Papers

Quantum Krylov subspace methods can extract ground and excited states by diagonalizing the Hamiltonian in a compact variational space. In practice, these spaces are almost always generated by real or imaginary time evolution, forcing a timestep trade-off between dynamical accuracy and basis collapse and often producing ill-conditioned overlap matrices that stall convergence. Here we introduce Quantum Krylov using Unitary Decomposition (QKUD), a time-evolution-free construction that maps Hamiltonian powers to implementable unitaries via the Hermitian transform $\sin(\epsilon H)/\epsilon$. QKUD reduces to the exact Hamiltonian-power Krylov recursion as $\epsilon\rightarrow0$, while finite $\epsilon$ provides a controllable deformation that tunes subspace geometry and improves conditioning. Across molecular active-space benchmarks and a frustrated 2D J1-J2 Heisenberg model, QKUD reproduces exact-Krylov convergence in well-conditioned regimes and systematically restores variational improvement when both exact Krylov and time-evolution Krylov stagnate. These results identify overlap conditioning, instead of time-evolution fidelity, is the key resource for robust quantum Krylov simulation and provide a resilient way forward for accurate quantum simulation of challenging quantum many-body problems.

We investigate the statistical properties of the vortex pinning potential in a thin superconducting film. Modeling intrinsic inhomogeneities by a random-temperature Ginzburg-Landau functional with short-range Gaussian disorder, we derive the pinning landscape $E(\mathbf{R})$ by determining how the vortex core adapts to randomness. Within the hard-core approximation, applicable for weak disorder, the energy landscape exhibits Gaussian statistics. In this regime, the mean areal density of its minima is given by $n_\text{min}\approx(6\xi)^{-2}$, indicating that the typical spacing between neighboring minima is significantly larger than the vortex core size $\xi$. Going beyond the hard-core approximation, we allow the vortex order parameter to relax in response to the inhomogeneities. As a result, the pinning potential statistics become non-Gaussian. We calculate the leading correction due to the core deformation, which reduces the density of minima with a relative magnitude scaling as $(T_c-T)^{-1/2}$.

We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states. Using an autoencoder neural network architecture on data from $L$-site Fermi-Hubbard models, we identify minimal latent spaces with a sharp reconstruction quality threshold at $L-1$ latent dimensions, matching the system's intrinsic degrees of freedom. We demonstrate the use of the trained decoder as a differentiable variational ansatz to minimize energy directly within the latent space. Crucially, this approach circumvents the $N$-representability problem, as the learned manifold implicitly restricts the optimization to physically valid quantum states.

Water behaves very differently at surfaces and under extreme confinement, but the boundary between these two regimes has remained unclear. Despite evidence that interfacial effects persist under sub-nanometre confinement, the molecular-scale behaviour and its evolution with slit width remain unclear. Here, we use machine-learning molecular dynamics with first-principles accuracy to probe water at graphene surfaces across slit widths ranging from the open-interface limit to angstrom-scale confinement. We find that water undergoes a sharp structural transition: when three or more water layers fit between the walls, the structure of the graphene-water interface is effectively indistinguishable from that in an open system, with density layering, hydrogen bonding, and orientational ordering retaining interfacial character. Below this threshold, however, angstrom-scale confinement strongly reorganises the liquid, producing enhanced ordering, a restructured hydrogen-bond network, and modified orientational motifs. These results establish a molecular-level picture that clearly separates interfacial behaviour from genuine nanoconfinement and provide guidance for predicting and controlling the structure of water in nanoscale solid-liquid environments.

Softer means stickier for solid adhesives, because material compliance facilitates close contact between non-conformal surfaces. Recent discoveries have revealed that soft materials can exhibit a rich array of new physics arising from competing effects of continuum elasticity, fluid-like surface mechanics, and internal poroelastic flows, all of which can directly impact interfacial interactions. In this work, we investigate this complex interplay across several orders of magnitude of elastic stiffness by measuring the complete adhesive contact geometry between compliant silicone gel microspheres and flat, rigid substrates. We observe a continuous elastocapillary transition in adhesion mechanics, with novel features revealed by both the breadth of data and the detailed contact geometries. Importantly, soft gel spheres exhibit a remarkably broad range of near-equilibrium contact morphologies and their contact line deformation is always mediated by a fluid contact zone that phase separates from the gel. To explain this, we develop a new model incorporating elastocapillary and poroelastic mechanics that predicts the complete range of adhesive behavior and elucidates energetic tradeoffs. The data and model together reveal a shallow energy landscape that may contribute to the robustness of everyday adhesives.

