Optics
Tunable Mid-Infrared Chiral Selective Absorber Based on Asymmetric V-Shaped Metasurfaces Driven by Chiral quasi-Bound State in the Continum
Yuqing Liu, Yi Wang, Ruihan Ma et al. (+3 more)
Efficient discrimination of circularly polarized light (CPL) is of great significance in photonics. In this study, we propose a nanophotonic device based on asymmetric V-shaped metasurfaces that exhibits strong discrimination between left- and right-circularly polarized (LCP and RCP) light at the resonant wavelengths of the absorption spectra. The
chiral-selective response originates from a quasi-bound state in the continuum (qBIC) mode enabled by controlled
symmetry breaking in the V-shaped structure, which exhibits high absorption under LCP illumination while the resonance is strongly suppressed under RCP incidence, leading to a large absorption contrast between the two circular polarizations. This significant absorption difference enables highly efficient
chiral discrimination. Furthermore, the resonant wavelength can be broadly tuned from 5200 to 6200 nm by scaling the structural dimensions without compromising the high absorption contrast between LCP and RCP. This work not only enables high-performance
chiral detection and separation, but also offers valuable guidance for reconfigurable
chiral nanodevices, with promising applications in areas such as enantiomer sensing,
chiral imaging, and CPL spectroscopy.
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All authors Yuqing Liu, Yi Wang, Ruihan Ma, Nianzhao Wang, Mengtao Sun, Yuqing Cheng
Applied Physics
Unravelling Challenges in Heating Power Measurements for Magnetic Hyperthermia – the RADIOMAG Round Robin Study Revisited
Lise G. Hanson, Daniel Ortega and Cathrine Frandsen
Non-adiabatic AC calorimetry is the most widely used technique for estimating the heating power of
magnetic nanoparticles in
magnetic hyperthermia. However, it is prone to systematic errors which lead to a standard deviation in the intrinsic loss power (ILP) of approximately 30-40%, as revealed by the RADIOMAG EU COST Action TD1402 round-robin study involving 21 European laboratories. In this study, we re-examine the RADIOMAG
dataset to both uncover previously unreported instrumentation issues, and to explore more deeply some of the reported instrumentation issues. We identify four common sources of error: i) Insufficient temperature resolution, ii) AC-field sensitive thermometers, iii) Non-physical temperature oscillations, and iv) Apparent non-linear heat loss. Based on these findings, we propose criteria for sufficient measurement quality and apply them to re-estimate the ILP values. These results have a standard deviation of 18-30%., demonstrating that addressing instrumentation and analysis issues can improve measurement reliability and decrease the inter-laboratory deviation by up to 38%. When re-estimating ILP, we used the initial slope method, arguing that the corrected slope method, which was previously used to investigate the RADIOMAG data, could introduce misleading interpretations of systematic ILP deviations due to sub-optimal measurement conditions and the unnoticed influence of non-linear heat losses. However, we emphasise that the corrected slope method is preferred, given a linear heat loss. Based on our analysis, we introduce a diagnostic protocol by using slope curves - a simple yet effective plot type - for identifying and solving common instrumentation challenges proactively before the data acquisition phase.
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Atmospheric and Oceanic Physics
Leveraging MTG-FCI fire observations for event-based fire behavior monitoring from near-real-time operation to seasonal analysis
Ronan Paugam, Jean-Baptiste Filippi, Akli Benali et al. (+10 more)
Wildfire monitoring and suppression require timely information on fire behavior, including fire energy release and rate of spread, to support operational decision-making and resource allocation. Active fire products from the Flexible Combined Imager (FCI) aboard the geostationary Meteosat Third Generation (MTG) satellites provide 10-min observations over Europe and Africa. Deriving fire behavior information from these observations requires associating individual hotspot detections into coherent fire events. We present a Fire Event Tracker (FET)
algorithm that performs spatio-temporal clustering of hotspot detections from the LSA-SAF FCI active fire product. The
algorithm assigns persistent identifiers to fire events and updates their geometry, fire radiative power, and rate of spread at each 10-min interval. The same parameterization is used for both near-real-time and retrospective processing. FET was applied retrospectively to the Mediterranean FCI hotspot archive of 2025 and operationally in two near-real-time contexts: wildfire monitoring in Portugal and support of the 2025 SILEX airborne campaign within the EUBURN project, where besides fire monitoring, FET products were also used to initialize coupled FOREFIRE-MesoNH simulations for plume
forecasting. Results show that event-based clustering of FCI active fire detections provides a consistent description of fire
evolution, enabling both tactical wildfire management and high-frequency seasonal fire analyses.
