Fluid Dynamics
Experimentally Resolving Gravity-Capillary Wave Evolution in Vessels of Unknown Boundary Conditions
Sean M. D. Gregory, Vitor S. Barroso, Silvia Schiattarella et al. (+2 more)
The geometries of surface wave modes are determined by the highly nontrivial interplay of capillarity and wetting effects at the boundaries of their domain. Aside from idealised scenarios, this commonly leads to unknown boundary conditions, thereby hindering theoretical formulation and experimental analysis. To address this problem, we introduce Extracted Mode Tracking (EMT), a data-analysis framework to obtain instantaneous amplitude and phase content of axisymmetric surface-wave modes from spatio-temporal measurements. This approach uses unsupervised machine learning techniques to extract a basis of wave modes directly from collected data; the spatial profiles require no prior theoretical modelling, and so the issue of unknown boundary conditions is circumvented. Time-resolved mode amplitudes are reconstructed by
geometric fitting at each recorded time-step, and the success is evaluated by a spectral signal-to-noise quantifier. Capabilities and limitations of EMT are systematically benchmarked on synthetic
datasets, finding strong resilience against noise, improved accuracy over alternative methodologies, and the ability to operate with restricted domains which poses significant merit for use in experimental systems with limited measurement field-of-view. Finally, we conduct a Faraday-wave experiment in a regime highly sensitive to boundary effects in order to further validate the method, and demonstrate the observational access to nonlinear wave-dynamics enabled by EMT. These results establish EMT as a general tool for analysing wave mode dynamics of axially-symmetric fluid interface systems, and open pathways for quantitative studies of nonlinear mode-interactions, stability, and
turbulence.
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All authors Sean M. D. Gregory, Vitor S. Barroso, Silvia Schiattarella, Anastasios Avgoustidis, Silke Weinfurtner
Chemical Physics
Machine learning the two-electron reduced density matrix in molecules and condensed phases
Jessica A. Martinez B., Bhaskar Rana, Xuecheng Shao et al. (+2 more)
Machine learning is rapidly accelerating materials and chemical discovery, but most current models target energies, forces, or selected molecular properties rather than the underlying many-body electronic structure. Learning electronic-structure proxies, such as reduced density
matrices, offers a path to surrogates that can predict a broad range of observables from a single ML model. Short of learning the full wavefunction, the two-electron reduced density
matrix (2-RDM) is among the most information-rich, minimally lossy targets, providing direct access to expectation values of arbitrary one- and two-electron operators regardless of the strength of the underlying electron correlation. Here we show that learning the 2-RDM is a feasible goal, yielding exceptionally accurate models. We develop surrogates for correlated wavefunction methods (including configuration interaction and coupled cluster) that yield 2-RDMs with sufficient fidelity to provide direct, training-free access to energies and forces for driving energy-conserving molecular dynamics. To tackle realistic molecular condensed phases, we leverage a many-body expansion of the 2-RDM, using our ML models to supply the expansion terms and enabling ML-powered, coupled-cluster-quality electronic structure and energetics for large solvated systems. As a demonstration, we showcase a coupled-cluster-level electronic-structure calculation of glucose solvated by 500 water molecules achieved at Hartree-Fock cost. This work establishes a general framework for learning correlated electronic structure with high fidelity and deploying it to systems beyond the reach of conventional ab initio methods.
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All authors Jessica A. Martinez B., Bhaskar Rana, Xuecheng Shao, Katarzyna Pernal, Michele Pavanello
Computational Physics · Cross-listed from cs.LG
Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model
Gyujun Jeong, Sungwon Cho, Minji Shon et al. (+13 more)
Ferroelectric field-effect transistors (FeFET)-based vertical NAND (Fe-VNAND) has emerged as a promising candidate to overcome z-scaling limitations with lower programming voltages. However, the data retention of 3D Fe-VNAND is hindered by the complex interaction between charge detrapping and ferroelectric depolarization. Developing optimized device designs requires exploring an extensive parameter space, but the high computational cost of conventional Technology Computer-Aided Design (TCAD) tools makes such wide-scale optimization impractical. To overcome these simulation barriers, we present a Physics-Informed Neural Operator (PINO)-based AI surrogate model designed for high-efficiency prediction of threshold voltage (Vth) shifts and retention behavior. By
embedding fundamental physical principles into the learning architecture, our PINO framework achieves a speedup exceeding 10000x compared to TCAD while maintaining physical accuracy. The resulting surrogate provides a physics-consistent data engine for compact model parameter extraction and look-up-table (LUT) generation, directly supporting reliability-aware SPICE simulation of Fe-VNAND. This study demonstrates the model's effectiveness on a single FeFET configuration, serving as a pathway toward modeling the retention loss mechanisms.