Quantum self-oscillatory phases are ubiquitous in driven-dissipative systems. Classically, each phase is defined by its flow pattern and how stationary sets organize phase space (e.g. fixed points and limit cycles), with transitions triggered by local bifurcations or global basin rearrangements. In the quantum regime, these reorganizations are often blurred by density-matrix averaging, and spectral indicators such as the Liouvillian gap can miss changes that unfold mainly in the transients. Here we introduce a topological graph invariant, the molecule, which captures the phase-space connectivity of fixed points and limit cycles. Transitions show up as discrete changes of this invariant, with each form marking a distinct quantum dynamical pattern (e.g. relaxation pathway). The molecule encodes the global topological constraints that govern how stationary sets and their basins can rearrange, clarifies when such rearrangements can affect the Liouvillian modes, and reveals additional transitions that remain hidden in the steady-state spectrum but stem from global changes of the flow topology. Our findings show that flow topology offers a clear and unified way to identify and classify dynamical phases beyond what Liouvillian spectra alone reveal.

Altermagnets represent a newly identified third class of collinear magnets and have recently emerged as a focal point in condensed matter physics. In this work, through first-principles calculations and theoretical analysis, we identify monolayer Fe$_2$MoX$_4$ (X = S, Se, Te) and Fe$_2$WTe$_4$, a class of iron-based transition metal chalcogenides, as promising altermagnetic materials. These systems are found to be semiconductors exhibiting spin splitting in their nonrelativistic band structures, indicative of intrinsic altermagnetic ordering. Remarkably, their valence bands feature a pair of valleys at the time-reversal-invariant momenta X and Y points. Unlike conventional valley systems, these valleys are related by crystal symmetries rather than time-reversal symmetry. We investigate valley-dependent physical phenomena in these materials, including Berry curvature and optical circular dichroism, revealing strong valley-contrasting behavior. Furthermore, we investigate the effect of uniaxial strain and show that it effectively lifts the valley degeneracy, resulting in pronounced valley polarization. Under hole doping, this strain-induced asymmetry gives rise to a piezomagnetic response. We also explore the generation of anisotropic noncollinear spin currents in these systems, expanding the scope of their spin-related functionalities. Our findings unveil rich valley physics in monolayer Fe$_2$MoX$_4$ (X = S, Se, Te) and Fe$_2$WTe$_4$, highlighting their significant potential for applications in valleytronics, spintronics, and multifunctional nanoelectronic devices.

Exceptional points (EPs), non-Hermitian spectral singularities, shape the evolution dynamics ranging from topological mode control to high-precision sensing. Higher-order EPs unlock enriched and sharpened spectral topology beyond the minimal second-order counterpart, thus expanding capabilities in those domains. However, the intrinsic topological fragility of higher-order EPs has long hindered their robust experimental realization. Here, we present a scalable architecture that implements arbitrary-order EPs via an optically mediated recurrent network comprising a single nanomechanical resonator and an ensemble of digital synthetic resonators. We experimentally validate this architecture's scalability for both linear and nonlinear mechanical EPs, demonstrating EPs up to the seventh order. Moreover, we reveal that the fundamental noise component, in contrast to some technical-noise contributions, shares the same system coupling channel and undergoes identical root-response amplification as the measured signal. The co-amplification mechanism sets the fundamental sensitivity limit near EPs of any order, as verified by direct signal-to-noise ratio measurements. Our work establishes a general platform for exploring higher-order EP-based phenomena while clarifying the fundamental boundary of non-Hermitian sensitivity enhancement. More broadly, it facilitates a connection between non-Hermitian physics and AI-inspired recurrent architectures across diverse physical systems.

