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Physics Physics

physics Watch Physics

Thursday 18 June

Data Analysis and Statistics · Cross-listed from stat.ML

Statistical Properties of Training & Generalization

Itay Lavie, Noam Levi and Yonatan Kahn

Deep learning has managed to evade numerous intuitions from classical statistics to achieve unprecedented performance on a number of real-world tasks. In this article, we investigate the key features and surprises of deep learning from a physics-informed perspective, taking care to point out and justify where possible the many choices inherent in constructing a deep learning model. In particular, we review the phenomenon of neural scaling laws and discuss their interplay with the constraints and inductive biases which may be present when applying machine learning to problems in physics.
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Plasma Physics

Dephasingless laser wakefield acceleration in a plasma waveguide

J.P. Palastro, K.G. Miller, C.D. Arrowsmith et al. (+10 more)

Laser wakefield accelerators (LWFAs) provide extremely large accelerating gradients for compact electron accelerators and photon sources but are limited by dephasing, where trapped electrons outrun the accelerating phase of the wakefield. Flying-focus pulses can eliminate dephasing by driving a wake at the vacuum speed of light, but these pulses involve tradeoffs such as varying spot size, long duration, or large plasma volume. Here we show that a spatiotemporally structured laser pulse propagating in a plasma waveguide can drive a wakefield at the vacuum speed of light while maintaining a constant spot size and ultrashort duration. The pulse is formed by superposing plasma-waveguide modes with appropriately selected frequencies. Compared with flying-focus approaches, the waveguide substantially reduces the required plasma volume. Scaling laws and quasi-3D particle-in-cell simulations show that the single-stage energy gain increases linearly with the number of modes used to construct the pulse, enabling larger energy gains or shorter stages than standard LWFA.
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All authors J.P. Palastro, K.G. Miller, C.D. Arrowsmith, R. Almeida, M.R. Edwards, A.L. Elliott, A. Kiewel, A. Konzel, L.S. Mack, D. Ramsey, D. Singh, A.G.R. Thomas, J. Vieira

Optics · Cross-listed from cond-mat.mtrl-sci

Anomalous magneto-optical response at $\mathrm{RuO_2 / WSe_2}$ van der Waals interface

Muhammad Hassan Shaikh, Abhijith Puthiya Veettil, Collin Maurtua et al. (+9 more)

Ruthenium dioxide ($\mathrm{RuO_2}$) has been proposed as an altermagnetic candidate, although its magnetic ground state remains controversial. Here, we probe weak interfacial magnetic states at the surface of (001)-oriented $\mathrm{RuO_2}$ films using the magnetic proximity effect (MPE) in a van der Waals heterostructure consisting of monolayer tungsten diselenide ($\mathrm{WSe_2}$) atop $\mathrm{RuO_2}$. Temperature-dependent magneto-optical spectroscopy reveals an anomalous excitonic energy shift and a deviation from conventional Varshni behavior below 55 K that are absent in an encapsulated $\mathrm{WSe_2}$ control sample. The anomalous shift reverses sign upon field cooling with opposite magnetic field polarity, indicating a magnetic origin. Polarization-resolved measurements further show a nearly field-independent and fluctuating valley splitting in $\mathrm{WSe_2 / RuO_2}$ in strong contrast to the conventional linear Zeeman splitting observed in the control bare $\mathrm{WSe_2}$ sample. These results suggest that the valley states are governed predominantly by interfacial exchange fields associated with weak surface magnetic states in $\mathrm{RuO_2}$, which do not produce a conventional linear Zeeman response within the applied magnetic field range. Importantly, this approach enables direct optical probing of emergent surface magnetism without introducing an additional ferromagnetic layer, positioning MPE-based optical probing as a tool for investigating weak surface magnetism and offering new possibilities for studying magnetic materials with controversial magnetic states.
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All authors Muhammad Hassan Shaikh, Abhijith Puthiya Veettil, Collin Maurtua, Dai Q. Ho, Subhash Bhatt, David T. Plouff, Malitha Gulawita, Kenji Watanabe, Takashi Taniguchi, John Q. Xiao, Anderson Janotti, Chitraleema Chakraborty

Geophysics · Cross-listed from astro-ph.EP

Geophysical and atmospheric implications of $f$O$_{2}$-dependent melting on rocky exoplanets

Mariana Sastre, Tim Lichtenberg, Laurent Soucasse et al. (+3 more)

