Papers

Cycles are ubiquitous in various networks such as social, biological, and technological systems, where they play a significant functional and dynamical role. This paper proposes a node similarity measure based on minimal simple cycles, referred to as cycle similarity. Specifically, the metric quantifies the similarity between two nodes by considering the minimal cycles that connect them through their neighboring nodes, with an upper bound imposed on the cycle size to ensure computational feasibility. We then systematically examine the effectiveness and applicability of this similarity measure through two fundamental tasks: link prediction and community detection. To address the scarcity of cycles in link prediction, an edge-addition correction strategy is introduced, whereby the existence of a candidate edge is hypothetically assumed before computing node similarity. Experimental results demonstrate that this correction leads to improved performance on datasets including karate, INT, PPI, and Grid. In hierarchical community detection using cycle similarity, we find that the significance of cyclic structures (reflected by Z-scores), the presence of pendant nodes with degree one, and the existence of cut vertices are the primary factors influencing the algorithm's performance.

The NL-$e$EDM experiment searches for a non-zero electric dipole moment of the electron $d_e$ ($e$EDM) in the ground state of barium monofluoride (BaF). A beam of BaF from a supersonic expansion source is probed with the spin precession method presented in \cite{Boeschoten2024}. This method permits the extraction of an $e$EDM value as well as values for parameters causing a possible systematic bias leading to a false $e$EDM. The currently achievable sensitivity is limited by statistics collected in a period of 34 hours and yields an $d_e$ of $2(3) \times 10^{-25}$ $e\,$cm. Furthermore, from the same dataset sufficiently strong limits on parameters which can induce a false $e$EDM are extracted. These are mainly the electric field E and the intensity of the lasers fields in the fiducial volume of the experiment. We summarize the steps required to upgrade of the experiment to reach a competitive level on $d_e$, e.g. an intense laser-cooled beam from a cryogenic buffer gas source and the light collection efficiency of fluorescence.

Conical Tensile Membrane Structure (TMS) is commonly used for aesthetics, economic design, high rain and snow loading. Such TMS shows complex aerodynamic behavior in presence of geometric nonlinearity, not adequately studied in the past. The aerodynamic responses of anticlastic conical TMS under random wind loading is presented herein along with an equivalent static wind resistant design approach. The stochastic wind loading on the TMS in the atmospheric boundary layer (ABL) is simulated via the Large Eddy simulation (LES); which is detailed in a previous study by the authors and hence not repeated here. The aerodynamic loading is then employed as input in conducting the nonlinear time history analyses considering open (i.e. without facade) and closed (with facade) TMS, supported by peripheral/radial cables. The influence of the key parameters (aerodynamic roughness height, the rise-span ratio of the TMS and the membrane prestress, notably) are demonstrated. Although increasing prestress and rise-to-span ratio enhances the stiffness of TMS, the former shows dominance. Increasing roughness height also lead to increased peak loading/responses by enhanced turbulence. An equivalent static wind-resistant design is presented via the Gust Response Factors (GRFs) and an additional Nonlinear Adjustment Factors (NAFs). These factors are presented systematically, encompassing alternative scenarios. Multi-linear regression models are presented for predictive modeling of these factors, along with a probabilistic analysis for their design values that can be employed in practice bypassing an involved nonlinear dynamic analysis.

The evaluation of the electrostatic potential is fundamental to the study of condensed phase systems. We discuss the calculation of the relevant lattice summations by Ewald-type techniques. A model charge density is introduced, that cancels multipole moments of the crystalline charge distribution up to a desired order, for accelerating convergence of the Ewald sums. The method is applicable to calculations of bulk systems, employing arbitrary unit cells in a classical or quantum context, and with arbitrary basis functions to represent the charge density. The efficacy of the method is demonstrated on the calculation of the fundamental gap of the gallium arsenide bulk semiconductor, as a prototype example, where significantly accelerated convergence is numerically confirmed, due to a reduction of the number of two-electron integrals that need to be computed. The approach clarifies a decades-old implementation in the CRYSTAL code.