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All authors Gyujun Jeong, Sungwon Cho, Minji Shon, Namhoon Kim, Woohyun Hwang, Kwangyou Seo, Suhwan Lim, Wanki Kim, Daewon Ha, Prasanna Venkatesan, Kihang Youn, Ram Cherukuri, Yiyi Wang, Suman Datta, Asif Khan, Shimeng Yu
Chemical Physics
For molecular polaritons, disorder and phonon timescales control the activation of dark states in the thermodynamic limit
Tianchu Li, Pranay Venkatesh, Qiang Shi et al. (+1 more)
Collective light-matter systems host an extensive
manifold of dark states whose role in the emergence of thermodynamic behavior remains poorly understood, especially in the presence of disorder and structured environments. Here, we develop a hybrid
matrix product state-hierarchical equations of motion (MPS-HEOM) approach that enables numerically exact simulations of polariton dynamics from a few emitters to the thermodynamic limit under both static and dynamic disorder. This allows us, for the first time, to provide a quantitative and operational answer to the long-standing question of what is the minimum system size required to reach the thermodynamic limit in collective polaritonic systems. By introducing a convergence scale, $N_{T}$, i.e., the number of molecules required for the photonic dynamics to reach the thermodynamic limit, we show that dynamic disorder generally poses a greater computational challenge than static disorder. We attribute this behavior to the suppression of collective light-matter dynamics by disorder, which dynamically activates non-collective degrees of freedom. We further find that $N_{T}$ exhibits a turnover behavior as the bath becomes more Markovian, as the bath timescales regulate bright-to-dark energy transfer and the involvement of dark and gray states. Hence,
phonon timescales control both the breakdown of collective behavior and the growth of $N_{T}$. Our results establish the suppression of collective behavior as the key mechanism governing thermodynamic convergence in disordered light-matter systems.
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All authors Tianchu Li, Pranay Venkatesh, Qiang Shi, Andrés Montoya-Castillo
Instrumentation and Detectors
Detective Quantum Efficiency of the Timepix4 Hybrid Pixel Detector and its Application to Parallel-Beam Diffraction
Zhiyuan Ding, Nina Dimova, Jonathan S. Barnard et al. (+6 more)
The detective
quantum efficiency (DQE) and normalised noise power spectrum (NNPS) of the Timepix4 hybrid pixel detector in event-driven mode in TEM have been measured at 100 kV and 200 kV. In a raw data readout mode, the zero-frequency DQE exceeds 0.9 at both 100 kV and 200 kV. At the Nyquist frequency, the DQE remains above 0.2 at 100 kV but drops close to zero at 200 kV. Initial parallel-beam diffraction data from a polycrystalline gold nanoparticle sample is reported which shows that at 200 kV Timepix4 can detect weak diffracted information beyond a 75 mrad half-angle.
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All authors Zhiyuan Ding, Nina Dimova, Jonathan S. Barnard, Giulio Crevatin, Liam O'Ryan, Richard Plackett, Daniela Bortoletto, Angus I. Kirkland, Marcus Gallagher-Jones
Computational Physics
How Physical Dynamics Shape the Properties of Ising Machines: Evaluating Oscillators vs. Bistable Latches as Ising Spins
Abir Hasan and Nikhil Shukla
Ising machines exploit the natural dynamics of physical systems to minimize the Ising
Hamiltonian and thereby address computationally hard combinatorial optimization problems. This paradigm has motivated a range of physical implementations. In the electronic domain, coupled networks of oscillators and bistable latches have emerged as two prominent realizations of Ising machines and are the focus of the present work. Despite this common abstraction, we demonstrate that differences in the underlying physical dynamics of oscillators and latches lead to fundamentally different stability properties of the resulting dynamical systems. Specifically, we show analytically that in Bistable Latch Ising Machines (BLIMs) all discrete Ising configurations possess identical linear stability, whereas in Oscillator Ising Machines (OIMs) the Jacobian spectrum depends explicitly on the
spin configuration, enabling selective destabilization of higher-energy states. We further corroborate this analysis using finite-noise perturbation experiments initialized near prescribed Ising configurations. These results highlight how the characteristics of the device nonlinearity directly shape the local dynamical properties of Ising machine implementations.
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Instrumentation and Detectors
First Optical Observation of Negative Ion Drift at Surface Pressure
F. D. Amaro, R. Antonietti, E. Baracchini et al. (+41 more)
We report the first observation of Negative Ion Drift (NID) at surface pressure of $900 \pm 7$ mbar at Laboratori Nazionali del Gran Sasso in a He:CF$_4$:SF$_6$ mixture using an optically read out Time Projection Chamber (TPC) within the CYGNO/INITIUM project. We present the first PMT waveform analysis in the NID regime, interpreting the temporal light pattern through a model that combines track geometry and charge transport. The inferred drift velocities correspond to mobilities of O(cm$^2$ V$^{-1}$ s$^{-1}$), consistent with negative ion transport. The observed linear scaling of the time extension mean with drift distance reveals the presence of a faster minority charge carrier population in addition to the dominant SF$_6^-$ species, drifting at a $\sim$25% higher velocity under external inputs. These results demonstrate multi-species negative ion drift operation at surface pressure in a He:CF$_4$:SF$_6$ mixture and open a concrete path toward large scale, low
diffusion optical TPCs for rare event searches.
