Fluid Dynamics
Dynamical Characteristics of the Body-Caudal Fin Joint of a Carangiform Swimmer and its Influence on Hydrodynamics
Dev Pradeepkumar Nayak, Muhammad Saif Ullah Khalid and Ali Tarokh
The hydrodynamics of fish swimming depend on the interaction between the undulation of the body and the flapping of the caudal fin. This study develops a computational framework of a Jackfish-inspired swimmer with an independently mounted caudal fin that pitches passively under fluid forces and a nonlinear torsional spring. The fin synchronizes with the body when damping and stiffness parameters are tuned correctly, producing passive pitching that closely resembles to the displacement of the actively pitching tail. At Re = 3000, synchronized passive pitching generates coherent hairpin and ring
vortices that reinforce streamwise momentum and contribute to thrust, whereas larger phase differences lead to wake spread in lateral direction and drag-dominated behavior. These results reveal that nonlinear peduncle mechanics naturally regulate amplitude, phase, and
recoil, offering a biologically inspired pathway toward
underwater robotic design using passive kinematics.
Read the full abstract ▾
Optics
Optical Manipulation of Erythrocytes via Evanescent Waves: Assessing Glucose-Induced Mobility Variations
T. Troncoso Enríquez, J. Staforelli-Vivanco, I. Bordeu et al. (+1 more)
This study investigates the dynamics of red blood
cells (RBCs) under the influence of evanescent waves generated by total internal reflection (TIR). Using a 1064 nm
laser system and a dual-chamber prism setup, we quantified the mobility of erythrocytes in different glucose environments. Our methodology integrates automated tracking via TrackMateo̧pyright} to analyze over 60 trajectory sets. The results reveal a significant decrease in mean velocity, from 11.8 {\mu}m/s in 5 mM glucose to 8.8 {\mu}m/s in 50 mM glucose (p = 0.019). These findings suggest that evanescent waves can serve as a non-invasive tool to probe the mechanical properties of
cell membranes influenced by biochemical changes.
Read the full abstract ▾
All authors T. Troncoso Enríquez, J. Staforelli-Vivanco, I. Bordeu, M. González-Ortiz
Computational Physics
Convolutional LSTM Surrogate for Mesoscale Hydrocode Simulations of Granular Wave Propagation
Kathleen Winona Vian Martinus, Sushan Nakarmi, Dawa Seo et al. (+1 more)
Granular materials subjected to impact loading exhibit highly heterogeneous spatiotemporal dynamics governed by wave propagation, pore collapse, and grain-scale rearrangements. Mesoscale hydrocodes resolve these processes but are computationally expensive, limiting their use in parametric studies and uncertainty quantification. In this work, we develop a convolutional Long Short-Term
Memory (ConvLSTM) neural network as a spatiotemporal surrogate for mesoscale simulations of weak shock propagation in granular media. Using two-dimensional hydrocode simulations as training data, we first consider a simplified "billiard break" problem in which a cue ball impacts a cluster of nine circular balls, all deformable. Sequences of pressure-field images serve as input-output pairs for a sequence-to-sequence ConvLSTM, which is trained to predict future frames from a short history. We compare several architectures and show that a relatively compact encoder-decoder ConvLSTM accurately reproduces the propagation of the pressure wave and the resulting particle motion for an unseen combination of cue-ball position and impact velocity. As a proof-of-concept extension, we apply the same ConvLSTM framework to previously published mesoscale simulations of weak shock compaction in a granular ensemble. When evaluated at piston impact speeds that were completely withheld from training, the surrogate captures the position and shape of the compaction front and its dependence on impact speed, while smoothing fine pore-scale details in the highly compacted region as expected. These results demonstrate that ConvLSTM models can serve as satisfactory surrogates for spatiotemporal mesoscale simulations of granular wave propagation, enabling accelerated exploration of parameter space and laying the groundwork for physics-informed, mesoscale simulations of granular materials under shock loading.
Read the full abstract ▾
All authors Kathleen Winona Vian Martinus, Sushan Nakarmi, Dawa Seo, Nitin Pandurang Daphalapurkar
Computational Physics
Equivariant Interatomic Potentials without Tensor Products
Thiago Reschützegger, Sarp Aykent, Gabriel Jacob Perin et al. (+5 more)
Foundational machine-learned interatomic potentials have emerged as powerful tools for atomistic simulations, promising near first-principles accuracy across diverse chemical spaces at a fraction of the cost of
quantum-mechanical calculations. However, the most accurate equivariant architectures rely on Clebsch-Gordan tensor products whose computational cost scales steeply with angular resolution, creating a trade-off between model expressiveness and inference speed that ultimately limits practical applications. Here we introduce Geodite, an equivariant message-passing architecture that replaces tensor products while incorporating physical priors to ensure smooth, well-behaved potential energy surfaces. Trained on the Materials Project trajectories
dataset of inorganic
crystals, Geodite-MP achieves accuracy competitive with leading methods on
benchmarks for materials stability prediction, thermal conductivity,
phonon-derived properties, and nanosecond-scale molecular dynamics, while running $3\text{--}5\times$ faster than models performing similarly. By combining predictive accuracy, computational efficiency, and physicality, Geodite enables faster large-scale atomistic simulations and high-throughput screening that would otherwise be computationally prohibitive.
