Fluid Dynamics
Elastohydrodynamic instability of a spinning elastic disk
Sifan Yin, Paul R. Kaneelil and L. Mahadevan
A soft thin elastic disk spinning in a viscous fluid experiences centrifugal tension generated by rotation together with viscous shear generated by the surrounding flow. While the former stabilizes the flat state, the latter can destabilize it. We combine the linearized Föppl-von Kármán equations for a rotating elastic disk with the shear stresses arising from the classical von Kármán swirling flow to derive an elastohydrodynamic stability problem. Linear stability analysis identifies the onset of buckling in terms of two dimensionless control parameters measuring centrifugal stiffening and fluid-induced shear. Above threshold the disk buckles into azimuthally periodic saddle-like modes whose wavenumber increases with increasing rotational tension. The buckled configuration also supports retrograde traveling waves that rotate more slowly than the material frame. These results identify a simple mechanism whereby fluid shear destabilizes rotating elastic structures.
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Accelerator Physics · Cross-listed from cond-mat.mtrl-sci
Photoemission from Semi-Infinite Crystals: First-Principles Scattering States and Quantitative Ag(111) Validation
Tyler Wu, Truman Idso, Siddharth Karkare et al. (+1 more)
Photoemission is an escape problem, yet first-principles calculations usually trap the electron in a periodic box. We present a parameter-free ab initio framework that removes this artificial boundary by constructing open scattering states for semi-infinite
crystal-
vacuum interfaces from Wannier
Hamiltonians and Green-function
embedding. The method gives continuum-normalized time-reversed LEED final states with microscopic quasiparticle attenuation. For Ag(111), it predicts absolute
quantum efficiency, vectorial photoemission, and mean transverse energy on the experimental scale.
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All authors Tyler Wu, Truman Idso, Siddharth Karkare, Tomás Arias
Optics
Synthesizing superoscillations with just two frequencies
Denys I. Bondar and Diyar Talbayev
Superoscillations, band-limited signals that locally oscillate faster than their highest
Fourier component, have recently enabled superspectroscopy and super-sensing. Previous experiments [Phys. Rev. Lett. 131, 153803 (2023), https://doi.org/10.1103/PhysRevLett.131.153803 and APL Photonics 10, 086107 (2025), https://doi.org/10.1063/5.0271556] relied on combining four quasi-sinusoidal harmonics to generate temporal superoscillations. Here, we show that just two harmonics are sufficient to produce superoscillations of comparable quality, as quantified by the local frequency. By reanalyzing prior experimental data with two harmonics (0.5 and 0.6 THz) and performing new experiments with 0.9 and 1 THz harmonics, we demonstrate a twofold enhancement in local frequency relative to the highest frequency component – matching the enhancement previously achieved with four harmonics. We provide a simple analytical explanation for the bichromatic superoscillations based on near-complete destructive interference, and identify an optimal frequency separation of approximately 10% that balances the superoscillatory frequency enhancement against signal amplitude. This simplification substantially lowers the experimental barrier to implementing superoscillation-based technology.
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Chemical Physics
Momentum Rescaling for Collapse to a Block Ehrenfest Dynamics
Ari R. X. Pereira and Benjamin G. Levine
The Ehrenfest with collapse to a block (TAB)
algorithm has recently been demonstrated to efficiently and accurately simulate nonadiabatic molecular dynamics in dense
manifolds of electronic states. TAB employs an Ehrenfest force for the classical
nuclei, accompanied by
stochastic collapses of the electronic density
matrix to account for decoherence. Energy conservation dictates that the
nuclear momentum be adjusted during such a collapse. In this paper, we present a prescription for rescaling the component of the
nuclear momentum that projects into the branching plane between arbitrary superposition states. This prescription yields accurate branching ratios and phase-space distributions when compared to alternative methods in which scaling is performed in the nonadiabatic coupling direction or the momentum direction. We justify this direction by deriving an effective nonadiabatic coupling vector from the localized Pechukas force during a collapse step.
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Chemical Physics
Accounting for Nanofilm Contributions in Interfacial Free Energy Calculations Using Classical Density Functional Theory
Yafan Yang, Zufeng Zuo, Xingyu Zhao et al. (+2 more)
Fluid nanofilms play a fundamental role in nanoscale interfacial thermodynamics, yet their treatment in classical density functional theory (cDFT) remains incomplete. We investigate nanofilm interfacial properties using a cDFT framework built upon the perturbed-chain statistical associating fluid theory (PC-SAFT) for both fluid-fluid and fluid-solid interfacial systems. Comparison with molecular simulations shows that the long-standing numerical discrepancies regarding free-standing nanofilms persist in the PC-SAFT functional predictions, supporting the view that they stem from thermal capillary-wave fluctuations rather than deficiencies of the density functional itself, as such fluctuations are inherently neglected in the mean-field approximation adopted by standard cDFT. Importantly, we establish a thermodynamically consistent framework for interfacial free energy (IFE) calculation, which explicitly incorporates nanofilm thermodynamic contributions and redefines the effective fluid volume by excluding the solid-phase region. The proposed method exhibits excellent consistency with another method based on the relationship between IFE and disjoining pressure for fluid systems, while both fluid-fluid and fluid-solid IFEs differ substantially from those predicted by the conventional method. We find that neglecting nanofilm contributions induces prominent size-dependent variations in the IFE and contact angle of hemicylindrical droplets, which is inconsistent with the extensive literature. Different methods also lead to opposite signs of the line tension for a hemispherical argon nanodroplet on a strongly lyophobic surface. The proposed framework provides a unified molecular-level basis for understanding interfacial processes involving nanofilms, including wetting, nucleation, adsorption, and other phenomena that rely on the accurate evaluation of IFEs.