The geochemical evolution of long-lived magma oceans is strongly regulated by volatile exchange between the molten mantle and the atmosphere. For planets inside the runaway-greenhouse limit, this coupled evolution can persist for billions of years. However, most existing studies assume Earth-like (oxidized) conditions and neglect the influence of redox state on melt thermodynamics and volatile release. We quantified how experimentally derived, oxygen-fugacity-dependent melting curves implemented within the coupled interior-atmosphere framework PROTEUS propagate into the thermal structure, melt fraction, and rheological evolution of rocky exoplanet interiors, applying this to the short-period super-Earth GJ 1132 b. We found strongly non-linear thermal responses to variations in melting curves. In volatile-poor systems, reduced melting curves promote earlier deep-mantle crystallisation relative to oxidised and Earth-like cases, favouring late-stage surface magma oceans sustained by greenhouse warming, while oxidized melting curves maintain higher melt fractions and a vertically extended magma ocean. Reduced mantles produce massive H$_2$-CO-rich atmospheres; oxidized mantles favour thinner H$_2$O-CO$_2$ envelopes. In volatile-rich systems, the interior reaches radiative equilibrium at high melt fractions, sustaining a steady-state global magma ocean in which melting curve variations do not significantly influence solidification timing. This indicates a hierarchical control: volatile inventory and surface oxygen fugacity act as the primary regulators of thermal state, while oxygen-fugacity-dependent melting relations provide a secondary modulation. These contrasting regimes produce distinct atmospheric compositions and formation timescales, offering testable spectral predictions for close-in rocky exoplanets evaluable with forthcoming JWST observations.
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All authors Mariana Sastre, Tim Lichtenberg, Laurent Soucasse, Dan J. Bower, Harrison Nicholls, Inga Kamp

Fluid Dynamics · Cross-listed from quant-ph

Operator learning for efficient quantum computation

Paul Over, Sergio Bengoechea, Leonardo Borello Busilacchi et al. (+3 more)

An efficient implementation of quantum algorithms is often hindered by the lack of efficient primitives for operators and state preparation. This limits both the ability of near-term quantum hardware to simulate complex problems and the potential of fault-tolerant algorithms to achieve practical quantum advantage. To address this, we propose a framework that maps discrete operators onto compact, structured quantum circuits. The resulting variational circuits can be tailored to the connectivity and long-range interaction of the target hardware. The learning process employs backpropagation together with a cost function that efficiently optimizes unitary operators and non-unitary –dense or sparse– operators using only a single ancilla qubit for block encoding. Additionally, we introduce a regularization term that reduces the approximation error. The approach is validated for both quantum mechanical and engineering applications. In the former case, we learn propagators that arise in native quantum problems –such as quantum simulation and quantum chemistry– and achieve improved resource scaling in comparison to standard Suzuki–Trotter expansions. In the latter case, we demonstrate the approach's ability to implement the second-order central finite difference approximation of the Laplace operator –relevant for solving partial differential equations– while improving upon current error metrics. The final example deals with learning a dense, non-unitary operator that arises in the analysis of inviscid potential flow around an airfoil. These results motivate the extension of the framework to a broader class of operator-synthesis problems beyond the prototypical engineering and quantum applications considered here.
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All authors Paul Over, Sergio Bengoechea, Leonardo Borello Busilacchi, Martin Kiffner, Thomas Rung, Alexios A. Michailidis

Instrumentation and Detectors

Nuisance-Aware Muon Tomography

Zhizheng Zhao, Changhao Qin, Rongfeng Zhang et al. (+5 more)

Cosmic-ray muon scattering tomography can image dense, shielded, or inaccessible objects without an artificial radiation source. In a compact magnet-free tracker, however, each accepted muon provides only a few hit positions and no event-by-event momentum measurement. The downstream hit residual is therefore a compound observable: target scattering, muon momentum, detector resolution, support material, air scattering, and track extrapolation all enter the same measured displacement. We introduce Nuisance-Aware Muon Tomography (NAMT), a residual-likelihood reconstruction method for magnet-free trackers. The upstream hits define the incident track, downstream hit residuals carry the scattering signal, and a radiation-length density field $\lambda=1/X_0$ predicts their material-induced variance through a path integral. NAMT marginalizes the unmeasured momentum with a shared event-level scattering scale and uses open-field blank scans to fix detector and environmental residuals before object reconstruction. On eight Geant4 benchmark scenes spanning strong, weak, and negative scattering contrast, NAMT-4P reaches a mean area under the ROC curve (AUC) of $0.916$ at $120$k effective muons and $1$ mm hit error, compared with $0.784$ for ASR, $0.749$ for MLS-EM, and $0.643$ for PoCA. NAMT-3P uses one downstream hit plane in reconstruction and still reaches $0.909$ mean AUC at the reference setting, while giving the highest reference mean contrast-to-noise ratio (CNR) and the best mean AUC at $30$k muons.
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All authors Zhizheng Zhao, Changhao Qin, Rongfeng Zhang, Zibo Qin, Ziyu Xu, Xingyu Xiao, Qiang Li, Qite Li

Computational Physics · Cross-listed from cond-mat.mtrl-sci

Large spin splitting at ferromagnetic surfaces of bulk antiferromagnets

William A. Schaarman and Sophie F. Weber

We use density functional theory and model Hamiltonians to reveal large spin splitting of bands localized at ferromagnetic surfaces of bulk antiferromagnets (AFMs). There is great interest in material platforms combining the robustness and ultrafast dynamics of AFMs with large, functional spin splitting which is often restricted to ferromagnets (FMs). Here, we show that a subset of AFM surfaces which have symmetry-allowed magnetization can host large spin splitting via bulk degeneracy lifting of sublattice-resolved exchange splittings. We find that the spin splitting is maximized for two ferromagnetic surface motifs: terminations with single uncompensated magnetic sublattices, and two-sublattice surfaces whose sublattices are magnetically compensated in the bulk, but acquire distinct crystal field environments via surface truncation. The latter case can yield FM-like spin splitting magnitudes while also having small uncompensated magnetization. We confirm these predictions with first-principles calculations of $\mathrm{Cr_2O_3}$ and $\mathrm{FeF_2}$, finding splittings as large as $1~\mathrm{eV}$ depending on the surface in question. Our findings point to intrinsic surface symmetry breaking as a route to large, functional spin splitting in an expanded range of AFM materials.
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Atomic Physics · Cross-listed from quant-ph