Read the full abstract ▾
All authors Thiago Reschützegger, Sarp Aykent, Gabriel Jacob Perin, Bruno Henrique Nunes, Flaviu Cipcigan, Rodrigo Neumann Barros Ferreira, Mathias Steiner, Fabian L. Thiemann
Physics and Society · Cross-listed from cs.DL
The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding
Yifan Qian, Zhe Wen, Alexander C. Furnas et al. (+3 more)
Federal research funding shapes the direction, diversity, and impact of the US scientific enterprise. Large
language models (LLMs) are rapidly diffusing into scientific practice, holding substantial promise while raising widespread concerns. Despite growing
attention to AI use in scientific writing and evaluation, little is known about how the rise of LLMs is reshaping the public funding landscape. Here, we examine LLM involvement at key stages of the federal funding pipeline by combining two complementary data sources: confidential National Science Foundation (NSF) and National Institutes of Health (NIH) proposal submissions from two large US R1 universities, including funded, unfunded, and pending proposals, and the full population of publicly released NSF and NIH awards. We find that LLM use rises sharply beginning in 2023 and exhibits a bimodal distribution, indicating a clear split between minimal and substantive use. Across both private submissions and public awards, higher LLM involvement is consistently associated with lower semantic distinctiveness, positioning projects closer to recently funded work within the same agency. The consequences of this shift are agency-dependent. LLM use is positively associated with proposal success and higher early-stage publication output at NIH, whereas no comparable associations are observed at NSF. Notably, the productivity gains at NIH are concentrated in non-hit papers rather than the most highly cited work. Together, these findings provide large-scale evidence that the rise of LLMs is reshaping how scientific ideas are positioned, selected, and translated into publicly funded research, with implications for portfolio governance, research diversity, and the long-run impact of science.
Read the full abstract ▾
All authors Yifan Qian, Zhe Wen, Alexander C. Furnas, Yue Bai, Erzhuo Shao, Dashun Wang
Optics
Visualization of Gaussian Mode Profile in Gigahertz Surface-Acoustic-Wave Resonators
Shizai Chu, Suraj Thapa Magar, John Nichol et al. (+1 more)
Surface-acoustic-wave (SAW) resonators operating at gigahertz (GHz) frequencies are widely used in wireless telecommunication and
quantum information processing. Successful implementation of such resonators calls for detailed microscopic understanding of their mode profiles, energy dissipation channels, and imperfections from microfabrication. In this work, we report on the visualization of acoustic waves in LiNbO3 SAW resonators by transmission-mode microwave impedance microscopy (TMIM). The Gaussian mode profile tightly confined by reflecting mirrors is vividly seen in the TMIM images, from which the linewidth of the resonator itself can be extracted. The spatially resolved acoustic profile also allows us to perform failure analysis on faulty devices. Our work establishes a pathway for further optimization of SAW resonators for classical and
quantum acoustic applications.
Read the full abstract ▾
All authors Shizai Chu, Suraj Thapa Magar, John Nichol, Keji Lai
Biological Physics
Interaction between cell membranes and protein inclusions in the large-deformation regime
Gaetano Ferraro and Michele Castellana
Biological membranes are dynamic surfaces whose shape and function are critically influenced by
protein inclusions (PIs). While membrane deformations induced by PIs have been extensively studied in the small-deformation regime, a variety of processes involves strong membrane deformations. We investigate the interaction between lipid membranes and PIs in the large deformation (LD) regime, with the finite-element method. We develop an approximate analytical solution that captures key features of the LD regime. We show that the force exerted by the membrane on a PI displays a non-monotonic behavior with respect to the PI vertical displacement. The qualitative features of this force appear to be independent of the
protein geometry. For two interacting PIs, the membrane-mediated potential exhibits sub-power-law decay with inter-
protein distance, reflecting the complex nature of the elastic medium. The interaction potential shows that conical PIs with identical and opposite orientations repel and attract, respectively, confirming the analogy between PI orientation and electric charge, in the LD regime. In the presence of membrane flows, we identify a characteristic velocity that separates two regimes in which bending
rigidity and viscous effects dominate, respectively, implying the onset of flow-induced deformations above such velocity threshold. Overall, our results provide quantitative predictions for membrane-
protein systems in biologically relevant scenarios involving LDs, with implications for
protein sorting, clustering, and membrane trafficking.