Human behavior plays a critical role in shaping epidemic trajectories. During health crises, people respond in diverse ways in terms of self-protection and adherence to recommended measures, largely reflecting differences in how individuals assess risk. This behavioral variability induces effective heterogeneity into key epidemic parameters, such as infectivity and susceptibility. We introduce a minimal extension of the susceptible-infected-removed~(SIR) model, denoted HeSIR, that captures these effects through a simple bimodal scheme, where individuals may have higher or lower transmission–related traits. We derive a closed-form expression for the epidemic threshold in terms of the model parameters, and the network's degree distribution and homophily, defined as the tendency of like–risk individuals to preferentially interact. We identify a resurgence regime just beyond the classical threshold, where the number of infected individuals may initially decline before surging into large-scale transmission. Through simulations on homogeneous and heterogeneous network topologies we corroborate the analytical results and highlight how variations in susceptibility and infectivity influence the epidemic dynamics. We further show that, under suitable assumptions, the HeSIR model maps onto a standard SIR process on an appropriately modified contact network, providing a unified interpretation in terms of structural connectivity. Our findings quantify the effect of heterogeneous behavioral responses, especially in the presence of homophily, and caution against underestimating epidemic potential in fragmented populations, which may undermine timely containment efforts. The results also extend to heterogeneity arising from biological or other non-behavioral sources.

This paper summarizes and consolidates fusion power-plant costing work performed in support of ARPA-E from 2017 through 2024, and documents the evolution of the associated analysis framework from early capital-cost-focused studies to a standards-aligned, auditable costing capability. Early efforts applied ARIES-style cost-scaling relations to generate Nth-of-a-kind (NOAK) estimates and were calibrated through a pilot study with Bechtel and Decysive Systems to benchmark balance-of-plant (BOP) costs and validate plant-level reasonableness from an engineering, procurement, and construction (EPC) perspective. Subsequent work, informed by Lucid Catalyst studies of nuclear cost drivers, expanded the methodology to treat indirect costs explicitly and to evaluate cost-reduction pathways for non-fusion-island systems through design-for-cost practices, modularization, centralized manufacturing, and learning. As ARPA-E's fusion portfolio expanded, these methods were applied across BETHE and GAMOW concepts (and select ALPHA revisits), including enhanced treatment of tritium handling and plant integration supported by Princeton/PPPL expertise. In 2023 the capability was refactored to align with the IAEA-GEN-IV EMWG-EPRI code-of-accounts lineage, while key ARIES-derived scaling relations were replaced by bottom-up subsystem models for dominant fusion cost drivers (e.g., magnets, lasers, power supplies, and power-core components) coupled to physics-informed power balances and engineering-constrained radial builds. These developments were implemented in the spreadsheet-based Fusion Economics code (FECONs) and released as an open-source Python framework (pyFECONs), providing a transparent mapping from subsystem estimates to standardized accounts and a consistent computation of LCOE.

In this work, we investigate the applicability of the core-polarization-corrected Dirac–Fock method, formulated within the framework of the local Dirac–Hartree–Fock (LDF) potential, for the accurate determination of static scalar and tensor electric dipole polarizabilities. This work presents theoretical values of blackbody-radiation-induced Stark shifts of atomic energy levels. The Dirac–Fock method augmented by core-polarization corrections is employed not only to evaluate these shifts but also to compute the Bethe logarithm for alkali-metal atoms. The results are critically compared with data available in the contemporary literature, and the strengths and limitations of the present approach are discussed.