Applications of quantum annealing to magnetic dipole hyperfine structure constants: First results beyond energies for atoms

Boni Paul, Subimal Deb, Per Jönsson et al. (+2 more)

We report the first results of the magnetic dipole hyperfine structure (HFS) constants of neutral $\mathrm{Li}$, Li-like $\mathrm{Be}$, neutral $\mathrm{Na}$, and Na-like $\mathrm{Mg}$ using a modified version of the Quantum Annealer Eigensolver (QAE) algorithm on D-Wave's quantum hardware. The results are benchmarked against relativistic configuration interaction with multiconfiguration Dirac Hartree-Fock (MCDHF) calculations using the General-purpose Relativistic Atomic Structure Package (GRASP), and simulated annealing. In our modified QAE, a zooming-and-sigma-annealing approach with a floating-point encoding scheme is adopted to estimate the ground-state eigenvalue and eigenvector of the relativistic Dirac-Coulomb Hamiltonian matrices ($H_{\mathrm{DC}}$) constructed from 11 or fewer configuration state functions (CSFs). For calculations with extended correlation orbital sets, we applied a CSF truncation scheme, retaining only CSFs (up to 12) that make significant contributions to the ground-state wavefunction. Our modified QAE precision is kept limited to three decimal places (up to 10 qubits). Hardware demonstrations on the D-Wave quantum processing unit (QPU) yielded results that were completely consistent with GRASP (at the chosen precision) in determining the magnetic dipole HFS constants, with accuracy varying across systems and $H_{\mathrm{DC}}$ matrix dimensions.
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All authors Boni Paul, Subimal Deb, Per Jönsson, Jörgen Ekman, Bhanu Pratap Das

Atmospheric and Oceanic Physics

PRecover 1.0: Process Rate Recovery with Machine Learning

Miriam Simm, Tom Beucler and Corinna Hoose

Comprehensive information on cloud microphysical process rates from numerical simulations allows for better understanding of precipitation formation pathways and aerosol-cloud interactions. However, resource limitations often make it impractical to include all microphysical process rates in the model output, limiting in-depth analyses. To address this shortcoming, we introduce PRecover, a data-driven post-processing approach to recover microphysical process rates that are not stored during runtime from standard output of a numerical weather prediction model. In particular, we train random forests, gradient boosting models, and feed-forward neural networks to recover microphysical process rates from a two-moment bulk microphysics scheme in the ICOsahedral Nonhydrostatic (ICON) model. We use cloud variables as input, obtained from high-resolution simulations in a limited-area setup over Europe. Warm-rain and ice microphysical process rates are recovered with a two-step classification-regression approach for both instantaneous and accumulated process rates. As a physics-based baseline, we assess whether process rates can be directly recalculated from stored ICON output variables. Accurate recalculation is possible for process rates such as accretion and self-collection but not for the autoconversion, rain melting or heterogeneous ice nucleation rate. Using PRecover, we successfully recover most of the process rates that are accumulated over output time steps of 10 minutes or less, but the values are increasingly difficult to recover for rates accumulated over longer accumulation intervals. To quantify predictive uncertainty, we provide calibrated prediction intervals through conformalized quantile regression. We demonstrate spatial transferability of the models with two case studies over different regional domains and simulation settings unseen during training.
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Computational Physics · Cross-listed from quant-ph

Multi-objective design of photon blockade for bright single-photon sources

Sunkyu Yu, Xianji Piao and Namkyoo Park

High-quality single-photon sources, realized through saturable emitters, photon blockade, or heralded pair generation, are indispensable building blocks for photonic quantum platforms. Although these mechanisms suppress multiphoton emission through distinct principles typically captured by analytical models, their practical implementation is constrained by conflicting requirements for purity, brightness, and indistinguishability, which must be balanced within high-dimensional design landscapes. Here, we propose a computational framework for optimizing competing metrics of single-photon sources. Building on a Liouville-space adjoint formulation that efficiently evaluates multiple objectives in Markovian open quantum systems, we develop a Jacobian-based update, which ensures first-order monotonic reduction of multi-objective costs. By incorporating simulated annealing to escape gradient-vanishing plateaus, our framework achieves a design success rate of nearly 60 % for photon blockade with g2(0) smaller than 0.1 and theoretically bounded brightness across a broad parameter space, without any analytical guidance. This framework provides a general recipe for multi-objective design of open quantum systems.
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Computational Physics · Cross-listed from quant-ph

Optimizing resource allocation for accuracy in noisy variational quantum algorithms