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All authors Ronan Paugam, Jean-Baptiste Filippi, Akli Benali, Jorge Gomes, Weidong Xu, Emanuel Dutra, Francois Andre, Damien Boulanger, Vianney Retornard, Andrea Meraner, Julia Harvie, Victor Penot, Cyrielle Denjean
Chemical Physics
Quantum computing for accurate large-scale electronic-structure calculations: DFT-embedded, post-processed quantum-selected configuration interaction
Tuan Minh Do, Yuichiro Yoshida, Tomoya Shiota et al. (+1 more)
We present a multilevel
embedding framework for
quantum chemistry calculations on a
quantum computer. In our framework, a
quantum algorithm treats the strongly correlated active space, while a high-level wave-function method such as coupled cluster theory or multireference perturbation theory recovers the remaining correlation in the surrounding region. A sampling-based
quantum algorithm,
quantum-selected configuration interaction, bridges the
quantum and classical treatments. The entire calculation is embedded in a low-cost density functional theory description of the surrounding environment using Manby's projection technique. We apply the framework to organic, metal-organic, and metallic systems, computing bond dissociation energies, adsorption energies, and reaction barriers using only the subset of
qubits of a 144-
qubit superconducting
quantum computer at the University of Osaka and achieving $\sim$1 kcal/mol agreement with classical references for a Menshutkin $\mathrm{S_N2}$ reaction inside a carbon nanotube. Our results may open the way to quantitatively reliable
quantum-classical hybrid calculations for large-scale chemical systems.
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All authors Tuan Minh Do, Yuichiro Yoshida, Tomoya Shiota, Wataru Mizukami
Atomic and Molecular Clusters
Influence of DFT Functionals on Low-Energy Electron Scattering Cross Sections of Nitric Oxide
Ashutosh Yadav, Felipe Fantuzzi, Nigel J. Mason et al. (+1 more)
Nitric
oxide (NO) is important in biological, atmospheric,
plasma, industrial, and astrophysical environments, where reliable electron-collision data support modelling charged-particle interactions with matter. Its well-known experimental properties make it suitable for assessing how the target electronic-structure description affects low-energy electron scattering calculations. In this work, NO properties were evaluated using B3LYP, M06-2X, PBE0, and $\omega$B97X-D3, with basis sets ranging from minimal to quadruple-
zeta quality. Bond length, dipole moment, ionisation potential, and polarisability were compared with experiment to assess the sensitivity of the target description to the functional and basis set. The aug-cc-pVQZ basis set was then used to generate target models for ab initio R-
matrix calculations over 0.1–20 eV. The total cross sections show low-energy resonance features, with the strongest functional dependence around the broad peak near 0.8–1.0 eV. A sharper, higher-energy structure is also observed below 2 eV, shifting from 1.74 to 1.82 eV depending on the functional. Differential cross sections show modest functional sensitivity, with more noticeable angular differences at 7.5 and 10 eV. These results show that the DFT functional and basis set affect the target properties, with the resulting target description influencing low-energy electron-scattering observables of NO. The comparison supports $\omega$B97X-D3/aug-cc-pVTZ geometry optimisation followed by aug-cc-pVQZ target-property calculations as a practical protocol for R-
matrix modelling of NO.
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All authors Ashutosh Yadav, Felipe Fantuzzi, Nigel J. Mason, Bobby Antony
Chemical Physics
A Comparative Study of Exponential Sum-Connectivity and Product-Connectivity Gourava Indices for Benzenoid Hydrocarbons
H. M. Nagesh, B. Azghar Pasha, U. Vijaya Chandra Kumar et al. (+1 more)
In this work, the exponential sum-connectivity Gourava index ($e^{SGO(G)}$) and the exponential product-connectivity Gourava index ($e^{PGO(G)}$) are computed and comparatively analyzed for benzenoid hydrocarbons. Our results demonstrate that these descriptors exhibit a strong mutual correlation and provide enhanced sensitivity in modeling the structural characteristics of molecular graphs. Regression analysis reveals that both indices are exceptionally reliable predictors of $\pi$-electronic energies, achieving correlation coefficients exceeding $0.999$. Notably, a comparative assessment indicates that the exponential product-connectivity variant offers a slightly superior fit, as its coefficients align more precisely with optimal least-squares results. These findings confirm that both exponential Gourava-based indices provide a robust framework for characterizing electronic properties, with the product-connectivity version showing particular promise for high-precision QSPR studies in benzenoid systems.
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All authors H. M. Nagesh, B. Azghar Pasha, U. Vijaya Chandra Kumar, Narahari N
Optics
Laser fractional frequency instability at $\mathbf{4\times 10^{-17}}$ with a room temperature optical reference cavity
Adam L. Parke, Eve Clulow, Wei Huang et al. (+8 more)
Ultrastable
lasers play a key role in optical frequency metrology, setting measurement speed and ultimately impacting both stability and accuracy of optical frequency standards. Here we demonstrate
laser fractional frequency instability at ${4}\times10^{-17}$ and
laser frequency linewidth of $12\,$mHz full width at half maximum, employing a 68 cm long optical reference cavity operating at room temperature. To the best of our knowledge, both frequency instability and linewidth are the lowest ever reported for a room temperature system. This work highlights that state-of-the-art frequency stability and spectral purity are achievable at room temperature, making them accessible to a broader range of users.