The third and fourth density and acoustic virial coefficients of neon were determined at temperatures between 10 and 5000 K from first principles employing the path-integral Monte Carlo (PIMC) approach. For these calculations, we used the pair potential of Hellmann $\textit{et al.}$ [J. Chem. Phys. 154, 164304 (2021)], which is based on supermolecular $\textit{ab initio}$ calculations with basis sets of up to octuple-zeta quality and levels of theory up to coupled cluster with single, double, triple, quadruple, and perturbative pentuple excitations [CCSDTQ(P)]. The potential also accounts for relativistic, retardation, and post-Born$-$Oppenheimer effects and is provided with reliable uncertainty estimates. To incorporate nonadditive interactions, we developed a nonadditive three-body potential based on extensive supermolecular CCSD(T), CCSDT, and CCSDT(Q) calculations with basis sets of up to sextuple-zeta quality. This potential also accounts for relativistic effects. The very small nonadditive four-body contributions to the fourth virial coefficients were considered using a relatively simple nonadditive four-body potential based on supermolecular CCSD(T) calculations. We calculated the third and fourth density and third acoustic virial coefficients directly by PIMC and the fourth acoustic virial coefficient indirectly using thermodynamic relations between the density and acoustic virial coefficients. The uncertainties of the pair potential and those estimated for our nonadditive three-body potential were rigorously propagated in the PIMC calculations into uncertainties for the virial coefficients. These uncertainties are distinctly smaller than those of almost all of the corresponding experimental virial coefficient data.

This paper examines the connection between photonic band-gap formation in two types of two-dimensional photonic crystals and the emergence of reverse electromagnetic energy flows generated by linearly polarized plane waves incident on a photonic-crystal slab. We show that these reverse energy flows, observed in both transmitted and reflected fields, originate from vortex structures in the Poynting vector. The resulting energy-flow patterns exhibit striking analogies to vortex formation in fluid motion past obstacles. The geometry and dynamics of the Poynting-vector vortices determine whether the incident electromagnetic energy is impeded, leading to the formation of photonic band gaps, or instead guided through the structure, enabling transmission.

Fewest-switches surface hopping (FSSH) is the most popular method for simulating photochemical processes of molecular systems. Recently, we have constructed long short-term memory (LSTM) networks as a propagator for electronic subsystems in FSSH dynamics simulations. The collective results on Tully's three models have been reproduced satisfactorily. In the present work, we develop an extended LSTM-FSSH framework to simulate realistic photochemical reactions. The input features of LSTM as well as the training procedure are redesigned to represent high-dimensional nuclear degrees of freedom in an effective way. Equivariant neural networks are integrated with LSTM to build adiabatic potential energy surfaces in ground and excited states. Photoisomerizations of $\mathrm{CH_2NH}$ and azobenzene are simulated, showing that our new proposed LSTM-FSSH method can produce excited-state lifetimes and product yields accurately in comparison with conventional FSSH simulations as reference. Only 10 reference trajectories are required for training LSTM networks, and then a trajectory ensemble can be generated with very efficient LSTM-FSSH dynamics simulations to obtain collective results.

Deep learning has emerged as a promising paradigm for spatio-temporal modeling of fluid dynamics. However, existing approaches often suffer from limited generalization to unseen flow conditions and typically require retraining when applied to new scenarios. In this paper, we present LLM4Fluid, a spatio-temporal prediction framework that leverages Large Language Models (LLMs) as generalizable neural solvers for fluid dynamics. The framework first compresses high-dimensional flow fields into a compact latent space via reduced-order modeling enhanced with a physics-informed disentanglement mechanism, effectively mitigating spatial feature entanglement while preserving essential flow structures. A pretrained LLM then serves as a temporal processor, autoregressively predicting the dynamics of physical sequences with time series prompts. To bridge the modality gap between prompts and physical sequences, which can otherwise degrade prediction accuracy, we propose a dedicated modality alignment strategy that resolves representational mismatch and stabilizes long-term prediction. Extensive experiments across diverse flow scenarios demonstrate that LLM4Fluid functions as a robust and generalizable neural solver without retraining, achieving state-of-the-art accuracy while exhibiting powerful zero-shot and in-context learning capabilities. Code and datasets are publicly available at https://github.com/qisongxiao/LLM4Fluid.

While language progresses through a sequence of semantic states, the underlying dynamics of this progression remain elusive. Here, we treat the semantic progression of written text as a stochastic trajectory in a high-dimensional state space. We utilize Allan deviation, a tool from precision metrology, to analyze the stability of meaning by treating ordered sentence embeddings as a displacement signal. Our analysis reveals two distinct dynamical regimes: short-time power-law scaling, which differentiates creative literature from technical texts, and a long-time crossover to a stability-limited noise floor. We find that while large language models successfully mimic the local scaling statistics of human text, they exhibit a systematic reduction in their stability horizon. These results establish semantic coherence as a measurable physical property, offering a framework to differentiate the nuanced dynamics of human cognition from the patterns generated by algorithmic models.