Harshit Verma, Thomas Ayral, Alexia Auffèves et al. (+1 more)

For quantum algorithms to achieve their full potential, we need methodologies to optimize them, such as reaching a given output accuracy with minimal resource costs. Here, we develop such a methodology for a class of Noisy Intermediate-Scale Quantum (NISQ) algorithms. We leverage simulations of a Variational Quantum Eigensolver (VQE) to propose a phenomenological model of such algorithms that captures the complex relationship between algorithmic accuracy, algorithmic resource costs, and the noise that exists in realistic quantum hardware. For this, we take the algorithmic resource cost to be the total number of quantum gate-operations in the algorithm; minimizing this cost typically makes the algorithm faster and more energy-efficient. We consider the subtle trade-off between quantum circuit size (small circuits are too imprecise, but large ones are too noisy), and the number of iterations of that quantum circuit for the full algorithm to sufficiently converge. Using a noise-metric-resource methodology, we identify the sweet spot (of circuit size versus iterations) that minimizes the algorithmic resource costs for a desired algorithm accuracy. It also gives the circuit size that maximizes algorithm accuracy for a fixed resource cost. Our methodology provides a practical guideline for near-term deployment of variational algorithms on realistic noisy hardware, including hardware that uses error mitigation.
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All authors Harshit Verma, Thomas Ayral, Alexia Auffèves, Robert Whitney

Optics

High-Power Laser Drives Motion in Ultra-thin Photonic Crystal Lightsails via Radiation Pressure

Lucas Norder, Ata Keşkekler and Richard A. Norte

Laser-driven lightsails have emerged as a promising route for accelerating ultralight spacecraft to high speeds using beamed optical energy. Realizing this concept pushes the limits of light-matter interaction, materials science, structural engineering, and nanomechanical design. A central challenge is to create nanophotonic reflectors that combine ultralow mass, large illuminated area, and survival under high optical power densities. No previous experiment has combined these constraints in a single structure sufficient to produce measurable radiation-pressure displacement. Here, we report the largest subwavelength tethered lightsails to date: nanoscale-thickness, millimeter-wide silicon nitride membranes patterned with billions of holes. Despite their subwavelength thickness, they achieve 99% reflection through resonant photonic modes, combining ultralow areal density with high reflectivity. Their compliance enables radiation-pressure displacements of up to 1.75 micrometer, a 50,000-fold increase over previous lightsail optomechanical responses. These thin mirrors are shown to withstand and maintain high reflectivity under directed laser intensities comparable to optical intensities at the surface of the Sun. Together, these results establish a testbed for high-power nanophotonics, directed-energy systems, and light-driven propulsion, defining the practical limits of ultrathin photonic materials under intense optical loading.
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Data Analysis and Statistics · Cross-listed from q-fin.ST

Trends, Volatility, Correlations, and Critical Phenomena in Financial Markets

Sara A. Safari and Christoph Schmidhuber

We forecast future volatilities and correlations of financial markets based on the current trends in these markets. This complements previous work that models future expected returns by a cubic polynomial of the current trend strength. Empirically, we observe that volatilities and correlations tend to increase day after day in times of strong up- or down-trends. This effect is particularly pronounced in down-trends. It can be accurately quantified by quadratic polynomials of today's trend strengths, which refine common mean-reversion models of volatilities and correlations. Our results improve the prediction of market risk by accounting for market trends. They also support a recent proposal to model financial markets by a lattice gas near its critical point.
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Fluid Dynamics

A high-fidelity numerical database for free-stream-turbulence-induced transition under varying pressure gradients

Louenas Zemmour, Xavier Gloerfelt and Paola Cinnella

Free-stream-turbulence-induced transition under streamwise-varying pressure gradients, as encountered on turbine blades, has received much less attention in high-fidelity simulations than its zero-pressure-gradient counterpart. The ERCOFTAC T3 experiments provide a carefully documented reference for this problem and remain central to transition-model calibration. Complementing them with full-field, three-dimensional data opens new opportunities for the development of physics-based and machine-learning transition models. To this end, wall-resolved implicit large-eddy simulations of the T3A, T3B, and T3C2–C5 flat-plate cases are performed with a high-order compressible solver, using an upstream slip-wall block that lets the synthetic free-stream turbulence develop up to the leading edge. The simulations reproduce well the measured transition locations, integral quantities, and velocity profiles, and predict a separation bubble near the end of the plate for the lowest-Reynolds-number case T3C4. As the Reynolds number decreases and transition moves into the adverse-pressure-gradient region, the mechanism evolves continuously from classical bypass transition to transition governed by the tendency of the laminar boundary layer to separate, up to separation-induced transition. Intermittency, conditioned statistics, and Reynolds-stress budgets document this evolution and the associated non-equilibrium effects, which challenge current RANS transition models.
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Computational Physics