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All authors Adam L. Parke, Eve Clulow, Wei Huang, Namneet Kaur, Reinhard Karembera, Jacques-Olivier Gaudron, Xi Zhang, Matias Risaro, Jacob Tunesi, Henry Bourne, Marco Schioppo
Biological Physics
Emergent swimming strategies of a smart three-bead swimmer
Ruma Maity, Maximilian Huebl, Julian Lemmel et al. (+2 more)
Low-Reynolds-number microswimmers have recently attracted much interest for their ubiquity in biology and their applications in biotechnology and medicine. However, a key obstacle for the design and deployment of artificial microswimmers lies in their autonomy: to successfully perform tasks in any real-world scenario, these swimmers need to be able to interact with and adapt to their environment without external control. Here, we train a simple two-dimensional model microswimmer (consisting of three-bead) to learn autonomous swimming strategies via Reinforcement Learning, focusing on neuroevolution techniques to derive controller architectures with minimal complexity. We identify five different, characteristic swimming gaits: three of these gaits lead to directed locomotion with varying grades of efficiency and two gaits result in a rotational, inefficient movement. Remarkably, all of these gaits can be achieved by very simple neural networks (with less than ten nodes and weights), showing that low-Reynolds-number swimming can be achieved efficiently and robustly while requiring only minimal computational power. These results are of particular interest to the experimental design of artificial microswimmers and may have implications for modeling biological microorganisms such as Chlamydomonas reinhardtii.
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All authors Ruma Maity, Maximilian Huebl, Julian Lemmel, Benedikt Hartl, Gerhard Kahl
Applied Physics · Cross-listed from cond-mat.mtrl-sci
The KNN rollercoaster: from bulk ceramics to phase engineered wafer-scale thin films
Giulia Pavese, Federico Orlando, Fabio Melzi et al. (+12 more)
Since the initial disclosure of the extraordinary piezoelectric coefficients of Potassium sodium niobate (KNN) in near-equimolar bulk ceramics, its development trajectory has resembled a rollercoaster, with its integration into microelectronics severely lagging due to thermodynamic stability issues and poor planar process compatibility. In this work, we revisit the bulk-derived phase diagram for the specific case of thin films integrated on silicon. By systematically investigating Mn-doped K1-xNaxNbO3 films grown on 8-inch wafers, we demonstrate that the optimal stoichiometry for thin films fundamentally diverges from the bulk equimolar standard. A Na-rich composition (> 70 at.%) is required to overcome substrate-induced constraints, effectively suppressing pyrochlore formation and chemical phase segregation while promoting dense columnar growth with a complete (001) out-of-plane polar orientation. Consequently, Na-rich films deliver outstanding functional properties, reaching remanent polarizations up to 14 uC cm-2, with piezoelectric coefficients of d33f= 79 pm/V and e31f = 10 C/m2. Supported by Density Functional Theory simulations, we correlate this enhancement with improved stability and a strain-driven structural reorientation toward a lower-
symmetry monoclinic phase with tilted polarization. By redefining the phase engineering rules for wafer-scale thin films, our results establish a clear route toward KNN integration in microsystems.
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All authors Giulia Pavese, Federico Orlando, Fabio Melzi, Walter Piazzi, Andrea Pescarolo, Federico Maspero, Marco Asa, Riccardo Gianola, Andrea Picco, Andrea Serafini, Kui Yao, Silvia Picozzi, Laura Castoldi, Miguel-Ángel Badillo-Ávila, Riccardo Bertacco
Classical Physics · Cross-listed from math.DG
Minimal surfaces: A Lagrangian derivation of first and second variations
Romain Lloria and Boris Kolev
This article develops a rigorous Lagrangian formulation of variational calculus for minimal surfaces, using extensively the concept of pullback covariant derivative. It is shown, in particular, using a
geometric argument that all tangential variations vanish. First and second normal variations are then derived.
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Physics and Society · Cross-listed from cs.CY
Political Persuasion and Endorsement in Large Language Models
Alessia Antelmi, Alessia Galdeman, Lucio La Cava et al. (+2 more)
Large
Language Models (LLMs) are increasingly employed as proxies for human behavior in computational social science. However, their tendency to internalize biases from training data raises concerns about their reliability in politically sensitive domains, specifically regarding their endorsement of persuasive
language. In this work, we examine whether LLMs endorse persuasion-infused messages and whether partisan persona prompting modulates such endorsement. We evaluate six LLMs from different geographic regions on content annotated with persuasion techniques drawn from real-world media sources, measuring the likelihood of endorsement using a five-point Likert scale. The models are prompted as either a neutral social media user or as a user with left- or right-leaning political views. Results show that, without political conditioning, LLMs generally do not endorse messages containing persuasion techniques, although model-level differences emerge, and that partisan persona prompting increases polarization of endorsement, particularly for persuasion-infused content. Endorsement further varies across persuasion techniques and topics. These findings reveal how political conditioning influences LLMs' endorsement behavior toward persuasion-infused content, raising concerns about their deployment as
agents in politically sensitive environments and complicating their use as reliable simulators of human political cognition.
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All authors Alessia Antelmi, Alessia Galdeman, Lucio La Cava, Arianna Pera, Giovanni Da San Martino
Physics and Society
Network model selection: A review of methods
Zoran Levnajić
Understanding the processes behind the
evolution of complex networks is a key objective in network science. An effective framework for tackling this challenge is network model selection, which involves finding the model from a set of candidates that best explains a given network. This book is a systematic review of methods for this purpose. Each method is outlined in three parts: its core principle (used to organize methods into four categories), other relevant details including my own observations, and software availability. The book provides a comprehensive overview of the state-of-the-art in network model selection and concludes by exploring future directions. A unified, optimal method could identify the mechanisms that shape real-world networks more precisely than any current approach. This work represents the first step toward developing such an optimal method. It will be a valuable resource for students and researchers in network science.