We propose a hybrid semi-Lagrangian scheme for the Vlasov–Poisson equation that combines the Numerical Flow Iteration (NuFI) method with the Characteristic Mapping Method (CMM). Both approaches exploit the semi-group property of the underlying diffeomorphic flow, enabling the reconstruction of solutions through flow maps that trace characteristics back to their initial positions. NuFI builds this flow map iteratively, preserving symplectic structure and conserving invariants, but its computational cost scales quadratically with time. Its advantage lies in a compact, low-dimensional representation depending only on the electric field. In contrast, CMM achieves low computational costs when remapping by composing the global flow map from explicitly stored submaps. The proposed hybrid method merges these strengths: NuFi is employed for accurate and conservative local time stepping, while CMM efficiently propagates the solution through submap composition. This approach reduces storage requirements, maintains accuracy, and improves structural properties. Numerical experiments demonstrate the effectiveness of the scheme and highlight the trade-offs between memory usage and computational cost. We benchmark against a semi-Lagrangian predictor-corrector scheme used in modern gyrokinetic codes, evaluating accuracy and conservation properties.

A major issue in exploiting plasmonic cavities as key components in nanotechnology is the effect of radiative and absorption losses on their electrodynamic behavior. Treating them as open-systems, we derive a time-local, completely positive master equation that retains non-secular interference between decay pathways and reduces to the standard secular description when the environment resolves polariton splitting. When it does not, the theory predicts order-one deviations from secular leakage dynamics, including bath-induced coherences and stabilization of dark polaritons, and provides a simple design criterion based on the ratio of polariton splitting to reservoir linewidth. A time-resolved leakage measurement, such as transmission, reflectivity, or photoluminescence, can be used to observe these effects.

Fluid mixtures that exhibit an azeotrope cannot be purified by simple bulk distillation. Consequently, there is strong motivation to understand the behavior of azeotropic mixtures under confinement. We address this problem using an ML-enhanced classical density functional theory (cDFT) applied to a binary Lennard-Jones mixture that exhibits azeotropic phase behavior. As proof-of-principle of a "train once, learn many" strategy, our approach combines a neural functional trained on a single-component repulsive reference system with a mean-field treatment of attractive interactions, inspired by the connection between cDFT and local molecular field theory. The theory faithfully describes capillary condensation and results from grand canonical Monte Carlo simulations. Moreover, by taking advantage of a known accurate equation of state, the "neural LMFT" we present well-describes bulk thermodynamics by construction. Exploiting the computational efficiency of neural LMFT, we systematically evaluate adsorption selectivity across a wide range of compositions, pressures, temperatures, and wall-fluid affinities. In cases where the wall-fluid interaction is the same for both species, we find that the pore becomes completely unselective at the bulk azeotropic composition. Strikingly, this unselective point persists far from liquid-vapor coexistence, including in the supercritical regime. Analysis of the bulk equation of state across a wide range of thermodynamic state points shows that the azeotropic composition coincides with equal partial molar volumes and an extremum in the isothermal compressibility. A thermodynamic analysis demonstrates that unselective adsorption corresponds to an aneotrope (a point of zero relative adsorption) and an extremum in the interfacial free energy. We also find that the two interfaces of the slit pore behave independently down to remarkably small slits.

We investigate the microwave-power dependence of magnon-photon coupling in a yttrium iron garnet-sphere/split-ring-resonator hybrid system at room temperature and demonstrate that nonlinear spin-wave interactions suppress the coupling through power-induced dissipation of magnetostatic modes. At low microwave power, the modes exhibit pronounced level repulsion, evidencing strong coupling to the microwave field. As the power increases, however, magnon linewidth broadening progressively weakens the coupling and ultimately suppresses it entirely below a threshold external magnetic field. We show that this behavior originates from Suhl's first-order instability: magnetostatic modes, which couple to the resonator, parametrically excites two counter-propagating magnons at half its frequency, causing modes below the threshold external magnetic field to vanish. In contrast, magnon modes above the threshold field remain robust even at high power, as the instability criterion is not satisfied in that regime. These results reveal a well-defined nonlinear boundary for magnon-photon coupled systems and highlight a favorable regime for exploiting nonlinear magnonics for frequency conversion, switching, and other functional magnonic devices.