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All authors F. D. Amaro, R. Antonietti, E. Baracchini, L. Benussi, C. Capoccia, M. Caponero, L. G. M. de Carvalho, G. Cavoto, I. A. Costa, A. Croce, M. D'Astolfo, G. D'Imperio, G. Dho, F. Di Giambattista, E. Di Marco, J. M. F. dos Santos, D. Fiorina, F. Iacoangeli, Z. Islam, H. P. Lima Jr., G. Maccarrone, R. D. P. Mano, D. J. G. Marques, G. Mazzitelli, P. Meloni, A. Messina, C. M. B. Monteiro, R. A. Nobrega, I. F. Pains, E. Paoletti, F. Petrucci, S. Piacentini, D. Pierluigi, D. Pinci, A. A. Prajapati, F. Renga, A. Russo, G. Saviano, P. A. O. C. Silva, N. J. C. Spooner, R. Tesauro, S. Tomassini, S. Torelli, D. Tozzi
Instrumentation and Detectors · Cross-listed from cond-mat.mtrl-sci
AIMD-L: An automated laboratory for high-throughput characterization of structural materials for extreme environments
Todd C. Hufnagel, Pranav Addepalli, Anuruddha Bhattacharjee et al. (+14 more)
Rapid developments in artificial intelligence and machine learning as applied to materials science are creating an urgent need for experimental data, which can be provided by high-throughput and autonomous laboratories. To date most demonstrations of such laboratories have focused on functional materials, with less
attention paid to structural materials. We present here the Artificial Intelligence in Materials Design Laboratory (AIMD-L), an automated, high-throughput facility for characterizing the microstructure and properties of structural metals and ceramics, with an emphasis on materials in extreme environments. AIMD-L has two custom instruments for characterization of structural materials: HELIX for shock studies of materials, and MAXIMA for X-ray diffraction and X-ray fluorescence spectroscopy. Specifically designed for high-throughput studies, HELIX and MAXIMA are each capable of collecting data at rates two to three orders of magnitude faster than conventional systems. A third experimental station, SPHINX, is a commercial nanoindenter modified for integration into the automated workflow of AIMD-L. A user (which may be human or an AI
agent) directs the experiments to be carried out by means of a centralized control program. The experimental stations are linked by a conveyance that moves samples around the lab, with a
robot at each station for sample transfer in/out of the instrument. The experimental stations also communicate with a common data layer that streams data autonomously from each instrument to a data portal, where their arrival triggers automated workflows for data reduction and analysis. The processed data are immediately available to the human operator or agentic AI, forming a closed loop for rapid decision-making and experimental control.
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All authors Todd C. Hufnagel, Pranav Addepalli, Anuruddha Bhattacharjee, Rohit Berlia, Jaafar El-Awady, David Elbert, Lori Graham-Brady, Axel Krieger, Harichandana Neralla, T. Joseph Nkansah-Mahaney, Mostafa M. Omar, Hyun Sang Park, K.T. Ramesh, Matthew Shaeffer, Eric Walker, Piyush Wanchoo, Timothy P. Weihs
Chemical Physics · Cross-listed from quant-ph
Operational impact of quantum resources in chemical dynamics
Julia Liebert and Gregory D. Scholes
Quantum coherence and other non-classical features are widely discussed in chemical dynamics, yet it remains difficult to quantify when such resources are operationally relevant for a given process and observable. While
quantum resource theories provide a comprehensive framework for comparing free and resourceful settings, existing approaches typically rely on resource monotones or on performance bounds under free operations, and do not directly quantify the maximal influence a chosen resource can exert on a fixed chemical dynamics. Here, we introduce task specific, process level quantifiers that upper bound the largest change a
quantum resource can induce in a target figure of merit. Central is a resource impact functional $\mathcal{C}_M(\Lambda)$, defined by comparing a state with its paired resource-free counterpart under the same
quantum channel $\Lambda$, which admits an operational interpretation in binary hypothesis testing. We derive variation and time bounds that constrain how rapidly a resource can modify a target signal, providing resource-aware analogues of
quantum speed limits. Moreover, we show that open system dynamics can be decomposed into free and resourceful components such that only the resourceful component contributes to $\mathcal{C}_M(\Lambda)$, thereby isolating the parts of a generator responsible for resource-induced changes in the observable. We illustrate the framework exemplary for energy transfer in a donor-acceptor dimer in two analytically solvable regimes. Our results provide a general toolbox for diagnosing and benchmarking
quantum resource effects in molecular processes.
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Plasma Physics
Effect of front surface engineering on high energy electron, X-ray and heavy ion generation from Relativistic laser interaction with thick high-Z targets
J. Twardowski, C. Kuz, A. S. Bogale et al. (+20 more)
Relativistic
lasers on solid targets generate hot electrons, and other secondary particles. These particles can be used for radiography, cancer therapy, or isochoric heating. A lower density or structured coating on high-Z targets can improve
laser-target energy coupling and subsequently enhance overall particle emission. In this work performed at the Scarlet Facility, a $10^{21}$ W/cm$^2$ intense pulse was incident on front surface coatings on 1 mm thick Ta. These coatings include a 12 $\mu$m plastic coating, a 50 $\mu$m thick foam coating, and a Au nanowire (NW) coating. Post-damage craters are correlated with reflected light on a MACOR screen, illustrating that less absorption in a target is directly tied to smaller craters. Additionally, more absorption in a target also leads to more MeV electrons and X-rays. Bare targets performed the best for electron and MeV X-ray generation, with X-rays of 30 MeV detected, as coatings tested were too thick and thus experienced lower intensities. Due to this larger spot size, foam and NW-coated targets generated the greatest heavy ion acceleration. Particle-in-
cell simulations tested on bare and plastic-coated targets illustrate that $\sim \mu$m thick plastic coatings perform better than bare Ta. These results underline the importance of density and thickness control of coatings on high-Z materials. In the future, post-damage crater analysis could provide an easy way to
benchmark absorption in a sample, and could later be compared against absorption estimates from particle-in-
cell simulations.