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Plasma Physics
Optimization of EUV output by experimentally validated radiation-hydrodynamic simulations across a broad laser parameter space
Nozomi Tanaka, Yu Yamamoto, Akira Sasaki et al. (+7 more)
Practical requirements such as improving wall-plug efficiency and reducing system footprint have become increasingly important with the introduction of extreme ultraviolet (EUV) lithography into high-volume
semiconductor manufacturing. These demands motivate the development of solid-state mid-infrared
lasers as alternatives to current CO2
lasers. Systematic exploration of
laser-to-EUV conversion efficiency (EUV-CE) over a broad parameter space is essential when altering the drive
laser's wavelength, because the EUV-CE depends on the
laser parameters in a complex manner. In this work, we performed a large-scale grid search of more than 140,000 parameter combinations for
laser-produced tin
plasma EUV sources using the radiation-hydrodynamics code
STAR-1D, which is validated against EUV source experiments. The systematic wavelength dependence of the optimum pulse width and target size is governed by the requirement to simultaneously achieve the electron temperature and density optimal for EUV emission, maintain efficient
laser absorption, and suppress EUV self-absorption. The resulting CE map predicts a global maximum of 5.63% at 5.5 {\mu}m. For the practically relevant 2 {\mu}m solid-state driver, a maximum CE of 4.64% is obtained, in good agreement with recent experimental results. Multiple operating points are identified over a broad range of pulse parameters, providing guidance for 2 {\mu}m-driven EUV source development.
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All authors Nozomi Tanaka, Yu Yamamoto, Akira Sasaki, Katsunobu Nishihara, Atsushi Sunahara, Tomoyuki Johzaki, Yuji Takagi, Kentaro Tomita, Shinsuke Fujioka, Masashi Yoshimura
Data Analysis and Statistics
The BRUIT-FM "open data" noise reduction challenge
Wayne C Crawford, Stephan Ker, Simon Rebeyrol et al. (+1 more)
The BRUIT-FM Challenge asks participants to reduce ”noise” — defined as any signal that masks the signal of interest — on an open
dataset of real and synthetic broadband seafloor seismology data. The
dataset includes signals from earthquakes, ocean infragravity waves and seafloor currents. It also includes training
datasets of synthetic data with reference solutions. Participants are asked to remove or separate these signals to obtain the best earthquake and seafloor compliance signals. The authors will make a first assessment of the submissions, based on noise reduction, signal deformation and ease of use. Participants are invited to a community workshop, in which they will validate the preliminary assessment and collaborate on a community paper describing the results, identifying promising pathways and proposing new directions. The best results will be added to a new version of the
dataset, which will persist as a
benchmark for future developments.
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All authors Wayne C Crawford, Stephan Ker, Simon Rebeyrol, Laurent Duval
Fluid Dynamics
High-order thermodynamic nonequilibrium in three-dimensional compressible flows: Kinetic moment closure and multigradient coupling
Huilin Lai, Qinghong Guo, Yanbiao Gan et al. (+3 more)
High-order thermodynamic nonequilibrium (TNE) in three-dimensional compressible flows reflects the breakdown of low-order kinetic moment closure in strong-gradient regions. Using Chapman-Enskog analysis, we identify the kinetic moment constraints required to describe third-order TNE. The analysis yields the third-order constitutive relations and
evolution equations for the viscous stress and heat flux, together with second-order expressions for their associated higher-order fluxes. These constraints enable the construction of a three-dimensional super-Burnett-level discrete Boltzmann model with 91 discrete velocities. The resulting D3V91 model reproduces shock-tube wave structures and resolves high-order TNE contributions that lower-order DBMs do not capture reliably. These results demonstrate that high-order TNE has a multigradient, rather than single-gradient, origin. For the four TNE quantities considered here, odd-order central moments, including the heat flux and the viscous-stress flux , are primarily governed by temperature gradients, whereas even-order central moments, including the viscous stress and the heat-flux-related flux , are dominated by velocity gradients. These leading-gradient dependences are not exclusive; they are substantially modified by density gradients, secondary gradients and transition-layer widths through higher-order derivative terms, gradient products and cross-couplings. When the secondary contributions become comparable to the leading-gradient terms, the nonequilibrium response transitions from a near-linear regime to an approximately exponential regime. This work establishes a super-Burnett-level DBM framework that treats kinetic moment closure and multigradient coupling consistently, providing a basis for resolving and interpreting high-order TNE in three-dimensional compressible flows.
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All authors Huilin Lai, Qinghong Guo, Yanbiao Gan, Bin Yang, Hailong Liu, Pengfei Lin
Plasma Physics
Coupled simulation of plasma-surface interactions during early stages of vacuum arcing
Roni Koitermaa, Andreas Kyritsakis, Tauno Tiirats et al. (+2 more)
We describe fully coupled simulations that bridge atomistic cathode dynamics and
plasma formation during the earliest stages of
vacuum arcing. The model combines molecular dynamics, finite element electrothermal calculations, electron emission and particle-in-
cell plasma simulations via dynamic transfer of particles between the surface and
plasma domains. Simulations of Cu nanoprotrusions reveal two routes to thermal runaway: direct Joule heating-driven instability and a novel nanoparticle-assisted mechanism, where detached nanoparticles generate neutral vapor that becomes ionized.