This work conducts a three-dimensional (3D), nonlinear magnetohydrodynamic (MHD) simulation to investigate wave generating, time-dependent reconnection around a magnetic null point. A non-periodic perturbation (in the $xz$-plane) triggers oscillatory reconnection (OR) at the 3D null, resulting in a self-sustained oscillation with a constant period $P$. We investigate the response of the system using three distinct wave proxies (compressible parallel, compressible transverse and incompressible parallel) as well as Spectral Proper Orthogonal Decomposition for decoupling and analyzing the resultant MHD wave behavior. We find that OR generates a slow magnetoacoustic wave of period $P$ that propagates outwards in all directions along the spine and fan plane of the 3D null point. We also find the generation of a propagating Alfvén wave of period $P$, exclusively along the $y$-axis in the fan plane, i.e. in the direction perpendicular to the spine motion. These findings provide new insights into waves generated from a 3D null point and their implications for coronal seismology.

Monitoring and forecasting fault slip evolution are fundamental for understanding earthquake cycles and assessing future seismic hazards. This study proposes a physics-based data assimilation framework that integrates geodetic observations with fault mechanics introducing spatial heterogeneity in frictional properties, with a particular focus on short-term fault slip forecasting. The proposed method employs physics-informed neural networks (PINNs) to calculate fault slip evolutions and to optimize the spatial distribution of frictional properties and is applied to the 2010 slow slip event beneath the Bungo Channel, southwest Japan, by changing the data period to be assimilated. When only the initial phase of slip acceleration is assimilated, a velocity-weakening frictional region is inferred beneath southwest Shikoku, corresponding to the initial nucleation are of the slow slip event. Out results demonstrate that the PINN-based data assimilation framework successfully forecasts slow transient slip even when only slip acceleration data are assimilated, whereas forecasts based on frictionally homogeneous models result in unstable fast slip. This difference can be interpreted as a consequence of introducing frictional heterogeneity, which allows both the characteristic size of the slipping region and the critical nucleation size to be variable, leading to stable slip evolution consistent with observations. When longer observation periods are assimilated, a velocity-strengthening region emerges around the slip-weakening patch, progressively restricting the direction of slip propagation. This velocity-strengthening region is interpreted as a mechanical constraint imposed by fault physics, linking the slip regions required to reproduce the observed geodetic time series. The results highlight the capability of PINN-based data assimilation incorporating geodetic observations and fault mechanics.

Measurement of stray light in a complex optical system can be a complex task. We developed a method to measure coherent stray light inside an assembled device, determining all stray light sources and their relative contribution. The method is based on the insertion of a laser with a scanned frequency and the detection of all the electrical and optical signals obtained from the instrument. The spectra calculated from these signals show fringes due to interference between each stray light contribution and the nominal beam. The frequency of these interference peaks indicates the difference in path length between the stray light path and the nominal path. To have a description of the measured data we realized optical simulations, which link the measured path length to a possible path throughout the system. In the following, we will show some measurements made on a test bench realized to simulate the performance of LISA interferometers and describe how accurate simulations are in predicting and explaining the measured results.

The ability to steer polariton flow on-demand holds significant promise towards nanophotonic applications and photonic circuitry. Polariton canalization, exhibiting intrinsic collimation and diffractionless transport, emerges as a promising solution without guiding structures. However, earlier demonstrations have been restricted to certain crystal surfaces with intrinsic hyperbolic responses and operated in the linear regime. Here, we experimentally demonstrate canalization of nonlinear exciton polariton condensates with optical reconfigurability in a birefringent CsPbBr3 perovskite crystal without intrinsic hyperbolic response. By embedding the birefringent perovskite crystal into a planar microcavity, the interplay between cavity transverse-electric-transverse-magnetic splitting and crystalline birefringence produces an anisotropic band geometry with a hyperbolic-flat-parabolic evolution of polaritonic isofrequency contours (IFCs). Nonresonant pumping drives exciton polariton condensation onto flat far-field contours with nonlinear emission amplification, leading to coherent canalized flows with over twentyfold collimation with respect to arc-shaped contours. Reconfiguring the optical pumping-spot size allows switching the nonlinear polariton condensates into hyperbolic and parabolic IFC regimes, leading to divergent propagation behaviour with collimating reconfiguration. Our study reveals a distinct canalization framework for shaping the nonlinear exciton-polariton condensate flows, opening opportunities for all-optical polaritonic logic circuits based on stabilized nonlinear quantum interconnects.