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All authors Yafan Yang, Zufeng Zuo, Xingyu Zhao, Shuyu Sun, Denvid Lau
Physics and Society
Cultural Evolution of Perfumes since 1900
Vahid Satarifard, Fabian Baumann, Geetanjali Minsky et al. (+4 more)
Perfumes are cultural artifacts and works of sensory art, composed from a finite, recombinable palette of notes that together evoke a distinctive scent impression. Here, we assemble the largest perfume corpus compiled to date, spanning multiple independent databases from 1900 to 2024, and study its
evolution through a multidisciplinary computational framework. We first characterize perfumes by properties such as complexity and novelty using crowd-sourced data, finding that compositions have grown more minimalist in their scent profiles, yet more novel in their note combinations, a transition that began in the 1990s. Next, we construct copy-lineage networks and examine how notes are selected across successive time windows. We show that although imitation is pervasive, a growing share of notes drifts free of selection, and original creations retain a measurable quality premium, where they last longer, project further, and earn higher regard. Finally, we construct the collaboration network of master perfumers and show that a perfumer's creative style behaves as a social contagion, transmitted through collaboration and decaying with social distance. We discuss the principal forces shaping this
evolution, including regulatory restrictions, cultural change, and the consolidation in the industry. Our findings position perfume as a culturally evolving system, akin to music and fashion, through which societies express and communicate hedonic sensory experiences.
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All authors Vahid Satarifard, Fabian Baumann, Geetanjali Minsky, Laura Sisson, Lou M. Haux, Christophe Laudamiel, Nicholas A. Christakis
Physics and Society
Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations
Domenico Pomarico, Alessandra Costantino, Gabriel Ramirez Sanchez et al. (+12 more)
A
quantum-inspired tensor network framework for wildfire susceptibility classification in the Gargano region is introduced, leveraging AlphaEarth
embeddings and
Matrix Product State models. The approach combines scalable geospatial representations with an interpretable
quantum mask, enabling both binary and multiclass classification of wildfire susceptibility. Beyond predictive performance, the study reveals a pronounced grokking transition in the binary case and provides a detailed analysis of inter-class confusion in the multiclass setting. By introducing level-resolved mixedness diagnostics based on reduced density
matrices, we show that the MPS classifier naturally encodes a hierarchy of class distinguishability, with non-adjacent categories becoming more separable than neighboring ones. These results demonstrate that tensor network models not only achieve competitive classification accuracy but also offer a physically grounded framework to quantify and interpret class separability in complex environmental
datasets.
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All authors Domenico Pomarico, Alessandra Costantino, Gabriel Ramirez Sanchez, Loredana Bellantuono, Davide D' Alò, Mario Elia, Alessandro Fania, Francesco Giordano, Niloofar Kheirkhahan, Raffaele Lafortezza, Ester Pantaleo, Sabina Tangaro, Roberto Bellotti, Alfonso Monaco, Nicola Amoroso
Computational Physics · Cross-listed from math.NA
Robust Hierarchical Matrix Compression of Acoustic Volume and Boundary Integral Operators
Alberto Almuna-Morales, Danilo Aballay, Ignacio Labarca-Figueroa et al. (+1 more)
Discretizing integral formulations of the Helmholtz equation yields dense linear systems. Hence, simulating acoustic models at larger scales or higher frequencies is typically constrained by
memory capacity. Fast
algorithms, such as hierarchical
matrix compression, reduce the
memory footprint substantially while controlling the approximation error in
matrix-vector multiplications. However, the commonly used Adaptive Cross Approximation suffers from early-convergence problems, where the iterative construction of low-rank decompositions stops before reaching the targeted error tolerance. This failure arises when the error
estimator does not capture significant components of the
matrix structure under partial pivoting. This manuscript proposes a new diagonal convergence criterion, additional
matrix elements for the pivoting strategy, an extended admissibility condition, and a sustained convergence check to improve the robustness of hierarchical
matrix compression. These modifications improve compression reliability without increasing
memory. We tested our compression strategy on various discretized volume and boundary integral operators. The computational results show that our approach successfully compresses all
benchmark matrices within predefined tolerances, thereby resolving the early-convergence issues encountered in standard
algorithms. This robust
matrix compression was achieved at the same
memory footprint as alternative compression strategies. Furthermore, a complexity analysis shows log-linear
memory scaling with mesh refinement at constant frequency. Finally, we successfully applied our robust
matrix compression
algorithm to a coupled system of volume and boundary integral operators that models transcranial ultrasound propagation. This confirms the feasibility of our robust
algorithm to accelerate large-scale simulations with high-resolution meshes in a biomedical application.