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All authors J. Twardowski, C. Kuz, A. S. Bogale, Z. Su, A. Lee, R. Kaur, M. Eder, Y. Noor, D. P. Broughton, Md Kazi Rokunuzzaman, R. Hollinger, A. Blackston, J. Strehlow, A. Baraona, P. Spingola, G. Tiscareno, D. Hanggi, B. Unzicker, C.-S. Wong, G. K. Ngirmang, F. N. Beg, D. Schumacher, E. Chowdhury
Chemical Physics
From Accurate Quantum Chemistry to Converged Thermodynamics for Ion Pairing in Solution
Niamh O'Neill, Benjamin X. Shi, William C. Witt et al. (+6 more)
Quantitative prediction of thermodynamic properties in solution is essential for translating atomistic simulations into reliable chemical insight. As an exemplar system, the behaviour of CaCO$_3$ in water has been widely studied to understand its mineralization in seawater, with potential implications for carbon-capture strategies. However, making accurate computational predictions has been a long-standing challenge, requiring both highly accurate electronic structure methods and extensive statistical sampling. Here, we combine advances in machine learning and electronic structure theory to fully resolve the ion pairing free energy of CaCO$_3$ with explicit solvation. We show that achieving quantitative agreement with experiment requires going beyond the standard density functional theory up to the "gold-standard" coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]. We generate a set of systematically improvable models, enabling reliable insights into the initial association mechanism of Ca and CO$_3$ ions prior to nucleation while fully quantifying enthalpic and entropic effects. Our results demonstrate that CCSD(T)-level thermodynamic predictions of complex aqueous systems can now be routinely achieved.
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All authors Niamh O'Neill, Benjamin X. Shi, William C. Witt, Blake I. Armstrong, William J. Baldwin, Paolo Raiteri, Christoph Schran, Angelos Michaelides, Julian D. Gale
Applied Physics · Cross-listed from eess.SY
Quantum Technologies and Edge Devices in Electrical Grids: Opportunities, Challenges, and Future Directions
Marjorie Hoegen, René Glebke, M. Sahnawaz Alam et al. (+11 more)
In modern power systems, edge devices serve as local hubs that collect data, perform on-site computing, sense electrical parameters, execute control actions, and communicate with neighboring edge devices as part of the larger grid. However, as the number of monitored nodes and control loops grows, traditional edge devices face serious limits. They can become overloaded by complex signal processing and decision tasks, causing delays and higher energy use. Standard sensors hit a noise floor that prevents them from detecting miniature changes, making it harder to spot early signs of faults or instability. Meanwhile, conventional communication links struggle with bandwidth limits, security risks, and rising encryption demands, which together slow down and weaken the transfer of critical grid information.
Quantum technologies have the potential to overcome these challenges.
Quantum computers can deliver exponential speed-ups for optimization and machine-learning tasks that ordinary processors cannot handle.
Quantum sensors can sense signals with atomic precision, giving edge devices a more precise view of grid dynamics.
Quantum communication techniques, including
quantum key distribution, offer methods to achieve information-theoretic security and ensure that information arrives quickly and without tampering. We explore how
quantum technologies can be integrated into edge devices, highlighting both opportunities and challenges.
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All authors Marjorie Hoegen, René Glebke, M. Sahnawaz Alam, Alessandro David, Juan Navarro Arenas, Nikolaus Wirtz, Mario Albanese, Daniele Carta, Felix Motzoi, Antonello Monti, Carsten Schuck, Andrea Benigni, Klaus Wehrle, Ferdinanda Ponci
Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Physics-Informed Diffusion Model for Generating Synthetic Extreme Rare Weather Events Data
Marawan Yakout, Tannistha Maiti, Monira Majhabeen et al. (+1 more)
Data scarcity is a primary obstacle in developing robust Machine Learning (ML) models for detecting rapidly intensifying tropical cyclones. Traditional data augmentation techniques (rotation, flipping, brightness adjustment) fail to preserve the physical consistency and high-intensity gradients characteristic of rare Category 4-equivalent events, which constitute only 0.14% of our
dataset (202 of 140,514 samples). We propose a physics-informed
diffusion model based on the Context-UNet architecture to generate synthetic, multi-spectral satellite imagery of extreme weather events. Our model is conditioned on critical atmospheric parameters such as average wind speed, type of Ocean and stage of development (early, mature, late etc) – the known drivers of rapid intensification. Using a controlled pre-generated noise sampling strategy and mixed-precision training, we generated $16\times16$ wind-field samples that are cropped from multi-spectral satellite imagery which preserve realistic spatial autocorrelation and physical consistency. Results demonstrate that our model successfully learns discriminative features across ten distinct context classes, effectively mitigating the data bottleneck. Specifically, we address the extreme class imbalance in our
dataset, where Class 4 (Ocean 2, early stage with average wind speed 50kn hurricane) contains only 202 samples compared to 79,768 samples in Class 0. This generative framework provides a scalable solution for augmenting training
datasets for operational weather detection
algorithms. The average Results yield an average Log-Spectral Distance (LSD) of 4.5dB, demonstrating a scalable framework for enhancing operational weather detection
algorithms.
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All authors Marawan Yakout, Tannistha Maiti, Monira Majhabeen, Tarry Singh
Atomic Physics · Cross-listed from gr-qc
Fundamental Limits of Quantum Sensors for Gravitational Wave Detection
Sergio Gaudio
Recent advances in
quantum sensing—optical clocks, frequency-dependent squeezing below the standard
quantum limit, and
quantum magnetometers—raise a natural question: can these technologies detect gravitational waves directly, or enhance existing detectors? We show that the answer is set primarily by the coupling mechanism between wave and sensor. From the
tidal Hamiltonian in
Fermi normal coordinates we identify three physically distinct mechanisms by which a gravitational wave couples directly to a
quantum system, and derive their transducer gains within linearized general relativity and non-relativistic
quantum mechanics. Internal atomic coupling yields a transducer gain $G_A = 2.4\times 10^{-20}$, with vanishing first-order energy shifts for all $J=0$ clock states—a $\sim\!10^{35}$ deficit relative to
laser interferometry. Center-of-mass (Doppler) coupling reaches strain sensitivities of $\sim\!10^{-18}$, still $10^4$ above LISA requirements. Only light-propagation coupling provides the enormous transducer gain that makes
laser and atom interferometry viable. For detectors of this third kind we quantify the
quantum enhancement accessible through the noise architecture: LISA's noise budget is predominantly classical, capping the combined enhancement at $\mathcal{E}\approx 1.04$, while ground-based detectors in the shot-noise-dominated regime achieve $\mathcal{E}=1.6$–$2.1$. Atom interferometers exploit the same mechanism to target the 0.01–10~Hz band.