Barium titanate (BaTiO$_3$) is emerging as a powerful integrated photonic material, combining strong $X^{(2)}$ and electro-optic nonlinearities with rapidly improving thin-film waveguide quality. Recent demonstrations of low-loss BaTiO$_3$ waveguides and high-Q resonators have established BaTiO$_3$-on-insulator as a promising platform for next-generation frequency-conversion and quantum photonic technologies. However, while BaTiO$_3$ electro-optic modulators are now well developed, nonlinear BaTiO$_3$ waveguide engineering remains comparatively immature. Techniques widely used in lithium niobate, such as periodic poling for quasi-phase-matching, are poorly suited to BaTiO$_3$ because epitaxial thin films exhibit high coercive fields, strong strain-clamping effects, multivariant domain structures, and slow, complex switching dynamics. These factors make accurate periodic poling challenging and hinder the development of efficient $X^{(2)}$ frequency converters. Here, we introduce a fabrication-robust alternative based on linear-nonlinear hybrid waveguides, where TiO$_2$ is selectively incorporated into BaTiO$_3$ ridge waveguides to enhance nonlinear mode overlap while relying solely on modal phase-matching. Using coupled-mode-theory simulations, we identify phase-matched geometries and show that the hybrid design achieves a 2.75x increase in normalized second harmonic generation efficiency over monolithic BaTiO$_3$ waveguides. The uniform, lithographically defined cross-section makes the approach highly scalable. These results position hybrid BaTiO$_3$-TiO$_2$ waveguides as a practical route to CMOS-compatible, high-efficiency $X^{(2)}$ devices for integrated quantum photonics.

Rigidity percolation provides an important basis for understanding the onset of mechanical stability in disordered materials. While most studies on the triangular lattice have focused on static properties at fixed bond~(site) occupation probabilities, the dynamics of the rigidity transition remain less explored. In this work, we formulate a dynamic pebble game algorithm that monitors how rigid clusters emerge and evolve as bonds are added sequentially to an empty lattice, with computational efficiency comparable to the standard static pebble game. We uncover a previously overlooked temporal self-similarity exhibited in multiple quantities, including the cluster size changes and merged cluster sizes during bond addition, as well as the number of simultaneously merging clusters. We identify large-scale cascade events in which a single bond addition triggers the merger of an extensive number of clusters that scales with system size with inverse correlation-length exponent. Using an event-based ensemble approach, we obtain high-precision estimates of the critical point $p_c = 0.660\,277\,8(10)$, the inverse correlation-length exponent $1/\nu = 0.850(3)$, and the fractal dimension $d_f = 1.850(2)$, representing substantial improvements over existing values.