A Social Force Model of the Evacuation from a Big Box Store

Gavin A. Buxton

We include elliptical cross-sections to physically represent people, and irregular polygons to represent wheelchair users, in an anisotropic social force model whose velocity and angular dependence also captures the social tendency for people to avoid walking into one another. Physical interactions are included that depend on the area of overlap between people, or obstacles, to capture normal forces that resist compression and tangential forces that resist sliding motion. The model is further extended to include decision making capabilities, small social groups, the spread of panic, and herding behavior. A large box store is simulated during an evacuation where people move through the store, along the shortest path, to their desired exits. The effects of exit choice, or the perceived availability of exits, on exit times is elucidated. It is found that ignoring 'staff only' exits, and only exiting from the main entrances, can significantly increase average egress times.
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Physics and Society

The Moving Target of Urban Equity: Spatiotemporal Demand and Double Disadvantage in Hefei, China

Shirui Zhou, Matteo Bruno, Mattia Mazzoli et al. (+5 more)

Equitable access to essential urban services is a pillar of modern planning, yet most accessibility models rely strictly on static residential locations, ignoring how demand shifts throughout the daily loop. This study introduces a population-based, temporally differentiated framework to examine the resulting "moving target" of urban equity, focusing on medical facilities and green spaces in Hefei, China. Utilising large-scale mobile phone GPS data, we construct dynamic residential and workplace population exposure surfaces to capture shifting hourly demand. We then evaluate accessibility via network-based travel times paired with a novel per-capita provision metric that accounts for real-time demand competition. We define double disadvantage as the co-occurrence of poor spatial accessibility and insufficient per-capita service availability. Counterintuitively, the results reveal that double-disadvantaged areas cluster primarily along the inner suburban belt rather than the remote periphery, where per-capita service provision remains relatively sufficient. Furthermore, temporal shifts drastically alter equity landscapes: daytime workplace concentrations intensely exacerbate demand competition in urban job centres. These findings demonstrate that urban inequality depends heavily on spatiotemporal population flows rather than just the fixed location of services. Ultimately, achieving true urban equity requires dynamic planning interventions that address time-varying demand rather than focusing solely on static, home-based metrics.
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All authors Shirui Zhou, Matteo Bruno, Mattia Mazzoli, Junfang Tian, Rui Jiang, Enwan Zhang, Zheng Li, Vittorio Loreto

Optics

Caustic-Driven Fluidic Microlenses for Enhanced Nonlinear and High-Energy-Density Physics

Sourabh Singh, S. Sree Harsha, Tamanna et al. (+1 more)

We demonstrate that caustic microlensing occurring in a liquid jet efficiently drives linear, nonlinear, and high-energy-density phenomena. In the linear regime, caustics provide localized focusing, distinct from external high-NA optics. In the nonlinear regime, they enhance the input field at the liquid-air interface and boost surface-sensitive processes. In the high-energy-density domain, caustic-driven localized laser absorption generates gigapascal shocks using microjoule femtosecond pulses, with scalability up to repetition rates of 0.2 MHz. Caustic-driven fluidic microlensing offers opportunities for surface nonlinear optics, ultrafast science, and high-energy-density physics.
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All authors Sourabh Singh, S. Sree Harsha, Tamanna, Prashant Kumar Singh

Computational Physics

Advancing Threshold-Inception Modeling for Predictive Simulation of Ionic Wind Fan Performance

Siim Heering, Juri Volodin, Vootele Mets et al. (+5 more)

This study investigates the predictive capability of a threshold inception-based multiphysics modeling approach for ionic wind fans by direct comparison with experimental measurements. A wire-to-cylinder electroaerodynamic (EAD) fan with variable electrode spacing is used as a reference system to assess the model's ability to reproduce airflow characteristics, discharge current, and performance trends under atmospheric conditions. Numerical simulations show good qualitative agreement with experimental results across all tested configurations; however, systematic deviations emerge at higher voltages and larger electrode gaps. Analysis of these discrepancies indicates that the commonly adopted assumption of perfectly smooth emitter surfaces can limit model accuracy. Experimental characterization of the emitter wire reveals micro-scale surface protrusions, which locally enhance the electric field and alter corona inception behavior. Incorporating representative surface roughness into the numerical model improves quantitative agreement with measured airflow velocities. The results demonstrate that while the threshold inception model provides a robust foundation for EAD fan simulations, electrode surface morphology is a critical factor for reliable prediction. This work advances the validation and refinement of ionic wind fan modeling methodologies and identifies key considerations for the development of more accurate engineering-oriented simulation tools.
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All authors Siim Heering, Juri Volodin, Vootele Mets, Rasmus Talviste, Jüri Raud, Karl-Eerik Unt, Indrek Jõgi, Veronika Zadin

Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Hartree-Fock Limit for Energies in Solids

Jānis Užulis and Andris Gulans

This study establishes a route to the Hartree–Fock (HF) limit for molecules and solids within the linearized augmented plane wave (LAPW) framework. We remove current limitations of the standard LAPW approach to nonlocal exchange by constructing radial basis functions and core orbitals consistently with the HF Hamiltonian. The presented method yields total energies of molecules and solids with a precision of a few $\mu$Ha, and we use it to provide reference data for 14 semiconductors and insulators. For the systems considered in this study, the standard approach based on (semi)local potentials for constructing radial basis functions and core orbitals remains highly precise for practical relative energies, including molecular and solid-state formation energies and Si self-interstitial defect formation energies. More broadly, the results provide stringent all-electron benchmarks for basis-set and pseudopotential assessment, improve error control in hybrid-functional calculations within LAPW, and open the way to X-ray spectroscopy simulations within LAPW based directly on hybrid-functional core orbitals.
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Chemical Physics

Can DFT-trained neural network potentials reproduce structure, solvation, and water-exchange properties in aqueous magnesium solutions?