Structured optical fields can be used for the injection and control of charge and spin-valley currents. Here, we present a systematical study of these phenomena for interband absorption of structured light in 2D Dirac materials. We derive general expressions for the current density and the quasi-classical generation rate of photoelectrons in the momentum, coordinate, and spin-valley spaces. We reveal mechanisms of the current formation determined by the local and non-local contributions to the optical generation, including the mechanisms related to optical alignment of electron momenta by linearly polarized light, optical orientation by circularly polarized light, and the class of charge and spin-valley photon drags sensitive to the phase and polarization profiles of the optical field. We develop a kinetic theory of electric and spin-valley currents driven by the optical field with spatially inhomogeneous intensity, polarization, and phase and obtain analytical expressions for the current contributions. The theory is applied to analyze the photocurrents emerging in TMDC layers and graphene excited by polarization gratings.

Quantum sensing is an emerging field with the potential to outperform classical methods in both precision and spatial resolution. However, the sensitivity of the underlying quantum platform also makes the sensors highly susceptible to their environmental noise. To address this issue, techniques from the field of quantum error mitigation use information about the noise to improve measurement results. We present a novel mitigation technique for quantum sensors to efficiently reverse the effects of any noise that can be described by a completely positive trace preserving map. The method leverages the knowledge acquired by a pre-characterization step of the device to automatically adapt to the complexity of the dissipative evolution and to indicate optimal sensing times $\tau$ to achieve the most accurate results. We demonstrate that our method reaches the best achievable sensitivity in noisy single-NV-center magnetometry. This work marks a further step toward more resilient quantum sensors with the smallest scale of resolution.

Kagome lattice antiferromagnets exhibit plethora of intriguing phases of matter. Particularly interesting state appears at the magnetic field-induced $1/9$ magnetization plateau observed in several recent experimental studies. The nature and exotic physical properties of the plateau however remain controversial due to an exceptional complexity of the state generated by geometrical frustration. Among candidate states recent studies found a $Z_3$ quantum spin liquid state, a valence bond crystal exhibiting an hourglass pattern and a valence bond crystal state with a $3\times 3$ periodicity and a windmill-shaped motif. Recent torque magnetometry measurements on YCOB single-crystal samples however indicate presence of Dirac-like spinons at $1/9$ magnetization plateau. We study properties of the plateau state using novel machine learning technique that combines variational Monte Carlo, symmetry enhanced neural network quantum states and flux insertion method. Our machine learning study reveals that the ground state at the $1/9$ plateau is a gapless $1\times 1$ chiral spin density wave caused by 2$k_F$ instability of the underlying composite Fermi liquid. The spin wave chirality results from the correlated spin order that reflects its nontrivial topology.

Hematite $\alpha$-Fe$_2$O$_3$ is a $g$-wave altermagnetic material, which has an easy-axis phase and easy-plane weak ferromagnetic phase below and above Morin transition temperature, respectively. The presence of these phases renders it a good candidate to study the characteristic spin splitting in altermagnets under the impacts of relativistic effect and finite temperature. In this regard, we have calculated the band structure of $\alpha$-Fe$_2$O$_3$ based on density functional theory (DFT) which also considers the Hubbard-U correction and spin-orbit coupling (SOC) effects. Additionally, the charge self-consistent DFT + dynamical mean-field theory (DMFT) calculations have been performed at finite temperatures. It is found that the altermagnetic spin splitting in $\alpha$-Fe$_2$O$_3$ preserves with either SOC or temperature effect taken into account. Furthermore, we present a numerical simulation of the x-ray magnetic circular dichroism (XMCD) of the L$_{2,3}$ edge of Fe using a combination of DFT with multiplet ligand-field theory (MLFT). In terms of the different Néel vectors present in $\alpha$-Fe$_2$O$_3$, we calculate the x-ray absorption spectroscopy (XAS) of the L$_{2,3}$ edge of Fe in the form of conductivity tensor and analyze the XMCD response from a perspective of symmetry. A characteristic XMCD line shape is expected when the Néel vector is along [010] direction (magnetic point group $2^\prime/m^\prime$) and the light propagation vector is perpendicular to the Néel vector, which can be further distinguished from the XMCD response originated from weak ferromagnetism with the light propagation vector parallel to the Néel vector.

