotic platforms to explore extensive combinatorial chemical and processing spaces. Such self-driving laboratories can not only increase the throughput of repetitive experiments, but also incorporate human domain expertise to drive the search towards user-defined objectives, including improved materials performance metrics. We present an autonomous materials synthesis extension to SARA, the Scientific Autonomous Reasoning Agent, utilizing phase information provided by an automated probabilistic phase labeling algorithm to expedite the search for targeted phase regions. By incorporating human input into an expanded SARA-H (SARA with human-in-the-loop) framework, we enhance the efficiency of the underlying reasoning process. Using synthetic benchmarks, we demonstrate the efficiency of our AI implementation and show that the human input can contribute to significant improvement in sampling efficiency. We conduct experimental active learning campaigns using robotic processing of thin-film samples of several oxide material systems, including Bi$_2$O$_3$, SnO$_x$, and Bi-Ti-O, using lateral-gradient laser spike annealing to synthesize and kinetically trap metastable phases. We showcase the utility of human-in-the-loop autonomous experimentation for the Bi-Ti-O system, where we identify extensive processing domains that stabilize $\delta$-Bi$_2$O$_3$ and Bi$_2$Ti$_2$O$_7$, explore dwell-dependent ternary oxide phase behavior, and provide evidence confirming predictions that cationic substitutional doping of TiO$_2$ with Bi inhibits the unfavorable transformation of the metastable anatase to the ground-state rutile phase. The autonomous methods we have developed enable the discovery of new materials and new understanding of materials synthesis and properties.

We demonstrate a dissipative protocol for ground-state preparation of a quantum spin chain on a trapped-ion quantum computer. As a first step, we derive a Kraus representation of a dissipation channel for the protocol recently proposed by Ding et al. [Phys. Rev. Res. 6, 033147 (2024)] that still holds for arbitrary temporal discretization steps, extending the analysis beyond the Lindblad dynamics regime. The protocol guarantees that the fidelity with the ground state monotonically increases (or remains unchanged) under repeated applications of the channel to an arbitrary initial state, provided that the ground state is the unique steady state of the dissipation channel. Using this framework, we implement dissipative ground-state preparation of a transverse-field Ising chain for up to 19 spins on the trapped-ion quantum computer Reimei provided by Quantinuum. Despite the presence of hardware noise, the dynamics consistently converges to a low-energy state far away from the maximally mixed state even when the corresponding quantum circuits contain as many as 4110 entangling gates, demonstrating the intrinsic robustness of the protocol. By applying zero-noise extrapolation, the resulting energy expectation values are systematically improved to agree with noiseless simulations within statistical uncertainties.

Tensor network states are an indispensable tool for the simulation of strongly correlated quantum many-body systems. In recent years, tree tensor network states (TTNS) have been successfully used for two-dimensional systems and to benchmark quantum simulation approaches for condensed matter, nuclear, and particle physics. In comparison to the more traditional approach based on matrix product states (MPS), the graph distance of physical degrees of freedom can be drastically reduced in TTNS. Surprisingly, it turns out that, for large systems in $D>1$ spatial dimensions, MPS simulations of low-energy states are nevertheless more efficient than TTNS simulations. With a focus on $D=2$ and 3, the scaling of computational costs for different boundary conditions is determined under the assumption that the system obeys an entanglement (log-)area law, implying that bond dimensions scale exponentially in the interface area of the associated subsystems.

We study the Brownian motion of a rigid rod threading through a small fixed ring while the ring can freely rotate. We derive the distribution function for the sliding displacement and the unit vector along the rod both at equilibrium and non-equilibrium. The equilibrium distribution is quadratic in the sliding displacement and is controlled by the moment of inertia (mass distribution). Applying the Onsager variational principle, we derive a Smoluchowski equation in which sliding and rotational diffusion are coupled. The mean square displacement (MSD) of sliding shows a metastable plateau in a certain time range before it approaches the final equilibrium value. The longest sliding relaxation time scales as $\alpha^{-1/2}$, where $\alpha$ is the dimensionless moment of inertia of the rod. The rotational relaxation time obtained from the orientational correlation function is longer than that of a rod with its center fixed but faster than a rod with one end fixed. These results may be useful in understanding the dynamics of polymers connected by sliding rings.