Sebastian Falkner, Pablo Montero de Hijes, Christoph Dellago et al. (+1 more)

Magnesium ions play an essential role in many biological processes but remain challenging to model in biomolecular simulations. Despite considerable scientific effort, classical force fields fail to simultaneously reproduce key structural, thermodynamic and kinetic solution properties, likely due to their inability to explicitly account for quantum many-body effects. Here, we develop and systematically benchmark MACE neural network potentials (NNPs) for aqueous MgCl$_2$ solutions trained on revPBE-D3/zd and revPBE0-D3/zd density functional theory reference data and assess their ability to reproduce a broad range of experimental solution properties including the structure of the first hydration shell, diffusion coefficient, activity derivative, water-exchange rate and mechanism as well as solvation free energy. Both NNPs accurately reproduce the octahedral structure of the first hydration shell, ion pairing properties and diffusion coefficients. Combining the NNPs with transition path sampling and other enhanced sampling techniques allows us to capture the rare event of water exchange in the first hydration shell of Mg$^{2+}$ revealing a dissociative exchange mechanism. Transition interface sampling yields exchange rates within one order of magnitude of experiment, representing a substantial improvement over classical dissociative force fields. In contrast, the NNP-derived solvation free energy significantly underestimates the experimental value, revealing a limitation of the present local NNP architectures for describing ion solvation thermodynamics. Our results demonstrate that DFT-trained NNPs can accurately describe Mg$^{2+}$ hydration structure, diffusion, ion pairing, and exchange kinetics, while highlighting the need for explicit long-range electrostatic treatments to achieve quantitative agreement with experimental ion solvation free energies.
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All authors Sebastian Falkner, Pablo Montero de Hijes, Christoph Dellago, Nadine Schwierz

Optics

Time- and frequency-domain study for electron beams penetrating dielectric nanospheres: fingerprints of Cherenkov and transition radiation

Wenhua Zhao, Christos Tserkezis, N. Asger Mortensen et al. (+1 more)

We present a theoretical study of Cherenkov and transition radiation for swift electron beams penetrating dielectric nanospheres using material models of different sophistication. Specifically, we perform a combined time-domain (numerically, via the discontinuous Galerkin time-domain method) and frequency-domain (numerically and analytically, via Mie-based theory) study, including the induced-field distribution, cathodoluminescence (CL) multipole/directional decomposition, as well as the time-dependent angular power flow. For low velocities below the Cherenkov threshold, we show that transition radiation is dominant in the far-field CL, and the near-fields at the transition points are primarily responsible for the main features observed in the far-field. For higher velocities far beyond the Cherenkov threshold, we identify the fingerprints of the observable Cherenkov front. Specifically, a constant-permittivity model allows us to isolate the respective contributions of CR and TR to the far-field radiation, thereby facilitating the interpretation of the results for a more realistic material model that includes material resonances. Our combined time- and frequency-domain framework provides a direct view of radiative excitation channels for swift electron beams penetrating dielectric nanoparticles, thereby revealing their interplay beyond the conventional frequency-domain analyses.
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All authors Wenhua Zhao, Christos Tserkezis, N. Asger Mortensen, Kurt Busch

Optics · Cross-listed from cond-mat.mtrl-sci

Terahertz frequency upconversion by coherently driving charge dynamics in the InSb/CdTe heterostructure

Pai Peng, Mingxiang Pan, Jiuming Liu et al. (+10 more)

We investigate terahertz (THz) harmonic generation in the InSb/CdTe heterostructure, demonstrating, for the first time, efficient in-plane magnetic field-induced second-harmonic generation (SHG). We also achieve significant third-harmonic generation (THG), rivalling Dirac materials such as graphene and Cd3As2. Our theoretical analysis identifies the primary SHG mechanism as the orbital-Zeeman correction to Drude conductivity, while the dominant THG contribution also shows Drude-like behavior. The results provide a general route to efficient THz harmonic generation in high mobility materials.
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All authors Pai Peng, Mingxiang Pan, Jiuming Liu, Yi Yang, Lei Wang, Hao Lin, Zehao Hu, Jianlin Luo, Tao Dong, Xufeng Kou, Xinbo Wang, Huaqing Huang, Luyi Yang

Applied Physics

Temperature-Dependent Charge Transport in USD-Grown High-Purity Germanium: Interplay Between Freeze-Out and Multi-Scattering Mechanisms

Narayan Budhathoki, Dongming Mei, Abhinna Rajbanshi et al. (+1 more)