Curvilinear magnetic nanostructures enable control of magnetization dynamics through geometry-induced anisotropy and chiral interactions as well as magnetic field modulation. In this work, we report a curvilinear magnonic crystal based on large-area square arrays of truncated nanospikes fabricated by conformal coating of 3D hierarchical templates with permalloy thin films. Brillouin light scattering spectroscopy reveals anisotropic band structure with multiple dispersive and folded Bloch-type dispersive spin-wave modes as well as non-dispersive modes exhibiting direction-dependent frequency shifts and intensity asymmetries along lattice principal axes. Finite element micromagnetic simulations indicate that curvature-induced variations of the demagnetizing field govern the magnonic response, enabling the identification of modes propagating in nanochannels and other localized on nanospike apexes or along the ridges connecting adjacent nanospikes. The combination of geometric curvature and optical probing asymmetry produces directional dependence of magnonic bands, establishing 3D hierarchical templates as a versatile platform for curvature-engineered magnonics.

The nuclear spin is a prime candidate for quantum information applications due to its weak coupling to the environment and inherently long coherence times. However, this weak coupling also challenges the addressability of the nuclear spin. Here we demonstrate nuclear magnetic resonance (NMR) on a single on-surface atom using a local scanning probe. We employ an electron-nuclear double resonance measurement scheme and resolve nuclear spin transitions of a single 47Ti isotope with a nuclear spin of I = 5/2. The quadrupole interaction enables to resolve multiple NMR transitions, which are consistent with our eigenenergy calculations. Our experimental results indicate that the nuclear spin can be driven efficiently irrespective of its hybridization with the electron spin, which is required for direct control of the nuclear spin in the long-lifetime regime. This investigation of NMR on a single atom in a platform with atomic-scale control is a valuable development for other platforms deploying nuclear spins for characterization techniques or quantum information technology.

Innovation in biomaterials has brought both breakthroughs and new challenges in medicine, as implant materials have become increasingly multifunctional and complex. One of the greatest issues is the difficulty in assessing the temporal and multidimensional dynamics of tissue-implant interactions. Implant biology remains hard to decipher without a noninvasive and multiplexed technique that can accurately monitor real-time biological processes. To address this, we developed a multifunctional, self-sensing implant material composed of gold nano-columns patterned on a titanium surface (AuNC-Ti). This material acts as a nanoengineered surface-enhanced Raman spectroscopy (SERS) substrate that amplifies biological Raman signals at the tissue-implant interface, providing the ability to sense tissue-material interactions in a multiplexed and nondestructive manner. AuNC-Ti SERS substrates were fabricated using oblique angle deposition (OAD) and characterized using scanning electron microscopy (SEM) to show uniform formation of AuNCs ($360 \pm 40$ nm in length and $50 \pm 16$ nm in width). X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and contact angle measurements demonstrated biocompatible surface chemistry with ideal wettability. Biocompatibility was further demonstrated via in vitro cytotoxicity assays on human aortic endothelial cells (HAECs) cultured on AuNC-Ti surfaces. The median SERS enhancement factor (EF) was calculated to be $1.8 \times 10^5$, and spatial identification of reporter molecules and porcine tissue components on AuNC-Ti surfaces was demonstrated using confocal Raman imaging and multivariate analysis. Our approach utilizes unlabeled SERS and machine learning, promising multiplexed characterization of tissue-material interactions and subsequently enabling tissue state determination and non-invasive monitoring of implant-tissue interaction.