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Fluid Dynamics
Boltzmann-Curtiss Description for Flows under Translational Nonequilibrium
Mohamed M. Ahmed, Mohamad I. Cheikh and James Chen
Continuum-based theories, such as Navier-Stokes equations, have been considered inappropriate for flows under nonequilibrium conditions. In part, it is due to the lack of rotational degrees of freedom in the Maxwell-Boltzmann distribution. The Boltzmann-Curtiss formulation describes gases allowing both rotational and translational degrees of freedom and forms morphing continuum theory (MCT). The first order solution to Boltzmann-Curtiss equation yield a stress tensor that contains a coupling coefficient that is dependent on the particles number density, the temperature and the total relaxation time. A new bulk viscosity model derived from the Boltzmann-Curtiss distribution is employed for shock structure and temperature profile under translational and rotational nonequilibrium. Numerical simulations of argon and nitrogen shock profiles are performed in the Mach number range of 1.2 to 9. The current study, when comparing with experimental measurements and Direct Simulation Monte Carlo (DSMC) simulation, show a significant improvement in the density profile, normal stresses and shock thickness at nonequilibrium conditions than Navier-Stokes equations. The results indicate that equations derived from the Boltzmann-Curtiss distribution are valid for a wide range of nonequilibrium conditions than those from the Maxwell-Boltzmann distribution.
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Fluid Dynamics
Prediction of Steady-State Flow through Porous Media Using Machine Learning Models
Jinhong Wang, Matei C. Ignuta-Ciuncanu, Ricardo F. Martinez-Botas et al. (+1 more)
Solving flow through porous media is a crucial step in the
topology optimisation of cold plates, a key component in modern thermal management. Traditional computational fluid dynamics (CFD) methods, while accurate, are often prohibitively expensive for large and complex geometries. In contrast, data-driven surrogate models provide a computationally efficient alternative, enabling rapid and reliable predictions. In this study, we develop a machine-learning framework for predicting steady-state flow through porous media governed by the Navier-Stokes-Brinkman equations. We implement and compare three model architectures-convolutional autoencoder (AE), U-Net, and
Fourier Neural Operator (FNO)-evaluating their predictive performance. To enhance physics consistency, we incorporate physics-informed loss functions. Our results demonstrate that FNO outperforms AE and U-Net, achieving a mean squared error (MSE) as low as 0.0017 while providing speedups of up to 1000 times compared to CFD. Additionally, the mesh-invariant property of FNO emphasizes its suitability for
topology optimisation tasks, where varying mesh resolutions are required. This study highlights the potential of machine learning to accelerate fluid flow predictions in porous media, offering a scalable alternative to traditional numerical methods.
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All authors Jinhong Wang, Matei C. Ignuta-Ciuncanu, Ricardo F. Martinez-Botas, Teng Cao
Data Analysis and Statistics
Jigar Patel and Tommaso Dorigo
We propose a physics-informed anomaly detection framework for
collider data based on a
Bayesian latent
diffusion model. Our method combines a probabilistic encoder with
diffusion dynamics in the latent space, allowing for stable and flexible density estimation while explicitly enforcing physics constraints, such as mass decorrelation and regularization of latent correlations. We train and test the model on simulated LHC jet data and evaluate its performance using seed-averaged ROC curves together with discovery-oriented metrics. Through a series of ablation studies, we show that the
diffusion process,
Bayesian regularization, and physics-motivated loss terms each contribute in a complementary way: they help stabilize training and improve generalization, even when the gains in peak performance are moderate. Overall, our results emphasize the importance of incorporating both uncertainty estimates and physics consistency when building reliable anomaly detection methods for new Physics searches in high-energy physics.
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Chemical Physics · Cross-listed from cs.LG
A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention
Eric Qu, Brandon M. Wood, Aditi S. Krishnapriyan et al. (+1 more)
Machine-learning interatomic potentials (MLIPs) have advanced rapidly, with many top models relying on strong physics-based inductive biases. However, as models scale to larger systems like biomolecules and electrolytes, they struggle to accurately capture long-range (LR) interactions, leading current approaches to rely on explicit physics-based terms or components. In this work, we propose AllScAIP, a straightforward,
attention-based, and energy-conserving MLIP model that scales to O(100 million) training samples. It addresses the long-range challenge using an all-to-all node
attention component that is data-driven. Extensive ablations reveal that in low-data/small-model regimes, inductive biases improve sample efficiency. However, as data and model size scale, these benefits diminish or even reverse, while all-to-all
attention remains critical for capturing LR interactions. Our model achieves state-of-the-art energy/force accuracy on molecular systems, as well as a number of physics-based evaluations (OMol25), while being competitive on materials (OMat24) and catalysts (OC20). Furthermore, it enables stable, long-timescale MD simulations that accurately recover experimental observables, including density and heat of vaporization predictions.