We report temperature-dependent charge transport measurements in p-type high-resistivity germanium crystals grown at the University of South Dakota. Hall-effect and four-probe resistivity measurements were performed on five planar samples over the temperature range of 2-300 K. The apparent Hall mobility exceeds 10$^6$ cm$^2$ V$^{-1}$ s${^-1}$ at cryogenic temperatures and decreases systematically with increasing temperature, while the effective Hall carrier concentration exhibits strong carrier freeze-out behavior at low temperatures. The combined evolution of Hall mobility, effective Hall carrier concentration, and resistivity reveals distinct transport regimes associated with carrier freeze-out, extrinsic conduction, and phonon-limited scattering. The transport behavior is interpreted using a Matthiessens-rule-inspired phenomenological mobility model motivated by the combined influence of ionized impurity, neutral impurity, and acoustic phonon scattering. Variations among samples are correlated with differences in effective Hall carrier concentration and transport behavior. These measurements establish a transport baseline for USD-grown high-resistivity germanium crystals and provide guidance for future material optimization toward detector-grade high-purity germanium for low-background rare-event detector applications.
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All authors Narayan Budhathoki, Dongming Mei, Abhinna Rajbanshi, Rongying Jin

Atmospheric and Oceanic Physics

Enhanced Gulf Stream Path Variability Under Intensified Stratification

Lennard Miller, Antoine Venaille, Stephane Popinet et al. (+1 more)

Increased upper-ocean stratification is an unavoidable consequence of global warming and will strongly impact the structure of ocean currents. Using a high-resolution ocean model, we show that intensification of stratification leads to the loss of coherence of the Gulf Stream Extension, replacing its steady eastward path with vigorous, chaotic meanders. This regime shift persists independently of changes in the Atlantic Meridional Overturning Circulation and surface wind forcing. Enhanced meandering under intensified stratification also proves to be a robust feature across both idealized and realistic ocean models that resolve mesoscale eddies, but is not captured by coarse-resolution models that parameterize eddies. The presented findings therefore highlight the need for improved representations of oceanic turbulence in climate projections.
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All authors Lennard Miller, Antoine Venaille, Stephane Popinet, Bruno Deremble

Chemical Physics · Cross-listed from cond-mat.soft

Constraint-Limited Tube Orientation of Entangled Polymers in Oscillatory Shear Deformation

Dario Nichetti and Alessio Zaccone

We develop a molecularly motivated description of the nonlinear index (NLI) in oscillatory shear deformation of entangled polymers. The central assumption is that the shear component of the tube-orientation tensor cannot grow without bound. Convective constraint release (CCR), chain stretch, and tube dilation progressively reduce the number and lifetime of orientational constraints, but the maximum shear alignment of a tube segment is geometrically limited by $S_{xy}\leq 1/2$. This motivates a constraint-limited orientation closure in which the NLI first grows approximately with strain amplitude and then approaches the limiting value $\mathrm{NLI}_{\max}=3$ asymptotically rather than through an artificial cutoff. The same framework yields a molecular expression for the characteristic half-saturation strain $\gamma_s$, defined by $\mathrm{NLI}(\gamma_s)=3/2$, in terms of the entanglement number, oscillation frequency, and a critical number of remaining orientational constraints. We further derive architecture-dependent expressions for the nonlinear onset strain $\gamma_c$ for linear, sparsely long-chain-branched, and more regularly branched polymers. The resulting framework provides a compact bridge between Fourier harmonic analysis, CCR-based tube dynamics, and the progressive loss of orientational memory in highly deformed entangled polymer liquids.
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Optics

Temporal dissipative solitons and optical frequency combs in coherently driven Kerr resonators

Stuart G. Murdoch, François Leo, Xiaoxiao Xue et al. (+2 more)

Kerr frequency combs have recently emerged as an exciting new photonic technology, with applications across science and engineering. Their formation within driven optical resonators that possess a Kerr nonlinearity is enabled through the rich landscape of localized nonlinear dissipative structures intrinsic to these systems. This article offers a comprehensive review of the physics that underpins these nonlinear comb-generating structures. Particular attention is placed on bright temporal cavity solitons and nonlinear switching waves – the canonical stable comb-generating states in the anomalous and normal dispersion regimes, respectively. Written as both a review and tutorial, the article also includes an in-depth treatment of the numerical methods required to simulate driven Kerr resonators, alongside a comprehensive discussion of the laboratory techniques used to experimentally realize and characterize Kerr combs.
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All authors Stuart G. Murdoch, François Leo, Xiaoxiao Xue, Stéphane Coen, Miro Erkintalo