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All authors Alberto Almuna-Morales, Danilo Aballay, Ignacio Labarca-Figueroa, Elwin van 't Wout
Computational Physics · Cross-listed from astro-ph.IM
A Scalable Fast Multipole Method Poisson Solver for the RAMSES code: II. Adaptive Mesh Refinement and Adaptive Time Stepping
Jun-Young Lee and Romain Teyssier
We present an extended implementation of a scalable, O(N) Poisson solver based on the fast multipole method (FMM), fully compatible with adaptive mesh refinement (AMR) and adaptive time stepping (ATS) within the RAMSES framework. Building on the unigrid
algorithm in Lee & Teyssier (2026), we introduce several novel elements, including the use of multiple FMM trees, one for each AMR level, a merged FMM tree for all active levels to optimize neighbor searches, and the introduction of the concept of "nearest-field" to enforce force
symmetry across refinement levels. Across a broad set of test problems, we find excellent, percent-level agreement with our reference traditional multigrid (MG) solver. We show, however, that FMM exhibits better momentum conservation properties across coarse-fine AMR interfaces. Finally, despite the overhead introduced by the spatio-temporal adaptivity, FMM shows better scalability than MG across various AMR configurations, with the largest gains obtained for the largest configurations.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Electron-Induced Formation of C$_{2}$ on Si(100) from Acetylene and Ethylene
Oliver MacLean, Marc Savoie, Damian G. Allis et al. (+12 more)
Hydrogen Desorption Lithography on Si(100) demonstrates the ability of the Scanning Tunneling Microscope (STM) to create functional atomic-scale structures and devices. The dehydrogenation of adsorbed molecules represents a potential complementary technique that has received little
attention. For example, formation of C$_{2}$ could introduce local strain or act as centers for subsequent reactions and would inform positionally controlled mechanosynthesis, an approach with vast potential in surface patterning and functionalization. Here, we studied the electron-induced dehydrogenation of acetylene and ethylene on Si(100) at 4 K using STM. Excitation of acetylene at +3.2 V and above induces configurational switching, including to a new sublayer-bonded geometry previously predicted to be an adsorption precursor, as well as migration and desorption. Excitation also induces dehydrogenation to C$_{2}$. Switching between three observed C$_{2}$ configurations can be induced by excitation at +4.2 V and above. Simulations using density functional theory reproduced the experimental images based on choice of functional, dimer-buckling averaging, and inclusion of diffuse basis functions. In addition, dehydrogenation could be induced using field-emitted electrons on a scale ranging from a single molecule to a radius of >10 nm. These observations highlight the potential of carbon dehydrogenation as an additional tool in Atomically Precise Fabrication (APF).
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All authors Oliver MacLean, Marc Savoie, Damian G. Allis, Rafik Addou, Ryan Groome, Si Yue Guo, Aru Joy Hill, Alex Inayeh, Hadiya Ma, Cameron J. Mackie, Sheena Ou, Marco Taucer, Denis A. B. Therien, Finley Van Barr, Ryan Yamachika
Physics and Society · Cross-listed from cond-mat.stat-mech
Localization transitions of diffusion dynamics in physical networks
Jun Yamamoto, Ivan Bonamassa and Márton Pósfai
Network
diffusion underlies many transport phenomena, with
Laplacian modes setting how information spreads and relaxes. In physical networks, however, connectivity alone is not enough: node volumes introduce local dwell times that regulate how flow is stored before being propagated. Here we show that physical heterogeneity reshapes
topology-driven localization, with the degree-volume ratio emerging as the relevant disorder parameter. We solve an analytical model in which ratio detuning qualitatively reorganizes the
Laplacian spectrum, and demonstrate in empirical networks how degree-volume correlations shift extremal eigenmodes away from the nodes selected by
topology alone. Our results reveal a general feature-rich-driven mechanism for localization control, showing that physicality non-trivially reshapes the disorder landscape governing network dynamics.
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Chemical Physics · Cross-listed from cond-mat.stat-mech
Markov state models revisited: Principles and algorithms for unbiased observables
David Aristoff, Robert J. Webber and Daniel M. Zuckerman
Markov state models (MSMs) have become ubiquitous tools for analyzing molecular dynamics (MD) simulations because of their simple, powerful premise: although complete MD sampling may be impossible, the MSM can "stitch together" transition probabilities derived from local sampling to provide a global picture of kinetics and mechanisms. In the standard MSM framework, the available MD data is organized into a single transition
matrix, which is then used to estimate all observables at a lag time chosen so the coarse-grained dynamics are approximately Markovian. This approach leads to avoidable model bias and motivates long lag times that obscure short-timescale processes of interest. In contrast, this paper shows how to obtain unbiased coarse-grained observables at any fixed lag time and for any fixed coarse-graining in the limit of infinite, properly weighted data. The central idea is to replace the single-
matrix framework with two transition
matrices – one representing
equilibrium dynamics and another representing source-sink recycling dynamics – and use the correct
matrix or
matrices to estimate the matched dynamical observables.