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All authors Eric Qu, Brandon M. Wood, Aditi S. Krishnapriyan, Zachary W. Ulissi
Atomic Physics · Cross-listed from quant-ph
An Atomic Interface for High-Dimensional Temporal Mode Quantum Networks
Shicheng Zhang, Aonan Zhang, Ilse Maillette de Buy Wenniger et al. (+5 more)
Temporal modes of
photons are a promising encoding scheme for high-dimensional
quantum networks due to their high channel capacity and fiber compatibility. However, realizing their full potential requires devices capable of synchronizing, processing and interfacing these modes across photonic and atomic bandwidths. In this work, we demonstrate a programmable high-dimensional temporal mode processor using a Raman
quantum memory in warm cesium vapor. We exploit the single-mode nature of the Raman interaction kernel, dynamically shaping the control field to synthesize a tunable coherent filter that selectively addresses specific temporal waveforms. This mechanism enables on-demand storage, filtering, and conversion, providing a coherent interface between MHz- and GHz-bandwidth modes. We validate the platform's selectivity across a basis of 30 orthogonal Hermite-Gaussian modes and certify high-fidelity
quantum operation via 5-dimensional process
tomography. By combining deterministic mode conversion with bidirectional bandwidth interfacing, we establish the Raman
memory as a critical active node for scalable
quantum information processing.
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All authors Shicheng Zhang, Aonan Zhang, Ilse Maillette de Buy Wenniger, Paul M. Burdekin, Jerzy Szuniewicz, Steven Sagona-Stophel, Sarah E. Thomas, Ian A. Walmsley
Atmospheric and Oceanic Physics
Evaluating the Predictability of Selected Weather Extremes with Aurora, an AI Weather Forecast Model
Qin Huang, Moyan Liu, Yeongbin Kwon et al. (+1 more)
AI weather foundation models now achieve forecast skill comparable to numerical weather prediction at far lower computational cost, yet their predictability for high-impact extremes across dynamical regimes remains uncertain. We evaluate Aurora using an event-based framework spanning tropical cyclones, freezes, heatwaves, atmospheric rivers, and extreme precipitation at lead times from 1 to 21 days. Aurora demonstrates strong short-range (1-7 day) skill across event types, including competitive tropical cyclone track accuracy and high spatial agreement for temperature and moisture extremes. However, a consistent subseasonal failure mode emerges: while large-scale circulation patterns remain moderately skillful at 14-21 day leads, threshold-based extreme intensity collapses as fields regress toward climatology. This divergence indicates that Aurora retains synoptic-scale dynamical structure but loses surface-impact amplitude beyond 7-10 days. The practical predictability horizon for deterministic AI extreme-event
forecasting therefore remains constrained by intrinsic atmospheric dynamics.
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All authors Qin Huang, Moyan Liu, Yeongbin Kwon, Upmanu Lall
Plasma Physics
An Overview of Relativistic Particle Pushers and their Extension to Arbitrary Order Accuracy
Holger Schmitz
Particle in
Cell (PIC) simulations have become a vital tool for the investigation of kinetic processes in
plasma physics. Many of the systems investigated with PIC simulations contain particles with relativistic velocities. The correct integration and the knowledge of possible sources of errors in relativistic particle trajectories is of importance to accurately judge the validity of the simulation results. Over the past few decades, various new integration schemes for relativistic particle trajectories in PIC simulations have been proposed. These are aimed at improving numerical accuracy in specific scenarios. This article presents a comprehensive comparison of particle pushers with a focus on explicit schemes. An important class of these schemes is found to be generalisable to arbitrary high order. A comparison of the fourth order variants of these schemes with their second order counterpart is also presented.
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Medical Physics
Rotation-invariant graph message passing enables acquisition protocol generalisation in learning-based brain microstructure estimation
Leevi Kerkelä and Hui Zhang
Estimating
brain microstructure has important applications in medicine and neuroscience.
Diffusion-weighted
magnetic resonance imaging enables measuring microstructure in vivo. Conventional biophysical model fitting can be accurate but is slow and impractical for time-critical clinical use, where machine learning can offer a potential route to rapid estimation. We address the problem of microstructure estimation under arbitrary acquisition protocols where most existing learning-based methods fail due to protocol assumptions, requiring retraining when the protocol changes. We present a graph neural network that represents input data as a point cloud in the 3D space where
diffusion-weighted measurements are made and performs rotation-invariant message passing with permutation-invariant pooling, producing fixed-size
embeddings that encode microstructure. The inductive biases of our relatively small model were guided by the underlying physics and
symmetries of the problem rather than by generic model architectures. Trained on randomised simulated data, our model demonstrates domain generalisation, accurately estimating microstructure from data with unseen real-world protocols without retraining. This approach represents a step towards a "train once, deploy anywhere" architecture, bringing rapid learning-based microstructure mapping closer to clinical deployment.
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Physics Education
Learning Together: A Format for Reflective Turn-Based Sharing in STEM Communities
James Day, Katherine R. Herperger and Kyle Monkman
Here, we present Learning Together: a simple, low-cost format for structured speaking and listening on historical, cultural, and equity-related topics within a physics community. In this article, we describe the process of running these hour-long sessions, including what is needed, how to set expectations for participants, and practical facilitation moves that help participants reflect safely on unfamiliar and sometimes difficult material. We aim to offer a replicable recipe that instructors and departments can adapt to their own contexts.