We perform direct numerical simulations of rapidly rotating annular centrifugal convection to investigate how mixed (asymmetric) velocity boundary conditions and geometric curvature shape the flow organisation and heat transfer. Motivated by the quasi-two-dimensionalisation under strong rotation and the long spin-up required for large-scale states, we employ two-dimensional simulations and consider four boundary-condition sets: no-slip/no-slip (INON), no-slip/stress-free (INOS), stress-free/no-slip (ISON) and stress-free/stress-free (ISOS). For fixed geometry with the radius ratio $\eta=0.5$ and over the Rayleigh number $Ra\in[10^6,10^9]$, the heat transfer is strongest for ISOS, followed by INOS and INON, while ISON exhibits a pronounced suppression as a strong zonal flow aligned with the rotation develops. In the three cases dominated by large-scale circulation, the Nusselt number $Nu$ follows an effective classical-type scaling close to $Nu\sim Ra^{0.27}$, whereas the zonal-flow branch displays a much weaker scaling $Nu\sim Ra^{0.1}$ and strong flow anisotropy with the large difference between the radial and azimuthal Reynolds numbers $Re_r\ll Re_\varphi$. A dissipation analysis shows that zonal-flow formation is accompanied by a transition from boundary-layer-dominated dissipation to a relatively low and uniform bulk dissipation, consistent with shear-induced plume suppression. By varying the radius ratio $\eta$, we demonstrate that increasing $\eta$ weakens curvature asymmetry and destabilises the zonal-flow state, leading to roll-dominated convection in the planar limit, and we relate the accompanying bulk-temperature asymmetry to the boundary heat flux asymmetry using a free-convective boundary-layer model.

Q-learning provides a standard reinforcement learning framework for studying cooperation by specifying how agents update action values from repeated local interactions outcomes. Although previous work has shown that reputation can promote cooperation in such systems, most models introduce reputation by modifying payoffs, encoding it directly in the state or changing partner selection, which makes it difficult to isolate the role of the learning signal itself. Here, we construct the reinforcement signal as a weighted combination of reputation and game payoffs, leaving the game and network structure unchanged. We find that increasing the weight on reputation generally promotes cooperation by consolidating clusters, but this effect is conditional on the learning dynamics. Specifically, this promoting effect vanishes in two regimes: when the learning rate is extremely small, which prevents effective information propagation and when the discount factor approaches one, as distant future expectations obscure the immediate reputational advantage. Outside these limiting cases, the efficacy of reputation in promoting cooperation is attenuated by higher learning rates but amplified by larger discount factors. These results advance the understanding of cooperative dynamics by demonstrating that cooperation can be stabilized through the reputational shaping of learning signals alone, providing critical insights into the interplay between social information and individual learning parameters.

We provide a theoretical framework to describe the dynamics of a free-electron beam interacting with quantized bound systems in arbitrary electromagnetic environments. This expands the quantum optics toolbox to incorporate free-electron beams for applications in highly tunable quantum control, imaging, and spectroscopy at the nanoscale. The framework recovers previously studied results and shows that electromagnetic environments can amplify the intrinsically weak coupling between a free-electron and a bound electron to reach previously inaccessible interaction regimes. We leverage this enhanced coupling for experimentally feasible protocols in coherent qubit control and towards the nondestructive readout and projective control of the electron beam's quantum-number statistics. Our framework is broadly applicable to microwave-frequency qubits, optical nanophotonics, cavity quantum electrodynamics, and emerging platforms at the interface of electron microscopy and quantum information.

Nonlinear metasurfaces offer compact control over frequency conversion and wavefront shaping. However, existing approaches, often based on geometric phase, lack wavelength selectivity, resulting in static nonlinear responses. Here, we demonstrate a thin-film lithium niobate metasurface that enables spectrally selective shaping of second-harmonic generation through resonance-engineered phase control. The structure consists of two regions with distinct phase responses, realized via spectral tuning of Mie-type resonances. This design enables simultaneous frequency conversion and spatial mode shaping, transforming a Gaussian pump near 1100 nm into a first-order Hermite-Gaussian mode at 550 nm, while maintaining the pump profile. The demonstrated approach offers a pathway toward ultracompact and tunable components for nonlinear holography and related applications.