With the rapid advancement of machine learning techniques for materials simulations, machine-learned force fields (MLFFs) have become a powerful tool that complements first-principles calculations by enabling high-accuracy molecular dynamics simulations over extended timescales. Typically, MLFFs are trained on data generated from density functional theory (DFT) using a specific exchange-correlation (XC) functional, with the goal of reproducing DFT-level properties. However, the uncertainties in MLFF-based simulations–arising from variations in both MLFF model architectures and the choice of XC functionals–remain insufficiently understood. In this work, we construct MLFF models of different architectures trained on DFT data from both semilocal and hybrid functionals to describe Li$^+$ diffusion in the solid-state electrolyte Li$_6$PS$_5$Cl. We systematically investigate how different XC functionals influence the Li$^+$ diffusion coefficient. To reduce statistical uncertainty, the mean squared displacements are averaged over 300 independent molecular dynamics (MD) trajectories of 70 ps each, yielding statistical variations below $1\%$. This enables a clear assessment of the respective influences of the functional and the MLFF model. Due to its tendency to underestimate band gaps and migration barriers, the semilocal functional predicts consistently higher Li$^+$ diffusion coefficients, compared to the hybrid functional. Furthermore, comparisons among various neural network methods reveal that the differences in predicted diffusion coefficients arising from different network architectures are of the same order of magnitude as those caused by different functionals, indicating that the choice of the network model itself substantially influences the MLFF predictions. This observation calls from an urgent need for standardized protocols to minimize model-dependent biases in MLFF-based MD.

High performance devices consisting of interdigitated electrodes (IDEs) on top of single-layer graphene (SLG) are candidates with favorable prospects for sensing applications. Graphene micro ribbons (GMRs) of various widths and IDE design geometries were fabricated and experimentally examined regarding their low-frequency noise (LFN) behavior. Measurements revealed a 1/f behavior and different kinds of trap activity behind it, which were studied through the analysis of random telegraph noise (RTN) signals. Our investigation suggests that adjusting the geometrical characteristics of either the GMR width or the IDE topology can significantly influence the signal-to-noise ratio (SNR) of SLG-based electronics. On the bright side, the results of our study can provide useful guidelines for fabrication decisions to maximize the SNR.

Strongly disordered superconductors (SDSCs) are widely used in qubits, microwave resonators, photon detectors, and other superconducting quantum devices. In SDSC-based devices, coherence times are limited by low-temperature microwave dissipation in the material. However, the standard Mattis-Bardeen theory fails in SDSCs because their single-particle spectrum exhibits a hard pseudogap $\Delta_{P}$ both below and above the transition temperature $T_{c}$. We develop a novel microscopic theory of the dependence of ac dissipation in such systems on temperature $T$ and frequency $\omega$. We analyze the resonator quality factor $Q(\omega,T)$ in the practically relevant range $\hbar\omega,\,T\ll\Delta\leq\Delta_{P}$, where $\Delta$ is the typical superconducting order parameter, distinct from $\Delta_{P}$. We show that low-$\omega$ dissipation is dominated by a new type of bulk localized collective modes arising from spatial inhomogeneity of the superconducting state. Consequently, $Q(\omega)$ decreases strongly with $\omega$ and exhibits two-level-system-like growth with $T$ for $T\ll T_{c}$. Our theory provides a microscopic understanding of existing and future experiments on thin films of $\mathrm{InO}_{x}$, TiN, NbN, and similar SDSCs, and is phenomenologically relevant to granular aluminum films. The results suggest strategies to mitigate intrinsic microwave losses in SDSC-based quantum devices.

Neural Quantum States (NQS) are powerful tools used to represent complex quantum many-body states in an increasingly wide range of applications. However, despite their popularity, at present only a rudimentary understanding of their limitations exists. In this work, we investigate the dependence of NQS on the choice of the computational basis, focusing on restricted Boltzmann machines. Considering a family of rotated Hamiltonians corresponding to the paradigmatic transverse-field Ising model, we discuss the properties of ground states responsible for the dependence of NQS performance, namely the presence of ground state degeneracies as well as the uniformity of amplitudes and phases, carefully examining their interplay. We identify that the basis-dependence of the performance is linked to the convergence properties of a cluster or cumulant expansion of multi-spin operators – providing a framework to directly connect physical, basis-dependent properties, to performance itself. Our results provide insights that may be used to gauge the applicability of NQS to new problems and to identify the optimal basis for numerical computations.

