Atmospheric and Oceanic Physics
Symmetric Composition of Anisotropic Operators for Global Subseasonal-to-Seasonal Climate Forecasting
Ziyu Zhou, Yuchen Fang, Weilin Ruan et al. (+4 more)
Accurate global Subseasonal-to-Seasonal (S2S)
climate forecasting is critical for disaster preparedness and resource management, yet it remains challenging due to chaotic atmospheric dynamics. Despite advances in geometry-aware representations, existing methods do not specify how the zonal and meridional interactions that govern real atmospheric dynamics should be explicitly coordinated and composed, leaving an important architectural gap for S2S
forecasting. In this paper, we propose AnisoCast, which explicitly models anisotropic global atmospheric dynamics for accurate S2S
climate forecasting. It couples: (1) an Anisotropic
Embedding strategy that tokenizes the global grid into latitudinal rings, preserving the integrity of zonal periodic structures; and (2) stacked Aniso Blocks that arrange latent Zonal and Meridional Operators in a weight-shared palindromic composition inspired by symmetric operator splitting. Extensive experiments on the ERA5 reanalysis
dataset demonstrate that AnisoCast establishes a new state-of-the-art, significantly outperforming existing methods in both
forecasting accuracy and computational efficiency.
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All authors Ziyu Zhou, Yuchen Fang, Weilin Ruan, Tian Zhou, Shiyu Wang, James Kwok, Yuxuan Liang
Popular Physics
Infinite multiverses and where to find them?
Anubhav Kumar Srivastava, Pavel P. Popov, Guillem Müller-Rigat et al. (+1 more)
Have you ever watched superhero movies like Spider-Man: Into the Spider-Verse? Or played games where your choices create different outcomes? What if we told you that in the real world, something even crazier might be happening all the time, right under our noses? Imagine shrinking down to the size of an atom. What you'd see wouldn't be like our everyday world at all! This is the realm of
quantum physics, where the rules we know do not apply, where things exist everywhere and nowhere at once. The moment you observe something, it starts behaving differently. In this article, we will explore two of the many possible explanations for such phenomena, namely the Copenhagen interpretation and the many-worlds interpretation of
quantum physics. We will also try to answer the question of whether there are many copies of you roaming around in different universes, and why you haven't met one.
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All authors Anubhav Kumar Srivastava, Pavel P. Popov, Guillem Müller-Rigat, Maciej Lewenstein
Instrumentation and Detectors
Beam Test Performance of AstroPix sensor with 120 GeV protons
Bobae Kim, Regina Caputo, Manoj Jadhav et al. (+11 more)
AstroPix is a high-voltage CMOS (HV-CMOS) monolithic active pixel sensor (MAPS) developed for precision gamma-ray imaging and spectroscopy in the medium energy regime, as well as for precise shower imaging and tracking in the Barrel Imaging Calorimeter (BIC) of the Electron
Proton/Ion
Collider (ePIC) detector at the future Electron-Ion
Collider (EIC). We present beam test results of the AstroPix v3 sensor using a 120 GeV
proton beam at the Fermilab Test Beam Facility (FTBF), performed as part of the broader experimental campaign for the BIC prototype calorimeter. The sensor's 500 um pixel pitch enabled precise measurement of the beam profile, providing important information for calorimeter performance studies. Using the measured 120 GeV
proton data, we measure the energy deposit of minimum ionizing particles and use them to extract the corresponding effective depletion depth.
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All authors Bobae Kim, Regina Caputo, Manoj Jadhav, Sylvester Joosten, Carolyn Kierans, Henry Klest, Adrien Laviron, Richard Leys, Jessica Metcalfe, Jared Richards, Nicolas Striebig, Amanda L. Steinhebel, Daniel Violette, Maria Zurek
Instrumentation and Detectors
Millimeter-scale rigid diamond probe for high sensitivity endoscopic-magnetometry applications
Jihongbo Shen, Heng Yuan, Hongyu Tao et al. (+6 more)
Magnetometry based on diamond nitrogen-vacancy (NV) centers has been extensively studied for applications requiring diverse capabilities, spanning from nanometer spatial resolution to subpicotesla sensitivity. Among various applications, diamond magnetometers can demonstrate high sensitivity
magnetic sensing within millimeter-scale size for endoscopic applications. However, the trade-off between sensitivity and spatial resolution of diamond magnetometry makes it difficult to achieve such a probe. In this study, we present a millimeter-scale rigid diamond magnetometer probe with enhanced sensitivity via optimizing the optical design. By coupling the frustum diamond with the miniaturized compound
parabolic concentrator (CPC) lens, we enhance the fluorescence collection efficiency by 37% within 4 mm diameter, and the achieved sensitivity is 200 pT/Hz1/2 based on the sample with the resonance linewidth of ~8 MHz. With this verified structure, endoscopes with mm-size probe and picotesla sensitivity can be projected for
surgical and industrial applications in the future.