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Biological Physics · Cross-listed from cond-mat.other
Density of States Weighted Decoherence Probe Formalism for Charge Transport in DNA
Hashem Mohammad and M.P. Anantram
Nanoscale molecular systems such as DNA require an atomistic
quantum treatment to accurately capture their electrical properties, owing to their small dimensions. A central challenge in modeling transport through these systems is the inclusion of phase-breaking scattering. Decoherence-probe methods enable such modeling for large systems, but existing implementations have limitations. Energy-independent scattering rates tend to overly broaden energy levels, yielding an unphysically large density of states (DOS) within energy gaps. Conversely, energy-dependent models may introduce spurious energy levels and transmission peaks and require additional fitting parameters. To address these issues, we use a DOSweighted decoherence model in which the scattering rate and equivalently, the associated decoherence probe self-energy is proportional to the local DOS. The model iteratively updates the decoherence selfenergy and the DOS until self-consistency is achieved. This approach yields energy and spatially dependent scattering rates that avoid spurious energy levels without the excessive broadening of DOS in energy gaps. We also examine the impact of partitioning schemes that prevent artificial pathways for charge transport and discuss how they can be avoided. Overall, the DOS-weighted model provides an improved and more physically grounded framework for simulating charge transport in DNA and potentially other weakly coupled molecular systems.
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Space Physics · Cross-listed from astro-ph.SR
Velocity dispersion of Solar Energetic Particles in turbulent heliosphere
T. Laitinen and S. Dalla
Solar Energetic Particles (SEPs) are a signature of
solar eruptions, and to link them to acceleration mechanisms many studies investigate their injection time at the Sun, $t_{sun}$. We assess velocity dispersion analysis (VDA), an often-used method to derive $t_{sun}$. We use full-orbit simulations of 1–100 MeV SEP
protons in a novel model of the interplanetary
magnetic turbulence superposed on a Parker Spiral
magnetic field. The
turbulence is described analytically as dominant transverse fluctuations that are 2D with respect to the mean field, supplemented with a minor contribution of asymptotically slab
turbulence modes. We determine simulated SEP intensities for three
turbulence strengths and use VDA to obtain $t_{sun}$ and the apparent path length $s$ of the SEPs, employing an SEP onset threshold to mimic a realistic energetic
proton background before the SEP event. We find that
turbulence strongly affects $t_{sun}$ and $s$. For weak and moderate
turbulence, VDA estimates of $t_{sun}$ are 2-16 minutes after the actual
solar injection time, and the path lengths are 0.2-0.3 au longer than the Parker spiral. For strong
turbulence, the path lengths are $>5$ au, considerably longer than those typically obtained from SEP observations. We also investigate the effect of energy-dependence of the pre-event
proton background, and find that different background spectra result in 5-20-minute difference in VDA injection times, depending on the heliolongitude. We conclude that in many cases VDA-derived injection times include a significant contribution from
turbulence and/or the pre-event background and are not an accurate estimate of the acceleration time.
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Instrumentation and Detectors
Comprehensive characterization of a YAG:Ce scintillator: light yield, alpha quenching and pulse-shape discrimination
L. Gironi, S. Dell'Oro, E. Giussani et al. (+4 more)
Solid-state scintillators are widely used in particle and applied physics due to their versatility and resistance to diverse environments and operating conditions. This broad range of applications calls for thorough characterization of scintillating
crystals. Among these materials, cerium-doped yttrium aluminum garnet (YAG:Ce) is a promising scintillator owing to its favorable timing characteristics, high light yield, good mechanical properties, and chemical stability. In this work, we report a comprehensive experimental characterization of a YAG:Ce
crystal exposed to both $\gamma$ and $alpha$ radiation. We extract the scintillation decay time and light yield, and study their
evolution from room temperature down to approximately $-50 ^\circ$ C. We perform a detailed investigation of the quenching factor for \al particles in the energy range from about $6$ MeV down to $1$ MeV, finding a value that decreases from approximately $0.17$ to $0.10$. We also explore the possibility of pulse-shape discrimination based on the different signal
evolution depending on the interaction type, demonstrating strong classification capabilities. These results provide a detailed assessment of the performance of \YAG for radiation-detection applications and offer insight into its potential use in environments requiring reliable particle identification and stable response across a wide range of operating conditions.
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All authors L. Gironi, S. Dell'Oro, E. Giussani, C. Gotti, E. Mazzola, M. Nastasi, D. Peracchi
Computational Physics · Cross-listed from cond-mat.supr-con
Exotic Pressure-Driven Band Gap Widening in Carbon Chain-Filled KFI Zeolite and Its Pathway to High-Pressure Semiconducting Electronics and High-Temperature Superconductivity
C.T.Wat, K.C.Lam, W.Y.Chan et al. (+6 more)
Semiconducting devices face persistent challenges in operating at high pressure, as the band theory predicts that materials transition to a more metallic state under compression. However, our findings with carbon chains in KFI substrates reveal a conditional deviation from this norm. We not only witness the transition from polyyne (
semiconductor) to cumulene (metal) at medium pressure, but we also observe an unexpected re-entrance of the polyyne at high pressures, where the band gap in the polyyne increases with pressure. In addition, the synthesis of long cumulene chains has posed a longstanding challenge in the quest for high-temperature organic superconductivity. We have identified critical conditions for synthesizing extended cumulene chains within zeolite frameworks, highlighting the interplay between unconventional charge density waves and significant torsions. The KFI zeolite facilitates the formation of carbon chains exceeding 5,000 atoms, in stark contrast to around 100 other zeolites that are limited to ~10 atoms. The cumulene@KFI system demonstrates a superconducting transition temperature reaching ~62 K, surpassing the highest reported values for bulk iron-based
superconductors. This interplay between carbon structures and superconductivity not only advances our understanding of charge density waves but also heralds a new era in the study of novel applications
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All authors C.T.Wat, K.C.Lam, W.Y.Chan, C.P.Chau, S.P.Ng, W.K.Loh, L.Y.F.Lam, X.Hu, C.H.Wong
Computational Physics
Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants
Dmitry Korogod, Alexander V. Shapeev and Ivan S. Novikov
We present two models with explicit long-range electrostatics in the form of Coulomb interactions. Both models include point charges depending on their local atomic environments, and the second model also conserves a total charge of an atomic system. We combine the proposed long-range models with local Moment Tensor Potential and demonstrate that they reduce the training errors of the MTP models fitted on the same training sets including the CH$_3$COO$^-$+4-methylphenol and CH$_3$COO$^-$+4-methylimidazole organic dimers (non-periodic systems) and the NaCl
crystal (periodic system). For the organic dimers, the proposed models also give qualitatively correct predictions of the binding curves. Furthermore, in this study we introduce a method for calculating
phonon spectra of isotropic materials only via these long-range models fitted to energies, forces, and stresses. The developed long-range model with point charges dependent on atomic environments and conserving total charge is capable of predicting the correct value of the LO-TO splitting in the $\Gamma$-point in the isotropic NaCl. For this system, we also predict dielectric constant from dipole moment fluctuations calculated with molecular dynamics simulations conducted with the developed long-range model. The calculated dielectric constant is in good agreement with experiment. Finally, we demonstrate the broader applicability of the introduced approach by computing the
phonon spectrum of uniaxial tetragonal PbTiO$_3$. Although the method is formally derived for isotropic materials, we show that it is also perspective for uniaxial materials (e.g., PbTiO$_3$) as the spectrum obtained with our long-range interatomic potential corresponds to the one calculated with density functional theory.