Delafossite compounds containing rare-earth ions have been proven to be an ideal platform to investigate frustrated magnetic ground states. Here, we discuss two triangular-lattice antiferromagnets, TlErSe$_2$ and TlTmSe$_2$, as potential candidates for hosting exotic quantum states. Powder X-ray diffraction data analysis of the black-color polycrystalline Tl$RE$Se$_2$ ($RE$: Er and Tm) samples confirms the phase purity. Both materials crystallize in the trigonal $\alpha$-NaFeO$_2$ structure ($R\overline{3}m$) with lattice parameters $a$ = 4.1070(4) Å and $c$ = 23.1472(1) Å for the erbium compound and $a$ = 4.0916(1) Å and $c$ = 23.1483(2) Å for the thulium compound. Magnetic susceptibility measurements show an effective moment of $\mu_{\text{eff}} = 9.6(2) \mu_B$/f.u. ($7.5(1) \mu_B$/f.u.) for TlErSe$_2$ (TlTmSe$_2$) for temperatures above 200 K. While $^3$He specific-heat measurements reveal long-range magnetic order below $T_N = 0.42 $K for TlErSe$_2$, no sign of long-range magnetic order was observed for TlTmSe$_2$. Based on our results, we map out the T-H phase diagram for polycrystalline TlErSe$_2$ and discuss the striking difference in the magnetic behavior of TlTmSe$_2$ based on our ab initio quantum chemical calculations.

We argue that the low-temperature physics of $S=1$ pyrochlore magnets with a predominantly Ising-like easy-axis exchange coupling $J$ that favors the local tetrahedral body diagonals, and a comparably large easy-plane single-ion anisotropy $\Delta =J + \mu$ ($|\mu| \ll J$) that favors the plane perpendicular to these local axes will exhibit interesting new phenomena due to the competition between $J$ and $\Delta$. In the $T/J \rightarrow 0$ limit, we find three low temperature phases as a function of $\mu/T$: a short-range correlated paramagnetic phase, and two topologically-distinct Coulomb liquids separated by a $Z_2$ flux confinement transition. Both Coulomb liquids are described at long-wavelengths by a fluctuating divergence-free polarization field and have characteristic pinch-point singularities in their structure factor. In one Coulomb phase, the flux of this polarization field is confined to even integers, while it takes on all integer values in the other Coulomb phase. Experimental realizations with $|\mu| \ll J$ and negative are predicted to exhibit signatures of a transition from a flux-deconfined Coulomb phase to the flux-confined Coulomb phase as they are cooled below $T_{c_2} \approx 1.57|\mu|$, while realizations with positive $\mu \ll J$ will show signatures of a transition from a flux-deconfined Coulomb liquid to a short-range correlated paramagnet via a continuous $XY$-like transition at $T_{c_1} \approx 0.98 \mu$.

Predicting degradation rates is an important task in the development of new perovskite solar cells (PSCs). In this paper, we explore the feasibility of solving this problem using Machine Learning models supported by LLM tools. We consider both the "lifetime" prediction of the device and the prediction of degree of its degradation at specific time intervals. We demonstrate the ability of common LLM tools (ChatGPT, DeepSeek) to suggest and justify prediction methods in a dialogue with the user under conditions of incomplete information about the physical models of PSC degradation and the influence of the environment, providing rather accurate prediction. The results cover the formation of time series of efficiency with a given architecture, calculated using various available mathematical models together with environmental characteristics archived in various meteorological databases (illumination, temperature, humidity, UV level). We conclude that ChatGPT currently has sufficient access to training samples, can suggest to PSC designer various PSC models in the literature, and has adequate solutions for predicting degradation trends.