Read the full abstract ▾
Optics
Attosecond-timing millimeter waves via Kerr optical frequency division
Scott C. Egbert, Brendan M. Heffernan, James Greenberg et al. (+2 more)
Millimeter-wave oscillators underpin key applications in communication, spectroscopy, radar, and astronomy, yet their achievable spectral purity remains limited. Approaches that directly generate millimeter-wave carriers are fundamentally limited by
quantum and thermal phase-noise processes. Here we show that these limits can be overcome by combining Kerr-induced optical frequency division in a chip-scale microresonator with a large-spacing dual-wavelength Brillouin
laser. This 3.3 THz optical reference injection-locks a Kerr
soliton microcomb, with a repetition rate that becomes a coherently divided 300 GHz carrier with phase noise below the
quantum limit of a corresponding 300 GHz dual-wavelength Brillouin
laser and far below the thermo-refractive noise of a microring resonator. Cross-correlation phase-noise measurements were developed to show that the resulting oscillator reaches a phase-noise floor of -152 dBc/Hz at 1 MHz offset, consistent with photodetection shot noise. Integration of the measured spectrum yields an RMS timing jitter of 135 as from 1 kHz to 1 MHz. These results establish optical frequency division as a generic method for generation of sub-terahertz carriers with coherence no longer constrained by direct-generation limits.
Read the full abstract ▾
All authors Scott C. Egbert, Brendan M. Heffernan, James Greenberg, William F. McGrew, Antoine Rolland
Computational Physics · Cross-listed from nlin.PS
Numba-Accelerated 2D Diffusion-Limited Aggregation: Implementation and Fractal Characterization
Sandy H. S. Herho, Faiz R. Fajary, Iwan P. Anwar et al. (+4 more)
We present dla-ideal-solver, a high-performance framework for simulating two-dimensional
Diffusion-Limited Aggregation (DLA) using Numba-accelerated Python. By leveraging just-in-time (JIT) compilation, we achieve computational throughput comparable to legacy static implementations while retaining high-level flexibility. We investigate the
Laplacian growth instability across varying injection geometries and walker concentrations. Our analysis confirms the robustness of the standard fractal dimension $D_f \approx 1.71$ for dilute regimes, consistent with the Witten-Sander universality class. However, we report a distinct crossover to Eden-like compact growth ($D_f \approx 1.87$) in high-density environments, attributed to the saturation of the screening length. Beyond standard mass-radius scaling, we employ generalized Rényi dimensions and lacunarity metrics to quantify the monofractal character and spatial heterogeneity of the aggregates. This work establishes a reproducible, open-source testbed for exploring phase transitions in non-
equilibrium statistical mechanics.
Read the full abstract ▾
All authors Sandy H. S. Herho, Faiz R. Fajary, Iwan P. Anwar, Faruq Khadami, Nurjanna J. Trilaksono, Rusmawan Suwarman, Dasapta E. Irawan
Computational Physics
Bayesian identification of fibrous insulation thermal conductivity towards design of spacecraft thermal protection systems
Alex Alberts, Akshay Jacob Thomas, Kamran Daryabeigi et al. (+1 more)
The design of spacecraft thermal protection systems (TPS) requires accurate knowledge of thermal transport properties across wide ranges of temperature and pressure. For fibrous insulation, conventional measurement techniques in laboratory settings are typically limited to temperatures much lower than what is reached in atmosphere entry scenarios. Moreover, it is often the case that only temperature measurements are available, meaning that the thermal conductivity of the insulation must be indirectly inferred as an inverse problem. We propose a
Bayesian framework using information field theory (IFT) to reconstruct the thermal conductivity of high-temperature fibrous insulation from sparse experimental data. Under IFT, the conductivity is represented as a Gaussian process, and the physics is enforced via a physics-informed prior over the temperature derived from the heat equation. Bayes's rule produces an infinite-dimensional posterior distribution that quantifies uncertainty about the conductivity which can be evaluated in extrapolation regimes. We apply the method to Opacified Fibrous Insulation with both synthetic and experimental data to reconstruct the thermal conductivity beyond the experimental regime. The inferred conductivities are validated against reference data and then propagated into high-fidelity digital twins of flexible TPS performance under Mars and Earth entry trajectories. The results show that IFT yields accurate predictions with quantified uncertainty, enabling robust TPS sizing in regimes inaccessible to direct measurement.