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Atomic Physics · Cross-listed from quant-ph
Quantum Synchronization
Parvinder Solanki, Albert Cabot, Fernando Iemini et al. (+8 more)
Natural and engineered classical systems are replete with examples of synchronization, understood as the adjustment of rhythms of physical systems. Such synchronization is at the heart of the stability of several classical technologies, such as mechanical bridges and electrical networks. Given the advent of
quantum simulation and computation technologies, it is natural to study a
quantum analogue of synchronization and explore novel applications. This review surveys synchronization in few and many-body
quantum systems, measures that quantify them, and their applications to
quantum technologies.
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All authors Parvinder Solanki, Albert Cabot, Fernando Iemini, Federico Carollo, Midhun Krishna, Yeshma Ibrahim, Michal Hajdušek, Igor Lesanovsky, Rosario Fazio, Roberta Zambrini, Sai Vinjanampathy
Optics
Integration of hBN Single-Photon Emitters into a Hybrid Optomechanical Membrane-in-the-Middle Fiber-Cavity
Patrick Maier and Alexander Kubanek
The integration of membranes into optical resonators plays a key role in a variety of applications, including optomechanics. Membranes hosting single
photon emitters, ideally with access to
spin states, open new avenues in optomechanics,
spin-mechanics and
spin-optomechanics. Hexagonal boron nitride is among the most promising two-dimensional materials, showing excellent optical and mechanical properties combined with the ability to host optically active (
spin) defects. The deterministic creation of optically active defect centers in hexagonal boron nitride membranes and their coupling to optomechanical systems is an outstanding challenge. Here, we explore an alternative hybrid approach to establish coupling between a single
photon emitter in commercially available hexagonal boron nitride flakes and a fiber cavity mode. We address technical challenges, such as scattering losses arising from uncontrolled flake topography, and establish deterministic hexagonal boron nitride positioning on the cavity mirror. For the coupled system, we observe cavity-induced spectral enhancement by a factor of up to 100 at room temperature. We extend our work by positioning single
photon emitters in hexagonal boron nitride flakes on a highly strained silicon nitride membrane in a membrane-in-the-middle configuration. The mechanical vibrational modes of the silicon nitride membrane and cavity-coupled emission from the single
photon emitter are simultaneously observed. Our work is a first step towards the realization of a cavity optomechanical platform with an incorporated single
photon emitter and provides a starting point to explore hybrid
spin-optomechanics.
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Data Analysis and Statistics
Accelerating Electrochemical Impedance Spectroscopy Measurements by Reducing Reliance on Noisy Low-Frequency Data
Qiuyu Shi, Naohiro Fujinuma, Yonatan Kurniawan et al. (+4 more)
Electrochemical impedance spectroscopy (EIS) is a powerful tool for probing kinetic and transport processes in electrochemical systems, but its practical use is often limited by the long acquisition time and noise sensitivity of low-frequency measurements. Here, we present a statistical inference-assisted framework that reduces the reliance on low-frequency sampling by increasing the sampling density in cleaner high-frequency regions. Using AutoEIS and
Bayesian inference, the augmented high-frequency data are fitted to selected equivalent
circuit models (ECMs) to reconstruct the full impedance spectrum and quantify parameter uncertainty. To evaluate reconstruction performance, we introduce the critical frequency (fc), defined as the highest cutoff frequency at which the full EIS response can still be reliably recovered. The results show that additional high-frequency sampling can not only shift fc to higher values but also reduce the total measurement time. The specific improvement depends on the noise level of the EIS data, the number of added data points, and the ECM structure. Overall, this work provides a practical framework for designing fast and efficient EIS data acquisition and offers guidance for applying partial-frequency EIS reconstruction in real electrochemical characterization.
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All authors Qiuyu Shi, Naohiro Fujinuma, Yonatan Kurniawan, Runze Zhang, Ali Jaberi, Ashley Dale, Jason Hattrick-Simpers
Space Physics · Cross-listed from astro-ph.EP
Three-Dimensional Dust Distribution in the Jovian System from Juno/Waves Observations: Insights into the Halo Ring and Magnetospheric Dust
Yuqi Zhang, Shengyi Ye, Yuting Li et al. (+3 more)
Discoveries regarding the dusty rings of Jupiter and the Galilean satellites'
dust environment have been continuously refined by orbiters and flybys. Leveraging Juno Waves instrument electric field data, we developed a hybrid recognition framework, coupling Kvammen's Convolutional Neural Network (CNN) with rule-based differential peak analysis, to systematically map the Jovian
dust environment. This automated pipeline successfully identified over 150,000
dust impacts, effectively isolating
dust signals from intense magnetospheric noise, providing a high-resolution catalog of Jovian microdust distribution and offering a robust technical foundation for future missions. Analysis of the vertical cross-section of the Jovian
halo ring reveals a more detailed
dust distribution structure, with a distinct number density enhancement near the center of the
halo ring. Moreover, we report the continued evidence of
dust populations near or in the Jovian magnetosheath through identification of background
magnetic and
plasma data instant variations during magnetospheric boundary crossings.