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Instrumentation and Detectors
Recent GasPM advances: photon-feedback mitigation and LaB$_{6}$ photocathode studies
Simone Garnero, Kenji Inami, Kodai Matsuoka et al. (+2 more)
We report recent developments and tests with beams and
cosmic rays of the gaseous photomultiplier (GasPM). The GasPM is a photosensor that combines a photocathode with the avalanche-multiplication mechanism of a resistive-plate chamber, offering excellent time resolution and cost-effective scalability. In addition, the GasPM provides precise and efficient Cherenkov-based charged-particle identification if combined with a radiator. Our primary use case aims at an upgrade of the Belle II detector to suppress beam-induced background
photons, preferably detected off-collision time, that degrade the performance of the electromagnetic calorimeter. In 2022 we achieved a promising single-
photon time-resolution of 25 ps at 3.3 x 10$^6$ gain, using a picosecond-pulse
laser and a LaB$_6$ photocathode. However, a 2023 beam test with electrons impinging on a MgF$_2$ window attached to a CsI photocathode showed a worsening to 70 ps. This work aims at addressing the principal causes of the time-resolution degradation. We primarily target ultraviolet-
photon emission during excitation and de-excitation of the gas molecules, which leads to a secondary signal that overlaps the primary signal, spoiling time resolution (
photon feedback). We design and execute an improved beam test. Along with several GasPM configuration changes, we introduce a new 10 GSPS frequency digitizer to better discriminate primary from secondary signals thus enabling the study of
photon feedback. We also conduct a
cosmic-ray test using a LaB$_6$ photocathode, which is known to have higher than CsI's resistance to ions drifting backwards onto the photocathode and to air exposure, to probe
quantum efficiency in view of an upcoming beam test.
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All authors Simone Garnero, Kenji Inami, Kodai Matsuoka, Ryogo Okubo, Koichi Ueda
Applied Physics
Epitaxy of strained, nuclear-spin free $^{76}$Ge quantum wells from solid source materials
Maximilian Oezkent, Chen-Hsun Lu, Lucas Becker et al. (+13 more)
Germanium
quantum well
heterostructures have rapidly emerged as a leading platform for solid-state
quantum information processing; however, material quality limits scalability, and higher structural quality, higher purity, as well as zero
nuclear spin, are required. Here, we address these problems by employing the heaviest of Ge isotopes, by evaporating high-purity $^{76}$Ge radiation detector material, as utilized in fundamental
neutrino particle physics experiments, to fabricate $^{76}$Ge/$^{28}$Si$^{76}$Ge
quantum wells for
quantum applications and explore the respective challenges. Specifically, we demonstrate improved results on strain-relaxed virtual Si$_{0.2}$Ge$_{0.8}$ substrates, forward graded from Si, with a dislocation density below 3.7$\cdot$10$^{5}$ cm$^{-2}$, explore
nuclear spin-free solid-source molecular beam epitaxy, and demonstrate first
quantum transport in $^{76}$Ge
quantum wells. We demonstrate a record-level
quantum well interface width of 0.3 nm by X-ray reflectivity, and quantitatively compare it to atom probe
tomography and scanning transmission electron microscopy. The grown layer reveals
nuclear-
spin-bearing
impurity concentrations below 10$^{19}$ cm$^{-3}$ and chemical
impurity levels below 10$^{18}$ cm$^{-3}$, except for residual carbon attributed to the graphite crucible of the Ge source, which may reach up to 10$^{19}$ cm$^{-3}$. Low-temperature magneto-transport measurements yield electron mobilities of 6.1$\cdot$10$^4$ cm$^2$V$^{-1}$s$^{-1}$ at 15 mK with a carrier density of 2.2$\cdot$10$^{11}$ cm$^{-2}$, indicating that residual carbon is the dominant scattering mechanism.
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All authors Maximilian Oezkent, Chen-Hsun Lu, Lucas Becker, Sebastian Koelling, Robert H. Blick, Eloïse Rahier, Stefan Schönert, Nikolay Abrosimov, Thilo Remmele, Torsten Boeck, Georg Schwalb, Oussama Moutanabbir, Martin Albrecht, Carsten Richter, Jens Martin, Kevin-P. Gradwohl