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All authors Roni Koitermaa, Andreas Kyritsakis, Tauno Tiirats, Flyura Djurabekova, Veronika Zadin
Optics
Single-Frequency Symmetry-Empowered Through-Barrier Sensing in Reconfigurable Complex Media
Shuai S. A. Yuan, Viktar Asadchy and Philipp del Hougne
Mirror
symmetry can strongly enhance the transmission of waves through a barrier inside a complex medium. We recently showed that this phenomenon enables quantitative through-barrier sensing: by tuning programmable scatterers on one side of the barrier to maximize the broadband total transmission through the barrier, the characteristics of scatterers at mirror-symmetric positions on the other side of the barrier can be determined. Considering a sufficiently large bandwidth was crucial to ensure that no accidental narrowband asymmetric resonance can outperform the
symmetry-induced transmission enhancement. Here, we overcome this scheme's vexing need for a large bandwidth by replacing the underlying frequency diversity with configurational diversity. Specifically, we introduce auxiliary tunable scatterers at mirror-symmetric positions on either side of the barrier and sweep their characteristics through a series of random mirror-symmetric configurations. We tune the programmable main scatterers on one side of the barrier to maximize the average of the total through-barrier transmission over a series of configurations of the auxiliary scatterers at a single frequency, in order to sense the characteristics of the main scatterers on the other side of the barrier. We systematically study the accuracy of our single-frequency sensing scheme based on a multiport-network system model that cascades two mirror-related wave-chaotic cavities with a weakly transmitting barrier in between. We further examine an extension to non-reciprocal chaotic cavities involving circulators. Altogether, our results establish configurational diversity as a route to single-frequency,
symmetry-empowered through-barrier sensing in reconfigurable complex media.
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Physics and Society · Cross-listed from cs.GT
Exploring cooperation mechanisms via deep reinforcement learning in network common-pool resource games
Yihang Qin, Jinying Zou and Lin Wang
Sustaining cooperation in resource-constrained populations requires allocation mechanisms that balance individual incentives, resource sustainability, and distributional
fairness. This paper proposes a network common-pool resource game in which individuals are embedded in complex networks, participate in multiple overlapping local resource pools, and face endogenous resource constraints during strategy
evolution. Within this framework, we first examine two representative allocation mechanisms, equal allocation and proportional allocation. The results show that equal allocation produces fair but inefficient outcomes by weakening contribution incentives, whereas proportional allocation can temporarily promote cooperation but amplifies accumulated advantages and leads to severe inequality. To overcome these limitations, we develop a graph neural network-based reinforcement learning framework in which a learned social planner allocates local pool resources without directly controlling individual strategies. Simulation results under four representative network
topologies show that the learned planner sustains higher cooperation levels and average accumulated resources, and reduces inequality compared with the baselines. Furthermore, we interpret the learned policy and distill it into two simpler mechanisms: a resource-dependent mixture mechanism for regular networks and a degree-conditioned mixture mechanism for heterogeneous networks. These mechanisms reveal that effective allocation should adapt to both local resource states and structural positions, providing an interpretable route from reinforcement learning policy search to mechanism design in networked resource-sharing systems.
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Chemical Physics · Cross-listed from cond-mat.soft
Aqueous-alcohol mixtures in dimension two: miscibility and micro-segregation
Camille de la Vaissiere, Ayse Butuner and Aurélien Perera
Two dimensional site interaction models of water and alcohols are mixed in various proportions and studied by Monte Carlo simulations, with the purpose to clarify problems related to simulation of real micro-heterogeneous systems. Three alcohols are considered, methanol, pentanol and octanol. The main finding is that, while real alcohols demix with water from butanol onward, their 2D analogs are always fully miscible, while developing increasingly pronounced micro-segregation as the alcohol tail length increases. This is not a consequence of the intrinsically higher fluctuations in 2D, but rather a reorganization of these fluctuations under the charge ordering mechanism. The second finding is that water drives the micro-segregation through strong self-aggregation, but this is not enough to achieve full phase separation because of the water-alcohol contact at the outer rim of the water domains. In this work we examine how this local heterogeneity develops with increasing alcohol alkyl tails, monitored with the study of pair correlation functions, structure factors and Kirkwood-Buff integrals. The absence of clear local self-averaging of the latter provides an illustration of the tension between energy driven maintaining of local structures and
entropy driven global homogeneity. In that, the 2D modelisation of real hydrogen bonding mixtures allows to better capture and reveal the physics behind the chemistry of these
liquids.