Energy-efficient fast switching of spin orientations or textures is a core issue of spintronics, which is highly demanded but remains challenging. Different from the mainstream routes based on spin-transfer torque or spin-orbit torque, here we propose another mechanism coined as magnetoelectric torque to switch the magnetization in polar magnetic bilayers via pure electric field. In some magnetic van der Waals bilayers, when the electrostatic energy of polarization can compensate the interlayer magnetic coupling, a magnetoelectric torque is generated to fastly flip spins within a few picoseconds, which is demonstrated by combining the first-principles calculations, analytic model, as well as atomistic simulations. Such a magnetoelectric torque doesn't rely on the spin-orbit coupling and is generally active in polar magnetic homostructures and heterostructures. Our work provides an alternative route to switch magnetization in nanoscale, which may benefit the energy-saving and fast-response spintronic devices.

The application of generative models in crystal structure prediction (CSP) has gained significant attention. Conditional generation–particularly the generation of crystal structures with specified stability or other physical properties has been actively researched for material discovery purposes. Meanwhile, the generative models capable of symmetry-aware generation are also under active development, because space group symmetry has a strong relationship with the physical properties of materials. In this study, we demonstrate that the symmetry control in the previous conditional crystal generation model may not be sufficiently effective when space group constraints are applied as a condition. To address this problem, we propose the WyckoffDiff-Adaptor, which embeds conditional generation within a WyckoffDiff architecture that effectively diffuses Wyckoff positions to achieve precise symmetry control. We successfully generated formation energy phase diagrams while specifying stable structures of particular combination of elements, such as Li–O and Ti–O systems, while simultaneously preserving the symmetry of the input conditions. The proposed method with symmetry-aware conditional generation demonstrates promising results as an effective approach to achieving the discovery of novel materials with targeted physical properties.

When a liquid droplet completely wets a hard substrate, its spreading dynamics follow Tanner's law, with the droplet radius growing as the one-tenth power of time. Here, we investigate how these dynamics change when silicone oil droplets spread on soft silicone elastomer and gel films supported by a rigid silicon substrate. While the droplets fully wet the elastomer surface, they also simultaneously swell the elastomer film. By varying the film thickness, we observe deviations from the classical power-law scaling, which we interpret in terms of changes to the effective stiffness and the absorption potential of the system. We describe the spreading behavior using a phenomenological model that accounts for both absorption and mechanical contributions.

Discrete charge fluctuations, routinely observed in semiconductor quantum dot devices, may contribute significantly to device drift and errors resulting from qubit miscalibration. Understanding the nature and origins of these discrete charge fluctuations may provide insights into material improvements or means of mitigating charge noise in semiconductor quantum dot devices. In this work, we measure multi-level charge fluctuations present in a Si/SiGe double quantum dot device over a range of device operating voltages and temperatures. To characterize the parameter-dependent dynamics of the underlying fluctuating degrees of freedom, we perform a detailed analysis of the measured noise timeseries. We perform algorithmically assisted drift detection and change point detection to detrend the data and remove a slow fluctuator component, as a preprocessing step. We perform model comparison on the post-processed time series between different $n$-level fluctuator ($n$LF) factorial hidden Markov models (FHMMs), finding that although at most sweep values the independent pair of 2LFs model would be preferred, in a particular region of voltage space the 4LF model outperforms the other models, indicating a conditional rate dependence between the two fluctuators. By tracking fluctuator transition rates, biases, and weights over a range of different device configurations, we estimate gate voltage and conductivity sensitivity. In particular, we fit a phenomenological, detailed balance model to the extracted independent 2LFs rate data, yielding lever arm estimates in the range of $-2 \mu$eV/mV up to $4 \mu$eV/mV between the two 2LFs and nearby gate electrodes. We expect that these characterization results may aid in subsequent spatial triangulation of the charge fluctuators.

Diffusion is a fundamental physical phenomenon with critical applications in fields such as metallurgy, cell biology, and population dynamics. While standard diffusion is well-understood, anomalous diffusion often requires complex non-local models. This paper investigates a nonlinear diffusion equation where the diffusion coefficient is linearly dependent on concentration. We demonstrate through a perturbative analysis that this physically-grounded model exhibits transient anomalous diffusion. The system displays a clear crossover from an initial subdiffusive regime to standard Fickian behavior at long times. This result establishes an important mechanism for trasient anomalous diffusion that arises purely from local interactions, providing an intuitive alternative to models based on fractional calculus or non-local memory effects.

Motivated by recent interest in chiral superconductivity in narrow bands, we develop a general framework to clarify how band topology and quantum geometry affect superconducting pairing and connect to the two-body problem. Berry curvature does not merely favor a chiral pairing channel; it produces a sequence of chiral pairing instabilities indexed by angular momentum, controlled by the Berry flux through the Fermi sea, with a Little-Parks-like periodicity in momentum space. We show that Berry curvature converts a nonchiral attractive interaction into a geometrically frustrated Cooper problem in momentum space. The relevant control parameter is the Berry-curvature flux enclosed by the Fermi sea, $\Phi = b k_F^2$, which acts as an effective Aharonov-Bohm flux for the order parameter defined on the Fermi surface. As $\Phi$ is tuned, the leading pairing instability switches between odd angular-momentum channels $m=1,3,5...$, producing a cascade of first-order transitions and Little-Parks-like oscillations of $T_c$.