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All authors Yuqi Zhang, Shengyi Ye, Yuting Li, Wenyue Li, Guangzhou Wang, Xinya Duanmu
Instrumentation and Detectors · Cross-listed from gr-qc
Optomechanical transfer factors for scattered light noise estimations in the beamtubes of ground-based gravitational wave detectors
M. Andrés-Carcasona
Scattered light is a relevant noise source in current ground-based gravitational-wave detectors and a critical design concern for next-generation observatories. Beamtube scattered light estimates usually combine optical propagation simulations with analytical couplings that do not fully propagate the frequency-dependent optomechanical response of the interferometer to the strain readout. In this work, we use improved analytical transfer factors to convert scattered-light field perturbations in the Fabry–Pérot arm cavities into equivalent strain noise, consistently including radiation-pressure coupling, signal-extraction dynamics, microscopic detunings, and the homodyne readout angle. The formalism keeps the full amplitude and phase quadrature content of the scattered field, including the cross terms that arise in both diffraction and backscattering noise. For diffraction, we also identify the regime in which the linearized coupling to baffle motion is valid, avoiding unnecessary phase wrapping. Using representative LIGO,
Cosmic Explorer (CE), and Einstein
Telescope - Low Frequency (ET-LF) configurations, we show that legacy estimates are recovered in phase dominated regimes, but can differ when radiation-pressure coupling, quadrature correlations, or detuned signal extraction become important. In particular, the revised ET-LF estimate changes substantially with respect to previous beamtube noise budgets due to the detuned signal extraction cavity. These results provide a more complete framework for scattered light noise estimations for present and future gravitational-wave detectors.
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Fluid Dynamics · Cross-listed from stat.AP
Metamodel-based methodology for uncertainty propagation and global sensitivity analysis of tailings dam-breach flows
Yuri Taglieri Sáo, Geraldo de Freitas Maciel and Julian Cardoso Eleutério
Tailings dam-breach analyses are essential for flood-hazard assessment, emergency planning and risk estimation, but their results are strongly affected by uncertainties in breach development, released volume and tailings rheology. This study proposes an efficient probabilistic methodology that integrates uncertainty quantification and global sensitivity analysis for tailings dam-breach studies. High-dimensional outputs (spatial maps) and the computational cost of deterministic simulations are addressed through dimensionality reduction and metamodeling. The methodology is demonstrated on a
benchmark case with complex terrain using HEC-RAS v6.6 and considering uncertainties in breach parameters and rheological properties. The results quantify uncertainty in maximum flow depth and arrival time, characterize their statistical distributions and identify the spatial influence of the main input variables through sensitivity maps. Sensitivity indices reveal the dominance of breach parameters near the dam and yield stress farther downstream. The modular and non-intrusive framework can be coupled with other deterministic models and applied to different dam-breach scenarios, supporting more standardized and risk-informed assessments.
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Biological Physics · Cross-listed from quant-ph
Efficient quantum transport in disordered Floquet networks
Hlér Kristjánsson, Jonathan Brugger, Gabriel Dufour et al. (+2 more)
We propose a mechanism for fast and efficient
quantum transport through disordered networks with variable on-site energies, inspired by photosynthetic complexes. The mechanism relies on an interplay between inter-site couplings of the network and driving by external vibrations. Two design principles are shown to ensure close-to-perfect transport despite the disorder, namely a reflection
symmetry in Floquet-
Hilbert space and the existence of a dominant doublet or triplet of Floquet states.
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All authors Hlér Kristjánsson, Jonathan Brugger, Gabriel Dufour, Christian Scheppach, Andreas Buchleitner
Applied Physics
The Butterfly Defect Effect in Cylindrical Shell Buckling: Non-localized Interactions of Localized Imperfections
Uba K. Ubamanyu, Antoine Blond, John W. Hutchinson et al. (+1 more)
We investigate the buckling of axially compressed cylindrical shells containing localized defects, focusing on interactions among multiple defects and between them and the shell edges. Using high-fidelity finite-element simulations, we first characterize the parameter space of single-dimple sensitivity, defect-edge coupling, and pairwise defect-defect interactions. Building on this deterministic baseline, we run
stochastic simulations of shells with randomly distributed imperfections whose amplitudes are sampled from a log-normal distribution. Post-buckling deformations form non-axisymmetric, butterfly-shaped patterns whose `wings' reach far across the shell surface. This spatial extent induces unavoidable interactions with neighboring defects and the clamped edges, so that buckling is not necessarily initiated by the deepest defect. A larger defect population raises the likelihood of an extreme defect, producing a statistical size effect: as the mean number of defects grows, the mean knockdown factor decreases asymptotically and its variability decays exponentially. The butterfly defect effect qualitatively explains the scatter in historical experimental data by correlating the knockdown factor with the cylinder length-to-thickness ratio ($H/t$) rather than the radius-to-thickness ratio ($R/t$) alone, thereby establishing $H$ as a key parameter for stability alongside $R$ and $t$. Nonetheless, the knockdown factors obtained here remain well above those reported in historical experiments, indicating that the localized Gaussian dimple, though nearly the worst-case imperfection for spherical shells, is not so for cylinders.