Geophysics

Acceleration methods for the planar 3D ILSA hydraulic fracturing model

V. I. Shukalo, A. V. Valov and A. N. Baykin

Planar 3D models of hydraulic fracturing provide a practical balance between models with restrictive geometric assumptions and fully 3D simulators, capturing fractures with arbitrary planar footprints at moderate computational cost. Nevertheless, applications such as treatment design optimization and mini-frac test interpretation require large ensembles of simulations, for which the cost of planar 3D models remains a significant bottleneck. This work presents acceleration strategies for the planar 3D Implicit Level Set Algorithm (ILSA) to reduce simulation runtime while preserving numerical accuracy. A unified planar 3D ILSA scheme that consolidates the nested loops of the elastohydrodynamic solver and the front tracking algorithm into a single iterative process is introduced. A matrix splitting approach is applied to the linearized elastohydrodynamic system, moving the dense part of the elasticity operator to the right-hand side and yielding a sparse system matrix that can be solved more efficiently. Anderson acceleration is incorporated into the solution of the elastohydrodynamic system to improve convergence. A predictor–corrector scheme is additionally examined in combination with these methods. Each technique is evaluated individually and in combination on both the reference and unified schemes across five benchmark cases. Numerical experiments demonstrate that the unified scheme alone delivers an average 2.3x speed-up, reaching 3.8x for the Sandglass case. The combined application of all techniques achieves an average 4.2x speed-up and up to 14.4x for the Sandglass case. The schemes are also assessed during fracture closure, where the matrix splitting and Anderson acceleration prove most effective, together reaching up to 3.4x.
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Plasma Physics · Cross-listed from astro-ph.SR

Once more: Leaky MHD waves in coronal magnetic flux tubes

Hans Goedbloed and Rony Keppens

By a detailed comparison of leaky magnetohydrodynamic waves in coronal magnetic flux tubes with leaky electromagnetic waves in dielectric media it is shown that the latter kind may be called quasi-normal modes, since they can be regularised by a normalisation which systematically cuts off the contribution of the external homogeneous region, whereas such a possibility is forbidden for the former kind by the conservation of magnetic flux. Consequently, leaky magnetohydrodynamic waves cannot be systematically applied to coronal seismology, i.e. to the inverse spectral problem of determining the different equilibrium distributions of the fields by comparing the spectra they produce with the observed ones.
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Optics

A unified resource-pool architecture for high-dimensional direct-detection optical communication

Jingze Liu, Zhijuan Gu, Xinyang Yu et al. (+12 more)

Increasing optical communication capacity without proportionally increasing receiver complexity remains a key challenge for direct-detection links. Conventional systems typically assign wavelength, polarization and intensity to fixed, separately recovered functions, so that alphabet expansion is accompanied by additional demultiplexing, polarization handling, receiver branches and electronic processing. Here we introduce a unified resource-pool architecture for high-dimensional direct-detection optical communication, in which wavelength, polarization and intensity are jointly organized as a composite optical symbol space and recovered through optical-domain joint projection rather than dimension-by-dimension separation. The receiver is implemented with an integrated disordered photonic processor that transforms each composite optical state into a reproducible multi-output electrical fingerprint for single-shot direct recovery. In a dual-wavelength transmission experiment, the system resolves 4096 composite symbols, corresponding to 12 bits per symbol slot, with a bit error rate of 4.25e-4 after 10 km standard-fiber transmission. Additional experiments demonstrate dense polarization alphabets, wavelength-indexed state-space expansion and high-launch-power operation over hollow-core fiber. These results establish disorder-enabled joint projection in an integrated photonic processor as a route to hardware-efficient high-dimensional direct-detection communication beyond conventional dimension-partitioned receiver architecture.
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All authors Jingze Liu, Zhijuan Gu, Xinyang Yu, Ziwen Zhou, Zhuyixiao Liu, Mingming Zhang, Yuxuan Xiong, Peng Li, Zhongyao Luo1, Jiajie Yuan, Hao Wu, Zhipei Sun, Siqi Yan, Yu Yu, Ming Tang

Computational Physics · Cross-listed from hep-ph

Two Flavon Froggatt-Nielsen Models with Genetic Algorithms

Miguel Crispim Romão and Stephen F. King

We present the first systematic and comprehensive scan of two-flavon Froggatt-Nielsen (FN) models, employing artificial intelligence techniques to explore the high-dimensional, mixed discrete-continuous parameter space. Extending the standard single-flavon FN framework to a two-flavon setup in which separate flavon fields couple independently to the up- and down-type sectors, we demonstrate that the relative phase between their vacuum expectation values (vevs) provides a natural and generic source of CP violation absent in single-flavon models. To explore this enlarged model space, we cast the search for phenomenologically viable models as a multi-objective optimisation problem, formulating each experimental constraint as a separate objective, and employ the Non-dominated Sorting Genetic Algorithm III to simultaneously fit all 18 FN charges, 45 Wilson coefficients, and flavon parameters to both the quark and lepton sectors. Our approach requires no separate training phase and identifies phenomenologically viable models orders of magnitude faster than prior reinforcement learning methods. Imposing experimental constraints on CKM and PMNS mixing angles and CP phases, charged fermion masses, and neutrino squared-mass differences, we discover over $100\,000$ unique viable models with a remarkably low duplication rate, indicating that the space of valid two-flavon FN realisations has not been exhausted. Both Normal and Inverted neutrino mass squared orderings are realised, with the relative hierarchy between the flavon vevs producing qualitatively distinct predictions for the effective neutrinoless double beta decay mass $m_{ee}$. We further demonstrate the existence of minimal FN realisations with maximal flavon exponent as small as three, and of models reproducing charged fermion masses to within $6\%$ without any dedicated continuous parameter optimisation.
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