Read the full abstract ▾
All authors Alex Alberts, Akshay Jacob Thomas, Kamran Daryabeigi, Ilias Bilionis
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Controlling HER activity and stability of $\gamma$- and 6,6,12-Graphyne through engineered B-N doping: DFT and Reactive MD simulations
Juan Gomez Quispe, Matheus Medina, Subhendu Mishra et al. (+3 more)
Graphynes offer a chemically heterogeneous $sp/sp^{2}$ carbon framework with distinct electronic regimes and site-selective reactivity. Here, Density Functional Theory and Reactive Molecular Dynamics Simulations are combined to evaluate pristine, B-doped, N-doped, and B-N co-doped $\gamma$-graphyne and 6,6,12-graphyne (meta/ortho/para). $\gamma$-graphyne is a
semiconductor, while 6,6,12-graphyne exhibits an anisotropic Dirac-like semi-metallic dispersion. B/N substitution reconstructs near-$E_F$ states via dopant $\pi$ hybridization, and B-N pairing stabilizes defects through donor-acceptor compensation, with the ortho substitutions being the most favorable. Hydrogen adsorption remains weak on pristine
lattices but becomes locally optimized upon doping, with near thermo-neutral $\Delta G_{\mathrm{ads}}$ 'hot spots' predominantly on $sp$-proximate carbon sites adjacent to the dopants. Reactive MD at 300 K further reveals an activity stability trade-off: B-N ortho in $\gamma$-graphyne sustains controlled hydrogen uptake without catastrophic bond scission, whereas B-N meta/para degrade, and 6,6,12-graphyne is generally more susceptible to over-hydrogenation. These results identify the B-N geometry as a key design variable for graphyne-based HER catalysts, which require both a favorable $\Delta G_{\mathrm{ads}}$ and finite-temperature hydrogenation stability.
Read the full abstract ▾
All authors Juan Gomez Quispe, Matheus Medina, Subhendu Mishra, Douglas S Galvao, Abhishek Singh, Pedro Alves da Silva Autreto
Plasma Physics
Asymptotic scaling theory of electrostatic turbulent transport in magnetised fusion plasmas
T. Adkins, I. G. Abel, M. Barnes et al. (+8 more)
Turbulent transport remains one of the principal obstacles to achieving efficient
magnetic confinement in fusion devices. Two of the dominant drivers of the
turbulence are microscale instabilities fuelled by electron- and ion-temperature gradients (ETG and ITG), whose nonlinear saturation determines the cross-field transport of particles and energy. We present a simple asymptotic scaling theory that unifies ETG- and ITG-driven
turbulence within a common framework. By balancing the fundamental time scales of linear free energy injection, nonlinear decorrelation, and parallel propagation, the theory isolates the dependence of the heat flux on
equilibrium parameters to two key quantities: the parallel system scale and the outer-scale aspect ratio. We show that these quantities encapsulate the essential physics of saturation, leading to distinct predictions for ETG and ITG transport: a cubic scaling with the temperature gradient in the electron channel, and a linear scaling in the ion channel, the latter recovering the ITG scaling recently proposed and numerically verified in axisymmetric geometry by Nies et al. [Phys. Rev. Res. 8, 013295 (2026)]. Extensive nonlinear gyrokinetic simulations confirm that these theoretical predictions in fact hold irrespective of the
magnetic geometry (slab, tokamak, or stellarator), including the first numerical confirmation of the cubic ETG scaling anticipated by earlier studies. Our theory depends only on the parallel system scale and the outer-scale aspect ratio, and hence provides a physics-based foundation for fast, geometry-aware transport models, offering a pathway toward reactor optimisation in both tokamaks and stellarators.
Read the full abstract ▾
All authors T. Adkins, I. G. Abel, M. Barnes, S. Buller, W. Dorland, P. G. Ivanov, R. Meyrand, F. I. Parra, R. Nies, A. A. Schekochihin, J. Squire
General Physics
On the quantum separability of qubit registers
Szymon Łukaszyk
We show that the bipartite separability of a pure
qubit state hinges critically on the combinatorial structure of its computational-basis support. Using Boolean cube geometry, we introduce a taxonomy that distinguishes support-guaranteed separability from cases in which
entanglement depends on probability amplitudes. We provide closed-form support counts, identify forbidden configurations that enforce multipartite
entanglement, and show how these results can enable fast
entanglement diagnostics in
quantum circuits. The framework offers immediate utility in classical simulation,
entanglement-aware
circuit design, and
quantum error-correcting code analysis. This establishes support geometry as a practical and scalable tool for understanding
entanglement in
quantum information processing.
Read the full abstract ▾
Medical Physics
Phantom model for intracranial pressure
Célia Batonon, Heimiri Monnier, Arnaud Gauberville et al. (+1 more)
This report presents the MODÈFONE project, whose objective is to develop a simplified experimental model of the cerebrospinal system in order to investigate fluid-structure interactions and physiological adaptations under altered
gravity conditions, with a particular focus on microgravity. The experimental setup is based on a pulsatile hydraulic
circuit reproducing systolic and diastolic dynamics, coupled with deformable elements simulating vascular compliance and a cranial compartment immersed in a fluid representing cerebrospinal fluid. This model enables the analysis of cranial and spinal pressures as well as their pulsatility. The purpose of this report is to describe the design and the results of the experimental setup.