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All authors Uba K. Ubamanyu, Antoine Blond, John W. Hutchinson, Pedro M. Reis
Computational Physics · Cross-listed from cs.LG
Thermodynamics-Informed Input Reparameterization for Neural Prediction of Real-Fluid Thermodynamic Properties in Supercritical Combustion
Haoze Zhang, Han Li, Ke Xiao et al. (+3 more)
Real-fluid thermodynamic property evaluation is a major computational cost in supercritical combustion simulations. In the enthalpy-based pressure-correction formulation, the closure evaluates temperature T, density $\rho$, and compressibility coefficient $\psi$ from the solver state (h,p,Y) through enthalpy-temperature inversion and repeated real-fluid equation-of-state evaluations. Neural-network surrogates offer fixed-cost inference, but direct mapping from (h,p,Y) to $(T,\rho,\psi)$ must capture the enthalpy-temperature relation and non-ideal equation-of-state response, resulting in a complex regression problem. This work introduces a thermodynamics-informed input reparameterization strategy, termed target-aligned input reparameterization (TAIR). TAIR replaces the raw enthalpy coordinate of each property network with a target-matched thermodynamic coordinate: the temperature network uses a temperature estimate obtained by inverting a constant-$c_p$ ideal-gas mixture enthalpy approximation, whereas the density and compressibility networks use an ideal-gas density estimate. These algebraic transformations use only solver-available variables and species constants, guiding the networks to learn real-fluid departures from ideal-gas baselines rather than reconstructing the full closure from raw enthalpy. The method is assessed using supercritical methane-oxygen counterflow flame data against a raw-input baseline and target-inconsistent cross-reparameterization controls. TAIR reduces held-out RMSE by factors of about 1.5, 2.0, and 7.5 for T, $\rho$, and $\psi$, respectively. For an unseen strain-rate flame within the augmented thermodynamic envelope, the corresponding factors are 3.6, 14.5, and 6.0. The target-inconsistent controls perform worse, indicating that the gains arise from thermodynamically matched input design rather than generic preprocessing.
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All authors Haoze Zhang, Han Li, Ke Xiao, Yangchen Xu, Runze Mao, Zhi X. Chen
Computational Physics
An interaction potential method for passive and active dynamics of hyperelastic materials
Francesco De Vita, Filippo Caruso Lombardi, Roberto Verzicco et al. (+1 more)
Simulating active biological tissues, such as the myocardium, requires constitutive models that are both physically faithful and computationally efficient. The most common approach relies on finite element methods that accurately discretize the underlying continuum hyperelastic problem which, in turn, require global nonlinear solve at each time step. On the other hand, fast, interaction-potential methods replace the continuum with a network of independent links approximating the mechanical response. We propose an interaction potential formulation for simulating active biological tissues that bridges this gap. The method recasts continuum hyperelastic constitutive laws in terms of tetrahedral edge strain. Unlike classical mass-spring models, the proposed formulation does not approximate the tissue as independent spring elements but preserves the energetic coupling between adjacent edges. Passive tissue mechanics is described by hyperelastic constitutive laws, while active contraction is incorporated through the active-strain multiplicative decomposition. Within the edge-based formulation, the active strain is incorporated through a time-dependent activated reference configuration. We further introduce a strategy for enforcing the material incompressibility constraint while avoiding volumetric locking. The resulting method can be interpreted as an edge-strain representation of a constant-strain tetrahedral continuum element, providing a bridge between continuum mechanics and discrete interaction potential solvers. Numerical tests demonstrate the capability of the method to effectively simulate different hyperelastic constitutive laws and to properly preserve the energy balance equation. Finally, the proposed method is applied to the active deformation of a realistic ventricle, reproducing longitudinal shortening and wall-thickening values consistent with the literature.