The physical origin of 'anomalous' resistivity in magnetic reconnection remains one of the longest-standing problems in space plasma physics. While the empirical Bohm diffusion scaling ($\eta \propto T/B$) is widely invoked to explain fast reconnection rates, it lacks a rigorous derivation from first principles. Here, we derive this scaling by modeling the ensemble of electron gyro-axes in a magnetized plasma as an overdamped spintronic condensate governed by the Landau-Lifshitz-Gilbert equation. We demonstrate that the breakdown of the "frozen-in" condition is rigorously identified as an Adler-Ohmic bifurcation: a topological phase transition where electron gyro-axes lose synchronization with the mean magnetic field. Unlike stochastic turbulence models, this framework predicts a coherent, explosive onset of resistivity that naturally saturates at the Bohm limit. We support this thesis with renormalization group theory and a novel analysis of Magnetospheric Multiscale mission data, which reveals an explosive phase space confinement consistent with collective phase slippage rather than chaotic scattering. These results suggest that Bohm resistivity is a universal topological property of magnetized matter at the critical point of reconnection.

Biological interactions with material surfaces span a wide range of length scales, yet conventional surface measurements often fail to account for scale, limiting the insights they provide for surface engineering. Here, we investigate how multiscale surface descriptors of plasma-modified silk fibroin and chitosan surfaces modify bacterial and immune cell response. Surface chemistry and topography were characterized using X-ray Photoelectron Spectroscopy (XPS) and Atomic Force Microscopy (AFM), followed by sliding bandpass filtration and multiscale curvature tensor-based methods to measure scale-dependent topographic features. Macrophage response and biofilm growth were assessed by fluorescence microscopy. Correlation strength showed scale-dependence with respect to surface features and biological structure: individual bacteria and small colonies correlated more strongly with fine-scale topographic features, whereas macrophage morphology correlated more strongly with larger-scale surface features. Notably, measured surface chemical descriptors generally did not correlate strongly with biofilm formation; nonetheless, chitosan and silk fibroin showed distinct trends in bacterial support, suggesting that material identity was not captured by the measured surface properties and that prevention of biofilms likely benefits from combinatorial approaches as opposed to physical surface modification alone. These results show that different biological structures interact with material surfaces at distinct length scales, as well as demonstrate the utility of multiscale analysis in identifying scales of interest in biological interactions with surfaces. Moreover, the data suggests that tailoring topographic feature size to the characteristic scale of the targeted biological entity is a potential strategy for antibacterial wound-healing materials without incurring cytotoxicity.

We study overdamped stochastic dynamics confined by hard reflecting boundaries and show that the combination of boundary geometry and an anisotropic diffusion tensor generically generates directed motion. At the level of individual trajectories, the no-flux condition enforces an oblique reflection at the boundary, which produces a systematic drift parallel to the surface. The resulting local velocity takes the general form $v_B(\mathbf{x})=\mathbf{t}(\mathbf{x})^{\!\top}\mathbf{D}\,\mathbf{n}(\mathbf{x})$, determined by the diffusion tensor $\mathbf{D}$ and the local boundary geometry encoded in the normal $\mathbf{n}$ and tangent $\mathbf{t}$. While this boundary-induced drift is local, it can accumulate into a macroscopic response, depending on the statistics of boundary encounters. We illustrate how this local boundary-induced drift gives rise to macroscopic transport using a minimal one-dimensional dimer composed of two particles with unequal diffusion coefficients. The repeated collisions act as reflections in configuration space and lead to sustained center-of-mass motion, including regimes of absolute negative mobility under constant forcing.

Metamaterials with floppy modes called mechanisms are a burgeoning template for shape-morphing systems and structures across scales. Here, we present a design recipe that transforms an arbitrary plane tiling into a 2D kirigami pattern with a single degree-of-freedom mechanism motion, greatly expanding the known library of mechanism-based designs. We reveal that these kirigami patterns, when deformed along their mechanism, have a bulk shape change invariant to the underlying microstructure of the pattern. Experimental observations confirm this unusual kinematic prediction in illustrative classes of designs. We also exploit this invariance to elicit different elastic responses in patterns with identical bulk shape change. Finally, we discuss generalizations to compact and non-planar kirigami, as well as 3D metamaterials, highlighting the broad applicability of our new approach to design.