Read the full abstract ▾
All authors Célia Batonon, Heimiri Monnier, Arnaud Gauberville, Léna Connesson
History and Philosophy of Physics · Cross-listed from quant-ph
Superluminal Transformations and Indeterminism
Amrapali Sen and Flavio Del Santo
Quantum theory is widely regarded as fundamentally indeterministic, yet classical frameworks can also exhibit indeterminism once infinite information is abandoned. At the same time, relativity is usually taken to forbid superluminal signalling, although Lorentz
symmetry formally admits superluminal transformations (SpTs). Dragan and Ekert have argued that SpTs entail a form of indeterminism analogous to that encountered in
quantum theory. Here, we derive a theory-independent no-go
theorem from a set of natural assumptions: any framework admitting non-order-preserving SpTs must either abandon finite information, relinquish time-symmetric informational content, deny that the past stores
memory, or abandon the notion that time determines a preferred causal ordering. In particular, one possible implication is that any theory accommodating SpTs suggests an ontology with unbounded informational content, akin to deterministic classical theories formulated over the real numbers. Consequently, any ontic indeterminacy associated with superluminal transformations cannot originate from finite information.
Read the full abstract ▾
Optics
Orbital angular momentum of spatiotemporal vortices: a ray-mechanical analogy
Sophie Vo, Konstantin Y. Bliokh and Miguel A. Alonso
Spatiotemporal
vortex pulses (STVPs) are wavepackets that carry transverse orbital angular momentum (OAM), whose proper quantification has been the subject of recent debate. In this work, we introduce a simplified mechanical model of STVPs, consisting of a loop of non-interacting point particles traveling at a uniform constant speed but at slightly di!erent angles. We examine di!erent initial conditions for the particle loop, including configurations that are
elliptic in space at a given time and configurations that are
elliptic in spacetime at a fixed propagation distance. Furthermore, employing a non-uniform mass distribution allows the particle loop to mimic the STVP not only in configuration space but also in momentum space. Remarkably, when supplemented by a semiclassical vorticity quantization condition, our mechanical model exactly reproduces di!erent wave-based OAM results previously reported for paraxial STVPs.
Read the full abstract ▾
Chemical Physics · Cross-listed from quant-ph
QDK/Chemistry: A Modular Toolkit for Quantum Chemistry Applications
Nathan A. Baker, Brian Bilodeau, Chi Chen et al. (+23 more)
We present QDK/Chemistry, a software toolkit for
quantum chemistry workflows targeting
quantum computers. The toolkit addresses a key challenge in the field: while
quantum algorithms for chemistry have matured considerably, the infrastructure connecting classical electronic structure calculations to
quantum circuit execution remains fragmented. QDK/Chemistry provides this infrastructure through a modular architecture that separates data representations from computational methods, enabling researchers to compose workflows from interchangeable components. In addition to providing native implementations of targeted
algorithms in the
quantum-classical pipeline, the toolkit builds upon and integrates with widely used open-source
quantum chemistry packages and
quantum computing frameworks through a plugin system, allowing users to combine methods from different sources without modifying workflow logic. This paper describes the design philosophy, current capabilities, and role of QDK/Chemistry as a foundation for reproducible
quantum chemistry experiments.
Read the full abstract ▾
All authors Nathan A. Baker, Brian Bilodeau, Chi Chen, Yingrong Chen, Marco Eckhoff, Alexandra Efimovskaya, Piero Gasparotto, Puck van Gerwen, Rushi Gong, Kevin Hoang, Zahra Hooshmand, Andrew J. Jenkins, Conrad S. N. Johnston, Run R. Li, Jiashu Liang, Hongbin Liu, Alexis Mills, Maximilian Mörchen, George Nishibuchi, Chong Sun, Bill Ticehurst, Matthias Troyer, Jan P. Unsleber, Stefan Wernli, David B. Williams-Young, Boqin Zhang
Optics
Single Pixel Imaging and Compressive Sensing: A Practical Tutorial
Dennis Scheidt
Single Pixel Imaging is an emerging imaging technique that employs a bucket detector (photodiode) to sample a spatially modulated light field, rather than measuring the spatial distribution with an array of detectors. This approach provides a low-cost alternative for imaging at unconventional wavelengths and enables improved signal collection in noisy measurement environments. Furthermore, it allows the application of compressive sensing to reduce the amount of acquired data and measurement time, facilitating live or in vivo imaging applications. This tutorial presents the experimental implementation of measurement bases and compressive sensing reconstruction methods, including both deterministic
algorithms and deep learning approaches. Accompanying Python notebooks guide readers through the reproduction of the presented results and support the application of the methods to their own work.
Read the full abstract ▾
Fluid Dynamics
Maximal spreading of impacting viscoelastic droplets
Orr Avni, Dongyue Wang, Mithun Ravisankar et al. (+1 more)
Droplet impact and spreading on solid substrates are well understood for Newtonian fluids, yet how viscoelasticity alone modifies the maximal spreading remains unclear. To identify the mechanisms governing the spreading dynamics, we conducted impact experiments and measured the maximal spreading diameter to quantify how fluid elasticity modifies the maximal spreading of impacting droplets. Experiments were performed using fluids within a narrow range of viscosity and surface tension, but with varying relaxation times. For a wide range of conditions, viscoelastic droplets follow a similar behavior as Newtonian ones; however, their maximal spreading diameter is significantly reduced compared with the Newtonian behavior when the Deborah number is of order unity. These observations are rationalized by incorporating the viscoelastic effects into a classical energy balance model. The scaling argument obtained from this model explains the reported reduction in maximal spreading and identifies the range of fluid properties for which the strongest viscoelastic effects emerge.