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Computational Physics
Towards stable and accurate electron dynamics via neural network based time-dependent variational Monte Carlo
Weizhong Fu, Zhe Li, Yubing Qian et al. (+3 more)
Real-time dynamics of interacting electrons lies at the interface between
quantum mechanics and non-
equilibrium physics, governing the microscopic origin of ultrafast phenomena of molecules and nano-materials. Though neural network variational Monte Carlo has achieved unprecedented accuracy for stationary state calculations, its extension to real-time
evolution remains challenging. In this work, we introduce the neural basis time-dependent variational Monte Carlo framework, which achieves stable and highly accurate simulations of electron dynamics. By constraining the time
evolution to a compact, customized
manifold spanned by the neural basis, we effectively bypass instability issues and achieve long-term stable
evolution. Moreover, we demonstrate that this framework yields
benchmark-quality accuracy in simulating the
laser-driven dipole responses of the hydrogen atom and a stretched hydrogen molecule, and accurately extracts the dynamic polarizabilities of helium and beryllium atoms. Our work reveals the vast potential of neural network wavefunctions for accurately describing real-time electron dynamics and establishes a promising new route for first-principles simulations of complex, time-dependent electronic phenomena.
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All authors Weizhong Fu, Zhe Li, Yubing Qian, Ruichen Li, Weiluo Ren, Ji Chen
Optics
Inverse Design of Realizable Metasurface based Absorbers using Improved Conditioning and Diversity Enhanced Progressively Growing GANs
Vineetha Joy, Mohammad Abdullah, Pramit Pal et al. (+3 more)
Metasurfaces enable precise manipulation of electromagnetic waves for applications such as beam steering, sensing, and stealth technology. However, inverse design of metasurfaces with targeted EM responses remains challenging due to the computational expense of iterative full wave simulation driven optimization and the limited conditioning fidelity and diversity of existing generative approaches. To address these challenges, this paper presents a generative inverse design framework for controllable and physically consistent metasurface synthesis under continuous spectral constraints. The proposed approach employs a progressively growing Wasserstein generative
adversarial network with gradient penalty integrated with feature wise linear modulation based conditioning for stable propagation of continuous spectral and fabrication constraints. EM consistency is embedded directly into the generative learning process through a surrogate assisted spectral
alignment loss, enabling physics constrained generation during training. Further, a determinantal point process based diversity regularization strategy is incorporated to generate geometrically diverse yet spectrally consistent realizations for the same target response. The effectiveness of the proposed framework is demonstrated through the generation of practically realizable metasurface absorbers exhibiting diverse reflection characteristics in the frequency range of 2 to 18 GHz. EM simulations validate that the generated designs meet the target specifications with high accuracy. The final proposed framework achieved an average mean squared error of 0.0052, diversity score of 0.8730, band
alignment accuracy of 0.8533, and a valid EM design generation percentage of 89.57, clearly demonstrating its capability to generate highly accurate, diverse, electromagnetically consistent and fabrication realizable metasurface configurations.
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All authors Vineetha Joy, Mohammad Abdullah, Pramit Pal, Anshuman Kumar, Amit Sethi, Hema Singh
Optics · Cross-listed from cond-mat.mes-hall
Breakdown of Fluctuational Electrodynamics in the Extreme Near Field
Philippe Ben-Abdallah
Fluctuational electrodynamics relies on the assumption that thermal fluctuations in distinct bodies are statistically independent. It is shown that this approximation breaks down in the extreme near-field regime, where hybridization of surface
phonon-polaritons across nanometric
vacuum gaps generates finite fluctuating-current cross correlations between opposite interfaces. Using a microscopic coupled-oscillator model combined with a Green-tensor formulation of the Poynting vector, the resulting correlation-induced correction to radiative heat transfer is derived. For polar materials, these correlations become significant when the hybridization energy approaches the intrinsic damping rate and can substantially modify conventional fluctuational-electrodynamics predictions at subnanometric separations.
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Optics
Design of an efficient Tunable Dual narrow-band MEMS Mid and Far IR emitter with Me-NTA for Industrial and Biomedical applications
Md. Imran Hasmi, Md. Saffat Gohor, Foez Ahmed et al. (+1 more)
Spectrally selective infrared (IR) thermal emitters are gaining much
attention now-a-days for sensing, spectroscopy and biomedical applications. In this research, two metasurface incorporated IR emitters are proposed and numerically analyzed using finite element method (FEM). First structure comprises a NiCr heater integrated with a NiCr-based metallic nanotube array (Me-NTA) metasurface to produce a single-narrowband emission in the mid-infrared (MIR) region. Furthermore, an Au-based Me-NTA metasurface on a NiCr-Au hybrid heater subsequently produces dual-narrowband emission in the short-and far-infrared (SIR and FIR) spectrums. Function of these emitters can be explained by Joule heating with the help of DC bias and consequently uniform temperature distribution can be observed along the active region. Simulation analysis shows that NiCr-metasurface based emitter produces single narrow-band near perfect emission centered at 4.5 {\mu}m in MIR region at an operating temperature of 700 K with maximum in-band conversion efficiency (CE) of 32.3% and radiated power of 199 mW. On the other hand, Au-metasurface based emitter generates dual-narrowband emission peaking at 2.5 {\mu}m and 10 {\mu}m, correlating to SIR and FIR subsequently, achieving maximum emission of 93% and 85%, respectively. The in-band CE for this emitter attains 10.4% and 4.4% in the first and second bands, associated with radiated powers of 350 mW and 147 mW, accordingly. Furthermore, execution of the emitter at 500 K reveals FIR emission with reduced power consumption. These results substantiate the possibilities of the suggested emitters in various industrial and biomedical applications.