Unlike discrete photonic circuits, which manipulate photons step-by-step using a series of optical elements, arrays of coupled waveguides enable photons to interfere continuously across the entire structure. When composed of a nonlinear material, such arrays can also directly generate quantum states of light within the circuit. To clarify the similarities and distinctions between these two approaches of quantum walks, we conduct here a systematic comparison between linear waveguide arrays, injected with photons produced externally, and nonlinear arrays, where photon pairs are continuously generated via parametric down-conversion. We experimentally validate these predictions using III-V semiconductor nonlinear waveguide lattices with varied geometries, enabling us to tune the depth of the quantum walks over an order of magnitude and reveal the gradual emergence of non-classicality in the output state. Finally, we demonstrate an inverse-design approach to engineer aperiodic waveguide arrays, whose optimized coupling profiles generate maximally entangled states such as the biphoton W-state. These results highlight the potential of continuously-coupled photonic systems to harness high-dimensional entanglement within compact architectures.

Single-particle heat engines at atomic and colloidal scales obey the universal thermodynamic bounds on work and efficiency. Here, we translate these principles to the macroscale by building an athermal Stirling engine whose working medium is a millimeter-sized, vibrofluidized granule confined in a time-dependent magnetic trap. By embedding a rattler within the granule to inject noise, we engineer overdamped, Brownian-like dynamics in an otherwise inertial particle. This design enables independent control over the granule's effective temperature and spatial confinement. Our engine quantitatively reproduces the universal power-efficiency trade-offs of finite-time thermodynamics, achieving the Curzon-Ahlborn efficiency at maximum power. Strikingly, we uncover a control parameter-dependent damping that leads to an unexpected dissipation mechanism - the losses in the compression stroke rival or even exceed those during expansion. Our work establishes an accessible experimental platform to study small-system thermodynamics in intrinsically athermal systems.

Electron-phonon coupling is believed to be responsible for many spectral anomalies in the cuprate superconductors. In particular, the $B_{1g}$ buckling mode of the oxygen ion in the $CuO_{2}$ plane has been proposed to be responsible for the dramatic peak-dip-hump(PDH) structure in the anti-nodal spectrum. The recent observation of the exceptional flat quasiparticle dispersion in the anti-nodal region and the sudden suppression of the PDH structure around the pseudogap end point cast doubts on such a scenario. Instead, a scenario involving the coupling to the antiferromagnetic spin fluctuation seems to resolve both puzzles naturally. Here we present a systematic study on the interplay between antiferromagnetic spin fluctuation and electron-phonon coupling in the cuprate superconductors. We show that the coupling strength to the $B_{1g}$ buckling mode is strongly suppressed by the vertex correction caused by the antiferromagnetic spin fluctuation in the $\mathbf{q}\rightarrow 0$ limit as a result of the destructive interference between electron-phonon coupling at electron momentum differ by the antiferromagnetic wave vector. Counterintuitively, we find that the same vertex correction enhances the phonon contribution to the PDH structure. We also find that while the coupling to either the antiferromagnetic spin fluctuation or the $B_{1g}$ buckling mode can generate a PDH structure in the anti-nodal spectrum with similar phenomenologies, the sudden suppression of such a structure around the pseudogap end point should be mainly attributed to the dramatic change in the nature of the spin fluctuation at such a critical doping. We suggest to take the PDH structure in the anti-nodal spectrum as a spectral signature for the emergence of fluctuating local moment in the pseudogap phase and the entrance of a doped Mott insulating state.

We develop a fluctuation framework to quantify the free energy difference between two equilibrium states connected by nonequilibrium processes under arbitrary dynamics and system-environment coupling. For an open system described by the Hamiltonian of mean force (HMF), we show that the equilibrium free energy difference between two canonical endpoints can be written as exponential averages of the HMF shift, divided by an explicit factor built from the chi-squared divergence between the initial and final system marginals. These relations hold at the endpoint level and, under an asymptotic equilibration postulate, admit trajectory representations for general driving and coupling protocols. A decomposition of the HMF increment along each trajectory separates the work-like contributions associated with changes in $\lambda(t)$ and $C(t)$, the heat-like exchange with the environment, and a feedback-like functional defined with respect to the initial protocol. In the frozen-driving regime with a noninteracting reference, the equalities reduce to new FEP-like expressions involving an environment functional and an explicit overlap correction, with the Zwanzig formula recovered as a limiting case. We validate the approach on an open system coupled to an environment and evolved under overdamped Langevin dynamics, where conventional Zwanzig FEP suffers from poor phase-space overlap and slow numerical convergence, while the present trajectory equality closely matches the exact free energy difference over a broad range of coupling strengths.