Read the full abstract ▾
All authors Orr Avni, Dongyue Wang, Mithun Ravisankar, Roberto Zenit
Chemical Physics
Unifying reciprocal and real space atomic dynamics in dilute gases
Jaeyun Moon
In solids, quanta of atomic vibrations are identified in reciprocal space by their frequency and wavevector as
phonons. At the opposite end of the matter spectrum, dynamics of dilute gases is conventionally described in terms of atomic or molecular collisions and translations in real space and time. These two formalisms are apparently incompatible, leading to difficulties in understanding atomic dynamics in intermediate matter. In this work, we demonstrate that normal modes, often synonymously considered as
phonons in solids, provide a microscopic description of various transport processes, including thermal conductivity,
diffusion coefficient, and shear viscosity, in a prototypical dilute gas, argon. Our results bridge the conceptual divide between solid and gas phase descriptions and establish normal modes as a unifying framework for atomic dynamics well beyond crystalline solids.
Read the full abstract ▾
Optics
Enhanced posterior sampling via diffusion models for efficient metasurfaces inverse design
Mathys Le Grand, Pascal Urard, Denis Rideau et al. (+5 more)
The inverse design of metasurfaces faces inherent challenges due to the nonlinear and highly complex relationship between
geometric configurations and their electromagnetic behavior. Traditional optimization approaches often suffer from excessive computational demands and a tendency to converge to suboptimal solutions. This study presents a
diffusion-based generative framework that incorporates a dedicated consistency constraint and advanced posterior sampling methods to ensure adherence to desired electromagnetic specifications. Through rigorous validation on small-scale metasurface configurations, the proposed approach demonstrates marked enhancements in both accuracy and reliability of the generated designs. Furthermore, we introduce a scalable methodology that extends inverse design capabilities to large-scale metasurfaces, validated for configurations of up to $98 \times 98$ nanopillars. Notably, this approach enables rapid design generation completed in minute by leveraging models trained on substantially smaller arrays ($23 \times 23$). These innovations establish a robust and efficient framework for high-precision metasurface inverse design.
Read the full abstract ▾
All authors Mathys Le Grand, Pascal Urard, Denis Rideau, Loumi Trémas, Damien Maitre, Louis-Henri Fernandez-Mouron, Adam Fuchs, Régis Orobtchouk
Physics and Society
Unveiling the impact of cross-order hyperdegree correlations in contagion processes on hypergraphs
Andrés Guzmán, Federico Malizia and István Z. Kiss
Contagion processes in social systems often involve interactions that go beyond pairwise contacts. Higher-order networks, represented as hypergraphs, have been widely used to model multi-body interactions, and their presence can drastically alter contagion dynamics compared to traditional network models. However, existing analytical approaches typically assume independence between pairwise and higher-order degrees, and thus study their roles in isolation. In this paper, we develop an effective hyperdegree model (EHDM) to describe Susceptible-Infected-Susceptible (SIS) dynamics on hypergraphs that explicitly captures correlations between the distribution of groups with different sizes. Our effective hyperdegree model shows excellent agreement with
stochastic simulations across different types of higher-order networks, including those with heterogeneous degree distributions. We explore the critical role of cross-order degree correlations, specifically, whether nodes that are hubs in pairwise interactions also serve as hubs in higher-order interactions. We show that positive correlation decreases the epidemic threshold and anti-correlation temporally desynchronizes infection pathways (pairwise and group interactions). Finally, we demonstrate that, depending on the level of correlation, the optimal control strategy shifts – from one that is purely pairwise- or higher-order-focused to one in which a mixed strategy becomes optimal.
Read the full abstract ▾
Optics · Cross-listed from quant-ph
Purcell enhanced electroluminescence of a unipolar light emitting quantum device at 10 micron
Marta Mastrangelo, Djamal Gacemi, Axel Evirgen et al. (+7 more)
Efficient generation of radiation in the mid- and far- infrared relies primarily on
lasers and coherent nonlinear optical phenomena driven by
lasers. This wavelength range lacks of luminescent devices because the spontaneous emission rate becomes much longer than the nonradiative energy relaxation processes and therefore emitters have to count on stimulated emission produced by linear or non-linear optical gain. However, spontaneous emission is not a fundamental property of the emitter. By engineering metamaterials composed of arrays of nano-emitters into microcavities coupled to patch
antennas, we have demonstrated mid-infrared electroluminescent devices emitting a collimated beam with excellent spatial properties and a factor 100 increase in the collected power, compared to standard devices. Our results illustrate that by reshaping the photonic environment around emitting dipoles, as in the Purcell effect, it is possible to enhance the spontaneous emission and conceive efficient optoelectronic light emitting devices that operate close to the thermodynamical
equilibrium as LEDs in the visible range.