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All authors Md. Imran Hasmi, Md. Saffat Gohor, Foez Ahmed, Jaker Hossain
Physics and Society
On Leadership Emergence in Opinion Dynamics on Social Networks
Martina Alutto, Lorenzo Zino, Karl H. Johansson et al. (+1 more)
Leadership in social groups emerges dynamically through interaction and opinion exchange. Empirical evidence indicates that individuals expressing strong opinions tend to gain influence, while sustained leadership critically depends on maintaining
alignment with the surrounding social context. Motivated by these observations, we introduce a coupled dynamical model describing the simultaneous
evolution of opinions and leadership in a networked population. Extending the Friedkin-Johnsen framework, we represent leadership as a time-varying susceptibility to social influence, which evolves according to a game-theoretic mechanism, consistent with social psychology evidence. Within this setting,
agents strengthen their leadership by expressing decisive yet socially coherent opinions, whereas misalignment with the collective state results in a loss of influence. We analyze the coupled dynamics and establish sufficient conditions to identify which
agents necessarily emerge as leaders and which act as followers in the social network.
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All authors Martina Alutto, Lorenzo Zino, Karl H. Johansson, Angela Fontan
Fluid Dynamics
Sub-Kolmogorov Intermittency and Multifractal Dissipation in Multiphase Turbulence
Marco Crialesi-Esposito, Alienor Riviere and Sergio Chibbaro
Multiphase
turbulence displays stronger intermittency than its single-phase counterpart, yet the origin and geometrical organization of its most intense small-scale fluctuations remain poorly understood. Using direct numerical simulations of the incompressible Navier–Stokes equations with surface tension, we show that the local dissipative cutoff broadens strongly in the presence of interfaces, with dissipative events extending deep into the sub-Kolmogorov range. These events are spatially concentrated around
topology-changing interfacial regions, namely breakup and coalescence. A multifractal analysis of the dissipation field further reveals that, while the spectrum above the Kolmogorov length, $\eta_K$, remains close to the single-phase case except for the most singular tail, the near- and sub-Kolmogorov range develops a markedly broader singularity spectrum supported on sparse intense structures. Our results show that breakup and coalescence do not simply perturb
turbulence locally, but imprint a distinct multifractal organization on dissipation in multiphase
turbulence.
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Fluid Dynamics
Stochastic Multiscale Reconstruction of Lagrangian Turbulence via Guided Diffusion Models
Conghui Wang, Tianyi Li, Luca Biferale et al. (+3 more)
Lagrangian
turbulence is characterized by intermittent, fat-tailed fluctuations and nontrivial correlations across temporal scales, making a quantitative description of its full multiscale probability distribution a longstanding challenge. A particularly important question is whether unresolved fine-scale fluctuations can be inferred from coarse-grained trajectory information. Here, we address this problem by sampling the conditional distribution of unresolved fluctuations using a
diffusion-model prior conditioned on large-scale dynamics obtained through a wavelet-based coarse-graining of Lagrangian trajectories. Using tracer trajectories from direct numerical simulations of homogeneous and isotropic
turbulence at $Re_\lambda \simeq 310$, we show that the reconstructed signals recover scale-dependent intermittent statistics, including high-order structure functions, flatness, and local scaling exponents, together with cross-scale temporal correlations between resolved and unresolved fluctuations. The method also reproduces the broad
stochastic variability of intermittent acceleration fluctuations conditioned on the same coarse-grained trajectory, whereas Gaussian-process reconstructions in wavelet representation suppress rare events. Our results show that small-scale Lagrangian intermittency can be modeled as a non-Gaussian conditional
stochastic process constrained by coarse-scale dynamics and quantitatively reproduced through data-driven generative sampling.
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All authors Conghui Wang, Tianyi Li, Luca Biferale, Qinmin Zheng, Michele Buzzicotti, Fabio Bonaccorso
Optics
Optical chirality of membrane metasurfaces with broken in-plane symmetry
Ivan Toftul, Brijesh Kumar and Yuri Kivshar
We study chiroptical properties of single-layer dielectric membrane metasurfaces with broken in-plane
symmetry. Although
chiral optics was long tied to structures lacking any mirror
symmetry, planar (2D)
chiral structures that retain a horizontal mirror plane now attract growing
attention for their strong
chiral optical effects. We explicitly show and explain how single-layer metasurfaces can achieve strong resonant
chiral response. Although the single-layer geometry forbids conventional co-polarized circular dichroism, strong resonant conversion circular dichroism appears to be possible in particular wavelength ranges determined by the spectra of photonic eigenmodes. We explore its origin starting with a $C_4$ rotation-symmetric and in-plane mirror-symmetric membrane metasurface and applying to it various in-plane perturbations. Simultaneous breaking of the in-plane mirror
symmetry and lifting the rotation
symmetry unlocks resonantly enhanced circular conversion dichroism. We derive selection rules for this effect and trace its origin to eigenmode interference and intercoupling using
chiral coupled-mode theory. Material absorption weakens the conversion dichroism produced by modes of opposite up-down parity, but it is essential for that produced by modes of the same parity.