The origin of life is yet a compelling scientific mystery that has sometimes been attributed to high-pressure impacts by small solar system bodies such as comets, meteoroids, asteroids, and transitional objects. High-pressure torsion (HPT) is an innovative method with which to simulate the extreme conditions of astronomical impacts and offers insights relevant to prebiotic chemistry. In the present study, we investigated the polymerization and stability of adenosine monophosphate (AMP), a key precursor to ribonucleic acid (RNA), in dry and hydrated conditions (10 wt% water) under 6 GPa at ambient and boiling water temperatures. Comprehensive analyses with the use of X-ray diffraction, Raman spectroscopy, Fourier-transform infrared spectroscopy, nuclear magnetic resonance, scanning electron microscopy, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry revealed no evidence of polymerization, while AMP partly transformed to other organic compounds such as nucleobase-derived fragments of adenine, phosphoribose fragments, dehydrated adenosine, protonated adenosine, and oxidized adenosine. The torque measurements during HPT further highlight the mechanical behavior of AMP under extreme conditions. These findings suggest that, while HPT under the conditions tested does not facilitate polymerization, the formation of various compounds from AMP confirms the significance of astronomical impacts on the prebiotic chemistry of RNA on early Earth. Keywords: Ribonucleic acid (RNA), Origin of life; Phase transformations; Chemical reactions, Small solar system bodies

Machine learning (ML) force fields have emerged as a powerful tool for computing materials properties at finite temperatures, particularly in regimes where traditional phonon-based perturbation theories fail or cannot be extended beyond the harmonic approximation. These approaches offer accuracy comparable to ab initio molecular dynamics (MD), but at a fraction of the computational cost. However, their reliability critically depends on the quality and representativeness of the training data. In particular, static training datasets often lead to failure when the force field encounters previously unseen atomic configurations during MD simulations. In this work, we present a framework for dynamically training ML force fields and demonstrate its effectiveness across materials with varying degrees of anharmonicity, including cubic boron arsenide (c-BAs), silicon (Si), and tin selenide (SnSe). Our method builds on the conventional lattice dynamics expansion of total energy and incorporates Bayesian error estimation to guide adaptive data acquisition during simulation. Specifically, we show that trajectory-averaged Bayesian errors enable efficient and targeted exploration of the configuration space, significantly enhancing the robustness and transferability of the resulting force fields. We further demonstrate how Bayesian error estimation can be applied to determine the convergence of the dynamic training without requiring additional ab initio data. This proposed framework offers a practical and easily implementable scheme to improve the training process, which is the most critical step in developing reliable ML force fields.

We present a deterministic optimization framework for Neural Network Quantum States (NQS) designed to bypass the sampling variance and slow mixing issues inherent in stochastic optimization. By projecting a neural backflow ansatz onto dynamically evolving configuration subspaces and applying a post-hoc second-order perturbative correction, our method provides a systematic route for optimizing the selected variational component of the wavefunction and estimating residual correlation through a post-hoc perturbative correction. The implementation utilizes a hybrid CPU-GPU architecture that shows empirical sub-linear wall-time scaling with respect to the subspace size over the tested range, enabling the calculation of strongly correlated systems, such as the chromium dimer, within Hilbert spaces of $10^{23}$ configurations. Benchmarks on molecular bond dissociations demonstrate that this deterministic approach yields stable convergence and accuracies comparable to selected reference methods in the tested systems.

Layered NbOX$_2$ ($X=\mathrm{Cl,\,Br,\,I}$), a member of the van der Waals ferroelectric family, exhibits intrinsic ferroelectricity and pronounced nonlinear optical responses, making it a promising candidate for integrated nanophotonics applications. While previous studies have emphasized the material's strong second-order nonlinear responses, higher-order nonlinear responses are still mostly unexplored. This work systematically investigates NbOI$_2$ using high harmonic generation (HHG) spectroscopy. Driven by an intense mid-infrared laser field centered at $\sim4~\mu\mathrm{m}$ wavelength, highly anisotropic odd- and even-order harmonics up to the 16th order are generated at a low peak intensity of $0.4~\mathrm{TW\,cm^{-2}}$, extending beyond the material's bandgap. Both bulk and flake forms of NbOI$_2$ display pronounced harmonic emission from the near-infrared to the deep-ultraviolet spectral region, with a notably high overall conversion efficiency compared to other known materials. Polarization-resolved measurements reveal that even-order harmonics remain aligned with the crystal polar axis regardless of the driving-field orientation, whereas odd-order harmonics are dynamically affected. First-principles calculations suggest that the flat valence band associated with Peierls dimerization enhances HHG efficiency through electron correlation. These findings provide fresh perspectives on HHG in van der Waals ferroelectric materials and facilitate the development of compact and tunable quantum light sources.