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All authors Francesco De Vita, Filippo Caruso Lombardi, Roberto Verzicco, Francesco Viola
Computational Physics · Cross-listed from cond-mat.mtrl-sci
ATLAS: A Foundation Neural Sampler for Amorphous Materials
Mouyang Cheng, Denis Blessing, Botao Yu et al. (+4 more)
Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to sample. Below the glass-transition temperature, conventional molecular dynamics and Monte Carlo become inefficient because equilibration relies on rare barrier-crossing events, while data-driven generative models are constrained by scarce and biased reference ensembles. Here, we introduce ATLAS, an efficient sampler that learns a
diffusion process to generate Boltzmann-distributed amorphous structures directly from a target energy function. Parameterized by an equivariant graph neural network, ATLAS generalizes across system size, temperature, and composition. By exploiting the time reversal of the
diffusion process, it enables efficient estimation of thermodynamic quantities and steering toward target observables. In two-dimensional Kob-Andersen systems, ATLAS reproduces parallel tempering Markov chain Monte Carlo structural distributions, free energies and
entropies, achieving below 0.2% free energy error in the low-temperature glass regime with over 500-fold fewer energy evaluations. In Cu-Zr and Cr-Co-Ni metallic glasses, ATLAS recovers experimentally observed short-range-order trends and steers structures toward prescribed order parameters and optimized bulk
moduli. Moreover, composition-amortized pretraining outperforms composition-specific training from scratch, reduces inverse-design costs by several hundred-fold, and enables sampling with expensive universal machine learning interatomic potentials. Coupled to a large
language model
agent, ATLAS searches an eight-element space for high-
entropy metallic glasses balancing stiffness and ductility, identifying a converged Pareto frontier within 480 oracle evaluations. Together, these results establish ATLAS as a foundation model for sampling, steering and designing amorphous materials.
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All authors Mouyang Cheng, Denis Blessing, Botao Yu, Gerhard Neumann, Mingda Li, Carles Domingo-Enrich, Yuanqi Du
Chemical Physics · Cross-listed from cond-mat.soft
One geometric barrier unifies melting, vitrification and jamming of hard spheres in all dimensions
Sujin B Babu
The Lindemann criterion that a solid loses stability once atomic vibrations reach roughly a tenth of the interparticle spacing, has remained an empirical rule for over a century. The numerical value was reproduced by mode-coupling and replica theories but never isolated as the consequence of a simple, verifiable argument. Here we show that for hard spheres in $d$ dimensions the rule follows from three exact
geometric ingredients. The contact
theorem fixing the coordination number from the equation of state, an isotropy identity fixing how non touching neighbors project onto an escape direction, and a first-passage argument which is derived, in which the elementary hop spans one interparticle spacing rather than one particle diameter. The resulting parameter-free master equation locates the kinetic glass transition, random close packing, the Kauzmann point, glass close packing, and
equilibrium crystal melting in $d=3$–$12$, each to within a few per cent of reported independent simulation and replica-theory values, and places all five on a single barrier surface. The theory makes two predictions that are verifiable, the Lindemann constant, $\c_L(3)=0.13$ per neighbor spacing in $3$ dimensions derived from the theory, which must fall systematically with increasing dimensions. The other being in two dimensions, the current theory predicts the arrest in the volume fraction $\eta_g=0.781$, the jamming at $\eta=0.832$, and both steps of the two-stage melting scenario, all of which are already corroborated by independent simulations and experiments.
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Plasma Physics
Exploring Self-Organization of Charged Dust Dimers in Plasma
Aman Singh Katariya, Amita Das, Mamta Yadav et al. (+1 more)
We investigate the self-organization of charged
dust dimers in
plasma using Molecular Dynamics (MD) simulations, with emphasis on both positional and orientational ordering. For a finite number of dimers confined by a radial electric field, the system evolves from simple arrangements to ring-like structures as the particle number increases. These rings exhibit diverse orientational states, including radial, transverse, and mixed
alignments of the dimer axis, reflecting a strong coupling between spatial confinement and orientational degrees of freedom. For larger systems studied under periodic boundary conditions, bulk-like behavior emerges with coupled positional and orientational correlations. The results highlight the significance of anisotropy in determining
equilibrium structures and demonstrate that orientational order plays a crucial role alongside positional ordering in complex
plasmas with shaped particles. This work provides motivation for experimental studies involving shaped
dust particles to explore orientational ordering phenomena beyond conventional spherical
dust particle systems.
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All authors Aman Singh Katariya, Amita Das, Mamta Yadav, Bibhuti Bhusan Sahu
Atmospheric and Oceanic Physics
On the sensitivity of machine-learned probabilistic weather forecast models to scale-aware scoring rules
Simon Lang, Martin Leutbecher and Sam Hatfield
Probabilistic forecast models can be machine-learned from data using loss functions based on scoring rules such as the Continuous Ranked Probability Score (CRPS). This note summarises a preliminary study comparing versions of AIFS-CRPS, a global weather forecast model, trained with different univariate and multivariate scoring rules that aim to explicitly represent scale-awareness in the loss function. In the first part, we compare the (almost) fair CRPS, a fair global energy score, and a graph energy score based on node neighbourhoods. Across standard verification metrics, forecast skill is broadly similar. In the extratropics we find only small differences, while in the tropics the graph energy score setup performs somewhat better and the global energy score shows some degradation. These results suggest that multivariate scores are a viable alternative to CRPS-based training for global machine-learned weather
forecasting. In the second part of the study, we analyse how different scoring rules and scale-aware loss constraints shape the spectra of forecast fields. It is apparent that any form of explicit scale-awareness improves realism. Here, the largest differences are likely associated with different effective weights per scale.