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Applied Physics
Electrolyte Bonding Engineering for Highly Uniform GeTe-based CBRAM and Parallel Hebbian Learning in Selector-free Hopfield Networks
Jiin Bang, Jingyeong Hwang, Unhyeon Kang et al. (+10 more)
Hopfield networks offer a hardware-friendly framework for energy-efficient associative
memory, yet their practical realization in memristor crossbar arrays is critically hindered by device-to-device (D2D) variability, which prevents reliable parallel programming. Here, we address this bottleneck through systematic composition engineering of the Ge-Te solid electrolyte in conductive bridge random access
memory (CBRAM) devices. By varying the Ge:Te ratio, we identify Ge3.5Te1 as an optimal electrolyte composition that suppresses
stochastic resistance variation by approximately three orders of magnitude compared to GeSe-based devices. Raman spectroscopy reveals that this dramatic improvement originates from a bonding network dominated by asymmetric-stretching GeTe4 tetrahedral units, which form interconnected free-volume channels that confine and stabilize Cu+ ion migration pathways. Leveraging this enhanced uniformity, we fabricate a selector-less 16x16 Cu/Ge3.5Te1 CBRAM crossbar array and demonstrate a 4x4 Hopfield associative network capable of learning and recalling binary pattern pairs via fully parallel programming using a half-selection scheme. Successful pattern recall is achieved for up to two stored associations despite the absence of selector elements, establishing a proof-of-concept for selector-free hardware implementations of associative
memory. These results highlight the critical role of electrolyte bonding structure in determining memristor uniformity and provide a materials-driven pathway toward scalable, parallel neuromorphic computing systems.
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All authors Jiin Bang, Jingyeong Hwang, Unhyeon Kang, Seungmin Oh, Kyungmin Lee, Jaehyun Park, Younghyun Lee, Hyun Jae Jang, Seongsik Park, YeonJoo Jeong, Inho Kim, Jong Keuk Park, Suyoun Lee
Geophysics
Multi-Condition Guided Diffusion Model for Controllable Elastic Parameter Synthesis
Hongling Chen, Qi Pang, Chuangji Meng et al. (+2 more)
Prestack elastic parameter inversion is important for reservoir characterization and quantitative seismic interpretation. Most existing deep-learning-based methods have achieved promising results, but they generally require sufficient labeled training data and have limited flexibility in integrating multi-source conditioning information. To address this issue, we propose a multi-condition guided
diffusion model for controllable elastic parameter synthesis. Elastic parameter training
datasets are first constructed based on well log statistics and geological characteristics of the target area and are used to train the
diffusion model. A unified multi-condition guided
diffusion framework is then developed to incorporate both implicit and explicit conditioning information. Specifically, iterative latent variable refinement, Adapter-based conditioning, and a
diffusion posterior sampling (DPS)-projection guidance strategy are introduced for implicit model-domain constraints, implicit structural constraints, and explicit conditioning-operator constraints, respectively. Synthetic examples demonstrate that the proposed method can generate elastic parameter samples that are consistent with the prescribed conditions under both single-condition and multi-condition guidance. When seismic data are used as conditioning information, the framework can be further adapted to seismic elastic parameter inversion. Experiments show that the proposed method improves the prediction of representative elastic parameters, including P-wave velocity, S-wave velocity, and density, compared with baseline methods. The synthesized samples can also support downstream deep-learning-based inversion under limited labeled data, achieving competitive performance.
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All authors Hongling Chen, Qi Pang, Chuangji Meng, Shian Shen, Jinghuai Gao
Geophysics
Preparing for the Next Carrington: Spatiotemporal Agent-Based Modeling for Safeguarding Satellite Infrastructure Under Extreme Space Weather Disturbances
Rushil Kukreja, Edward J. Oughton, Phillip M. Cunio et al. (+1 more)
Extreme space weather poses an existential threat to modern satellite infrastructure, with a Carrington-class
solar storm projected to cause economic losses of billions of dollars per day. Due to the rapid proliferation of satellites (with over 70,000 expected to be deployed in the next 5 years), understanding extreme space weather impacts has become essential for global economic stability and national security, and consequently, the lives of millions. However, our current vulnerability to such events remains largely unknown, and existing models rely primarily on statistical populations instead of individual satellite behavior. Through the development of a novel spatiotemporal
agent-based model (ABM), this study addresses two critical research challenges: (1) predicting the impacts of extreme space weather disturbances and (2) enabling real-time maneuver guidance for satellites during such events. Utilizing 41,644 satellite records, historical records from 5 recent space weather events, and atmospheric density models, we built individual satellite
agents with physics-driven behaviors that make independent decisions by dynamically responding to constraints such as propellant requirements and collision avoidance thresholds. Scenario analysis suggests that 95% of satellites in Low Earth Orbit altitudes would experience enhanced atmospheric drag of 8x baseline levels, increasing collision risks by 2-3x. Monte Carlo simulations also predict direct economic impact per affected satellite on the order of $40M. Furthermore, the model successfully uses real-time conditions to provide maneuver recommendations, with 92% accuracy. This study is thus the first to provide a prototype framework for real-time adaptive decision systems to safeguard satellites against the next Carrington-class disruption.
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All authors Rushil Kukreja, Edward J. Oughton, Phillip M. Cunio, Richard Linares