Read the full abstract ▾
All authors Marta Mastrangelo, Djamal Gacemi, Axel Evirgen, Salvatore Pes, Alexandre Larrue, Pascal Filloux, Isabelle Sagnes, Abdelmounaim Harouri, Angela Vasanelli, Carlo Sirtori
Plasma Physics
Single-laser scheme for reaching strong field QED regime via direct laser acceleration
Robert Babjak and Marija Vranic
We investigate a single-
laser scheme for reaching the strong-field QED regime based on direct
laser acceleration (DLA) of electrons followed by their head-on collision with the same
laser pulse reflected from an overdense foil. In this configuration, electrons are first accelerated inside an underdense
plasma by a relativistic
laser pulse and subsequently interact with the reflected
laser field, emitting high-energy
photons via nonlinear Compton scattering which decay into electron-positron pairs through the nonlinear Breit-Wheeler process. Using analytical scalings supported by quasi-3D particle-in-
cell simulations including QED effects, we demonstrate that a
laser pulse with power as low as 2 PW is sufficient to reach the
quantum regime characterized by $\chi_e> 1$ . For higher powers, we observe a rapid nonlinear increase in the number of generated positrons, reaching more than 2 nC for a 10 PW
laser pulse with energy of approximately 1.1 kJ. A semi-analytical model is employed to estimate the positron yield, showing good agreement with simulation results. We further study the influence of
laser depletion and the positioning of the reflecting foil on the efficiency of pair production. The presented scheme provides an experimentally feasible platform for probing strong-field QED effects using currently available multi-petawatt
laser systems.
Read the full abstract ▾
Optics
Illustrating Special-Relativity Phenomena via Gaussian Ray Optics
M. A. Bouchene
We highlight the correspondence between one-dimensional Lorentz transformations, which relate events observed from two distinct inertial reference frames, and ray transfer transformations in Gaussian optics. Specifically, we identify optical systems whose transfer
matrices reproduce the mathematical structure of the Lorentz transformation. Within this framework, we show that fundamental effects of special relativity as time dilatation, length contraction and loss of synchronicity find a direct counterpart in the behavior of light rays deviated by a diverging lens, thereby providing a novel and illustrative optical equivalence of relativistic phenomena. This article is intended for a broad audience of physicists. The correspondence described is general and may interest students with a moderate background in special relativity and optics, as well as teachers in the corresponding fields.
Read the full abstract ▾
Biological Physics
One scale to rule them all: interpretable multi-scale Deep Learning for predicting cell survival after proton and carbon ion irradiation
Giulio Bordieri, Giorgio Cartechini, Anna Bianchi et al. (+4 more)
The relationship between the physical characteristics of the radiation field and biological damage is central to both radiotherapy and radioprotection, yet the link between spatial scales of energy deposition and biological effects remains not entirely understood. To address this, we developed an interpretable deep learning model that predicts
cell survival after
proton and carbon ion irradiation, leveraging sequential
attention to highlight relevant features and provide insight into the contribution of different energy deposition scales. Trained and tested on the PIDE
dataset, our model incorporates, beside LET, nanodosimetric and microdosimetric quantities simulated with MC-Startrack and Open-TOPAS, enabling multi-scale characterization. While achieving high predictive accuracy, our approach also emphasizes transparency in decision-making. We demonstrate high accuracy in predicting RBE for in vitro experiments. Multiple scales are utilized concurrently, with no single spatial scale being predominant. Quantities defined at smaller spatial domains generally have a greater influence, whereas the LET plays a lesser role.
Read the full abstract ▾
All authors Giulio Bordieri, Giorgio Cartechini, Anna Bianchi, Anna Selva, Valeria Conte, Marta Missiaggia, Francesco G. Cordoni
Medical Physics
A combined dose and microdosimetric modeling framework incorporating volume effects correlates with tissue sparing in proton minibeam radiotherapy
Giulio Bordieri, Marco Battestini, Gianluca Lattanzi et al. (+4 more)
Proton minibeam (pMB) radiotherapy, delivers highly heterogeneous dose distributions alternating high-dose peaks and low-dose valleys. This aims to widen the therapeutic window by improving normal tissue sparing while maintaining the same or even better tumour control. The performance of pMB strongly depends on the collimator design and physical parameters. To better understand the physical and radiobiological drivers of this enhanced therapeutic window, we perform a detailed microdosimetric characterization of
proton minibeams and assess their impact. We characterize radiation quality with microdosimetry through Monte Carlo simulations. Then we extend the Generalized
Stochastic Microdosimetric Model to predict the normal tissue complication probability (NTCP) at different depths in water, 1cm, 2cm, and 4cm, for 100MeV
proton minibeams realized with varying configurations of collimator. Results are compared with conventional homogeneous field (HF) irradiation after dose normalization to the tumor. The developed model is applied by considering tissues as divided into several functional subunits, connected by a seriality parameter. Microdosimetric characterization of
proton minibeam irradiation shows differences between peak and valley regions in shaping lineal energy spectra, especially at low depth, while radiation quality uniforms progressively getting closer to the tumor. NTCP calculations results suggest an increased sparing effect for pMB over conventional HF. A strong dependence is found on the peak-to-valley dose ratio (PVDR), and on the seriality parameter. Predictions indicate substantial sparing from pMB, especially for PVDR > 15, including relatively serial organs with seriality around 0.7. This integrated dose-microdosimetric-biological framework elucidates how spatial fractionation, radiation quality, and organ architecture collectively shape tissue sparing in pMB.