The exploration of advanced CO-free catalysts and clarifying the ambiguous kinetic origins and governing factors would undoubtedly open up opportunities to overcome the sluggish kinetics of methanol electrooxidation and promote the development of direct methanol fuel cells. Herein, we constructed a family of Bi-modified Pt3M intermetallic catalysts (Bi-Pt3M/C, M=Cr, Mn, Co, Zn, In, Ga, and Sn) that follow CO-free dominated pathway and exhibit exceptional catalytic activity. More significantly, leveraging this platform, we have identified the pivotal factor governing the reaction kinetics in CO-free pathway, namely OH binding energy (OHBE). This arises because the rate-determining step (RDS) encompasses both C-H bond activation and water dissociation, whose respective barriers can be reflected by the OHBE. Accordingly, OHBE can act as an activity descriptor. Specifically, Bi-Pt3In/C stands out from other Bi-Pt3M/C and delivers the unprecedented mass activity of 36.7 A mgPt-1 at peak potential, far exceeding state-of-the-art Pt-based catalysts reported to date. Taking Bi-Pt3In/C as a proof of concept, we clearly elucidate the origin of enhanced MOR activity by combining theoretical calculations, kinetic isotope effects, and formaldehyde electrooxidation. Moreover, there exhibits a volcano-type trend between OHBE and the activity of Bi-Pt3M/C. Beyond the discovery of ultrahigh-performance catalysts, these findings provide a detailed mechanistic picture of RDS and offer an innovative design principle for advanced catalysts.

Cumulative geometric frustration can drive self-limited assembly and morphology selection through size-dependent energetic costs. However, the slenderness of quasi-one-dimensional systems generally suppresses the formation of long-range longitudinal gradients. We show that the suppression of longitudinal gradients can be overcome by tuning the ratio between the longitudinal and transverse (shear) moduli. We demonstrate the recovery of cumulative frustration across distinct quasi-one-dimensional systems, each frustrated through a different mechanism, by the introduction of a soft response mode. The resulting saturation length scale is universal and is dictated by the local constitutive information of the system.

We study the dynamics of capillary filling in tubes of regular polygon cross-section. Using Onsager variational principle, we derive a coupled ordinary differential equation and partial differential equation, which respectively describe time evolution of the bulk flow and the saturation profile of the finger flow. We obtain both numerical solution and self-similar solution to the coupled equations, and the results indicate that the bulk flow and the finger flow both follow the $t^{1/2}$ time-scaling. We show that due to the coupling effect of the finger flow, the prefactor for the bulk flow is smaller than that of the Lucas-Washburn prediction. The reduction effect is more pronounced when the side number $n$ of the regular-polygon is small, while as $n$ increases, the prefactor approaches Lucas-Washburn prediction.

Magnetoresistive effects are typically symmetric under magnetization reversal. However, nonlinear spin transport can give rise to unidirectional magnetoresistance in systems with strong spin-orbit interaction and broken inversion symmetry. Here, we demonstrate that the nonequilibrium magnon accumulation characterized by a finite magnon chemical potential can lead to a large and robust magnonic unidirectional spin Hall magnetoresistance (USMR) in the weakly coupled van der Waals antiferromagnet CrPS4 in contact with Pt. Unlike conventional magnonic USMR driven by magnetization fluctuations, this effect persists under strong magnetic fields and low temperatures, with a pronounced peak near the spin-flip transition. The magnitude of magnonic USMR in CrPS4/Pt exceeds that of YIG/Pt by more than two orders of magnitude and surpasses the electrical USMR in metallic Ta/Co bilayers by a factor of two. The observed field and temperature dependence indicates that spin transport is dominated by magnon chemical potential gradients rather than thermal- or fluctuation-driven magnon generation. These findings establish a new mechanism for nonlinear magnetoresistance in antiferromagnetic van der Waals heterostructures and open a route to magnon-based antiferromagnetic spintronic functionalities in two-terminal device geometries.