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Optics
Direct Spatiotemporal Imaging of Charge Carrier Dynamics in Operative Semiconductor Devices
Adriano Cola, Antonio Valletta, Isabella Farella et al. (+2 more)
Understanding charge carrier dynamics within electronic devices is crucial, as it governs both the internal
evolution of signals and the externally measurable current. Yet, direct imaging of these dynamics remains elusive: existing approaches are often indirect or inherently perturbative. Here, we introduce Carrier Dynamics by Pockels Imaging (CDPI), a sensitive, quantitative, and non-invasive method that enables direct access to internal charge dynamics. By probing the local electric field, CDPI visualizes photogenerated electron clouds in real space while simultaneously correlating their
evolution with both conduction and displacement currents, thereby establishing a unified picture of carrier transport. The technique provides direct insight into fundamental mechanisms \textemdash including drift,
diffusion, carrier-carrier interactions, trapping and detrapping \textemdash as well as the spatiotemporal
evolution of carrier cloud morphology. Demonstrated in CdTe radiation detectors with micrometre-scale spatial resolution and nanosecond temporal resolution, CDPI is broadly applicable to systems exhibiting appreciable electro-optic responses. Beyond conventional
semiconductors, the approach extends to organic, perovskite, and two-dimensional optoelectronic materials, offering a new paradigm for directly linking internal carrier dynamics to device-level functionality.
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All authors Adriano Cola, Antonio Valletta, Isabella Farella, Vaclav Dědič, Lorenzo Dominici
History and Philosophy of Physics · Cross-listed from quant-ph
Irreversibility, equilibrium and measurement in quantum mechanics
Christopher J. N. Coveney and Peter V. Coveney
Quantum mechanics is widely recognised as being incomplete. It is not consistent with the second law of thermodynamics and does not provide a scientifically credible physical account of the measurement process, the means by which coherence is broken and classically observable states are recorded. This has led to many ad hoc assumptions being used to account for various properties of
quantum systems, among which is the coherence time of
quantum devices that determines their ability to perform computations. Here, we show that all these properties can be accommodated naturally and consistently in the context of mixing
quantum systems which exhibit continuous spectra, as arises in the thermodynamic limit in
quantum statistical mechanics and
quantum gravity. In particular, for isolated systems we show that the time-reversal
symmetry associated with
unitary time
evolution of the
quantum state gives rise to time-
symmetry breaking and a semi-group
evolution which attains thermodynamic
equilibrium at long times. Moreover, the emergence of this non-
unitary time-asymmetry leads to microcanonical
equilibrium states in which all
quantum coherence is lost and is accompanied by the transformation of pure states into mixtures, leading in turn to an increase in
entropy. Inclusion of a macroscopic measurement apparatus shows how the outcome of a measurement corresponds to the von Neumann projection postulate, arising with probabilities in conformance with the Born rule. The mathematical structure of the theory which applies to
quantum systems with continuous spectra is closely analogous to the classical ergodic theory of dynamical systems and the conditions under which they attain
equilibrium states.
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Chemical Physics · Cross-listed from quant-ph
Quantum limits to chiroptical molecular discrimination
Mikael P. Backlund
Discriminating enantiomer pairs is of central importance in the molecular sciences. The preferential interaction between
chiral light and matter is commonly employed in this discrimination, despite fundamental challenges. Here we present quantitative error bounds for classifying the handedness of a
chiral, randomly oriented,
quantum optical emitter from the perspective of classical and
quantum hypothesis testing. Most interestingly, we find that a collective measurement on $N$
photons can provide an orders-of-magnitude improvement in error rate relative to a corresponding separable measurement.
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Applied Physics · Cross-listed from eess.IV
Fast reconstruction of tensor tomographic X-ray scattering data for real-time applications
André M. Antunes, Daniël M. Pelt and K. Joost Batenburg
X-ray scattering tensor
tomography reveals nanoscale structural orientation in 3D, but its reliance on slow iterative reconstruction limits real-time use. We introduce an extension of a direct reconstruction approach for parallel-beam geometries that computes algebraic filters approximating multiple iterative updates with a single filtering and back-projection step. By explicitly separating the tomographic projector from a view-dependent mixing operator, the method generalizes across tensor representations and modes of (small-angle) X-ray scattering measurement. Simulated and experimental results show that the approach approximates iterative reconstructions while reducing computation time by over an order of magnitude, realizing a reconstruction of a 53x53x53x28 tensor volume in 1 second on commercially-available hardware. The resulting speed and stability enable high-throughput analysis and open the door to real-time scattering-based imaging.
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