Read the full abstract ▾
All authors Giulio Bordieri, Marco Battestini, Gianluca Lattanzi, Francesco Romano, Emanuele Scifoni, Marta Missiaggia, Francesco Giuseppe Cordoni
Chemical Physics
Regularity Priors for the Linear Atomic Cluster Expansion
James P. Darby, Joe D. Morrow, Albert P. Bartók et al. (+3 more)
Machine-learned interatomic potentials enable large systems to be simulated for long time scales at near ab-initio accuracy. This accuracy is achieved by fitting extremely flexible model architectures to high quality reference data. In practice, this flexibility can cause unwanted behavior such as jagged predicted potential energy surfaces and generally poor out-of-distribution behavior. We investigate a general strategy for incorporating prior beliefs on the regularity of the target energy into linear Atomic Cluster Expansion (ACE) models and explore to what extent this approach improves the quality of the fitted models. Our main focus is an over-regularisation that replicates the Gaussian broadening used in Smooth Overlap of Atomic Positions (SOAP) descriptors within the ACE framework. Numerical tests indicate that the exact form of the prior is non-critical but that including such a prior leads to significant improvement in test errors, consistent repulsion at close-approach, eliminates spurious false minima in the potential energy and enhances stability during molecular dynamics simulations.
Read the full abstract ▾
All authors James P. Darby, Joe D. Morrow, Albert P. Bartók, Volker L. Deringer, Gábor Csányi, Christoph Ortner
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Resolving the band alignment of InAs/InAsSb mid-wave-infrared type-II superlattices
Michał Rygała, Julian Zanon, Andreas Bader et al. (+5 more)
In this work, three InAs/InAs$_{0.65}$Sb$_{0.35}$ superlattices with different periods were investigated using photoluminescence and photoreflectance measurements and their band structure was simulated using a 14 bulk-band kp model. The structures were studied by analyzing the
evolution of the spectral features in temperature and excitation power to determine the origin of optical transitions. After identifying which of these are related to the superlattice mini-bands, a rich collection of observed higher-order optical transitions was compared with refractive-index calculations. This procedure was used to adjust the parameters of the theoretical model, namely the bowing parameters of the InAsSb valence band offset and bandgap. It was also shown that the spectroscopy of the higher-order states combined with numerical modeling of the refractive index is a powerful tool for improvement of the material parameters, presenting a new approach to material studies of advanced
semiconductor heterostructures.
Read the full abstract ▾
All authors Michał Rygała, Julian Zanon, Andreas Bader, Tristan Smołka, Fabian Hartmann, Sven Höfling, Michael Flatté, Marcin Motyka
Fluid Dynamics · Cross-listed from cond-mat.soft
Coupled gas and bubble dynamics at the solidification front
Bastien Isabella, Cécile Monteux and Sylvain Deville
The formation and entrapment of gas bubbles during solidification significantly influence the microstructure and mechanical properties of materials, from metallic
alloys to ice. While gas segregation at the solidification front is well-documented, the real-time dynamics of bubble nucleation, growth, and engulfment-and their dependence on solidification velocity-remain poorly understood. In this study, we use in situ cryo-confocal fluorescence microscopy to investigate the coupled gas-bubble dynamics at the solidification front of carbonated water, systematically varying the solidification velocity ($V = 1-20 \mu m/s$) while maintaining a constant thermal gradient ($G = 15 K/mm$). Our experiments reveal that bubble nucleation is governed by a characteristic nucleation time, which emerges from the interplay between gas
diffusion ahead of the front, nucleation kinetics, and bubble growth, all competing with the advancing solidification front. These results allow us to estimate the critical gas concentration for bubbles nucleation in carbonated water. These results offer a detailed understanding of the mechanisms controlling bubble nucleation and entrapment during solidification at constant thermal gradient. They contribute to the development of strategies to control bubble formation in industrial processes where the presence of bubbles can either be detrimental or intentionally harnessed.
Read the full abstract ▾