The transmission eigenchannels of a disordered medium range from unity transmission and high internal energy density to vanishing transmission and low internal energy density. Although the transmission matrix underlies central ideas in mesoscopic electronic transport, it cannot be measured directly in electronic systems. For classical waves, however, the measurable matrix gives access to eigenchannels that control transmission and energy density within the medium. Coherence underlies transmission-eigenvalue scaling, the bimodal distribution in diffusive samples, and Anderson localization. What has remained unresolved is how interference among modal contributions is organized within individual eigenchannels throughout the sample. Here, microwave transmission-matrix measurements resolve the complex contribution of each incident mode to every transmitted mode of each eigenchannel. In simulations, we construct a flux matrix that generalizes the transmission matrix to every depth and further separate each internal modal contribution into forward- and backward-propagating components. For each eigenchannel, the directional flux in every mode throughout the sample factorizes into the squared modal amplitudes of the incident eigenchannel, coupling between waveguide modes, and modal alignment. In high-transmission eigenchannels, the contributions align increasingly constructively with depth, approaching nearly perfect alignment in the highest channel near the localization crossover. In low-transmission eigenchannels, contributions interfere destructively, producing vanishing transmission at a transmission zero. Because the contributions remain appreciable as their coherent sum approaches zero, transmission far below the noise floor of conventional transmission measurements can be determined.

The acceleration of materials discovery requires digital platforms that go beyond data repositories to embed learning, optimization, and decision-making directly into research workflows. We introduce DataScribe, an AI-native, cloud-based materials discovery platform that unifies heterogeneous experimental and computational data through ontology-backed ingestion and machine-actionable knowledge graphs. The platform integrates FAIR-compliant metadata capture, schema and unit harmonization, uncertainty-aware surrogate modeling, and native multi-objective multi-fidelity Bayesian optimization, enabling closed-loop propose-measure-learn workflows across experimental and computational pipelines. DataScribe functions as an application-layer intelligence stack, coupling data governance, optimization, and explainability rather than treating them as downstream add-ons. We validate the platform through case studies in electrochemical materials and high-entropy alloys, demonstrating end-to-end data fusion, real-time optimization, and reproducible exploration of multi-objective trade spaces. By embedding optimization engines, machine learning, and unified access to public and private scientific data directly within the data infrastructure, and by supporting open, free use for academic and non-profit researchers, DataScribe functions as a general-purpose application-layer backbone for laboratories of any scale, including self-driving laboratories and geographically distributed materials acceleration platforms, with built-in support for performance, sustainability, and supply-chain-aware objectives.

We address the general problem of formulating the dynamical large deviations of non-Markovian systems in a closed form. Specifically, we consider a broad class of “self-interacting” jump processes whose dynamics depends on the past through a functional of a state-dependent empirical observable. Exploiting a natural separation of timescales, \newnewtext{and under the corresponding mixing conditions,} we obtain the exact (so-called “level 2.5”) large deviation joint statistics of the empirical measure over configurations and of the empirical flux of transitions. As an application of this general framework, we derive explicit general bounds on the fluctuations of trajectory observables, generalising to the non-Markovian case both thermodynamic and kinetic uncertainty relations. We illustrate our theory with simple examples, and discuss potential applications of these results.

The Universal Operator Growth Hypothesis formulates time evolution of operators through Lanczos coefficients. In practice, however, numerical instability and memory cost limit the number of coefficients that can be exactly computed. In response to these challenges, the standard approach relies on fitting early coefficients to asymptotic forms, but such procedures can miss subleading, history-dependent structures in the coefficients that subsequently affect reconstructed observables. In this work, we treat the Lanczos coefficients as a causal time sequence and introduce a transformer-based model to autoregressively predict future Lanczos coefficients from short prefixes. For classical and quantum chaotic systems, our model outperforms asymptotic fits in both coefficient extrapolation and physical observable reconstruction, and achieves an order-of-magnitude reduction in error. The model also accurately extrapolates coefficients in integrable regimes, where no universal asymptotic fit exists. Remarkably, our model transfers across system sizes: it can be trained on smaller systems and then be used to extrapolate coefficients on a larger system without retraining. By probing the learned attention patterns and performing targeted attention ablations, we identify portions of the coefficient history that are most influential for accurate forecasts. Our results demonstrate that modern sequence models can serve as practical surrogates for probing operator dynamics deep in Krylov space, where brute-force Lanczos iteration can be computationally prohibitive.