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All authors Jihongbo Shen, Heng Yuan, Hongyu Tao, Zekun Niu, Haoming Xu, Chentao Zhang, Chen Su, Zhuo Wang, Chen Zhang
Physics and Society
Modelling Pedestrian Behaviour in Autonomous Vehicle Encounters Using Naturalistic Dataset
Rulla Al-Haideri and Bilal Farooq
Understanding how pedestrians adjust their movement when interacting with autonomous vehicles (AVs) is essential for improving safety in mixed traffic. This study examines micro-level pedestrian behaviour during midblock encounters in the NuScenes
dataset using a hybrid discrete choice-machine learning framework based on the Residual Logit (ResLogit) model. The model incorporates temporal, spatial, kinematic, and perceptual indicators. These include relative speed, visual looming, remaining distance, and directional collision risk proximity (CRP) measures. Results suggest that some of these variables may meaningfully influence movement adjustments, although predictive performance remains moderate. Marginal effects and elasticities indicate strong directional asymmetries in risk perception, with frontal and rear CRP showing opposite influences. The remaining distance exhibits a possible mid-crossing threshold. Relative speed cues appear to have a comparatively less effect. These patterns may reflect multiple behavioural tendencies driven by both risk perception and movement efficiency.
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Physics and Society
Is Innovation Becoming Less Disruptive? An Inventory of the Literature
Xiangting Wu, Linhui Wu, Michael Park et al. (+2 more)
A growing literature has examined whether innovation is becoming less disruptive, spanning diverse domains and data sources and using a range of methodologies. This paper provides an inventory of 105 studies exploring this question. The evidence is largely consistent in direction. Studies spanning scientific papers, patents, products, legal cases, music, and visual art consistently report evidence of a decline. This pattern holds not only for citation-based measures, but also for text-based approaches, firm displacement rates, product similarity networks, and audio and visual
embeddings. The literature has also identified notable exceptions, including rebounds in specific domains and predictable variation across field lifecycles. We catalog each study's data, methods, and findings to provide a resource for researchers and policymakers seeking to understand the current state of the evidence.
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All authors Xiangting Wu, Linhui Wu, Michael Park, Erin Leahey, Russell J. Funk
Chemical Physics
Optimization of random phase approximation calculations for improved energies of molecules, solids, and surfaces
Neung-Kyung Yu, Johannes Voss and Andrew J. Medford
We present an optimized random phase approximation method (optRPA26) that significantly improves upon conventional RPA. The method employs an empirically constructed hybrid functional to generate DFT orbitals to evaluate the RPA correlation energy, which is then scaled by a constant. Comprehensive
benchmarks across molecules, bulk solids, and surface systems demonstrate that optRPA26 consistently achieves high accuracy, with mean absolute errors of 0.05 eV for W4-11 reaction energies, 0.07 eV for cohesive energies, 0.09 eV for metal
oxide formation energies, 0.11-0.12 eV for adsorption of small molecules on metals, and 0.06 eV for adsorption on
oxides. In addition, optRPA26 correctly captures phase stability in metal
oxides and
magnetic metals. The optRPA26 approach can be run using standard RPA implementations, highlighting its potential as a general-purpose reference method that can accurately capture covalent, ionic, and metallic, and van der Waals bonding in molecules, solids, and interfaces.
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Data Analysis and Statistics · Cross-listed from astro-ph.CO
The Galaxy Bias Profile of Cosmic Voids:A Comparison of Void Finders
Ignacio G. Alfaro, Antonio D. Montero-Dorta, Jorge F. Bustillos et al. (+6 more)
Cosmic voids, the largest underdense regions in the Universe, provide unique laboratories for studying
galaxy formation and constitute powerful probes of cosmology. Recent work has shown that individual
galaxy bias (b_i), which quantifies how each
galaxy traces the underlying dark matter field, exhibits a characteristic radial dependence within spherical voids, defining a void bias profile in which
galaxies near void centers display systematically lower bias values. We investigate how the environmental modulation of individual
galaxy bias depends on the adopted void-finding
algorithm by comparing measurements across five distinct void definitions: spherical voids 'sparkling', watershed-based methods ('zobov' and 'revolver' in two modes), and free-form integrated-density voids ('popcorn'). We apply these complementary void-finding
algorithms to the same
galaxy sample drawn from the IllustrisTNG simulation (TNG300-1 at $z=0$) and compute individual
galaxy bias profiles as a function of distance from void centers. We quantify the correlation between b_i and the membership of the void catalogs and explore how this relationship varies with the integrated underdensity threshold for density-based methods. We find that the radial gradient of individual bias within voids, generally increasing from negative values at the void centers to higher values at the boundaries, is robust across most void definitions. However, density-threshold methods preferentially select
galaxies with b_i<0, while watershed methods without density constraints include substantial contamination from high-bias boundary
galaxies. The correlation between negative bias and void membership is systematically strengthened as the integrated underdensity threshold becomes less restrictive, with popcorn achieving the highest purity in isolating anti-biased populations.
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All authors Ignacio G. Alfaro, Antonio D. Montero-Dorta, Jorge F. Bustillos, Dante J. Paz, Andrés N. Ruiz, Andrés Balaguera-Antolínez, Ravi K. Sheth, Facundo Rodriguez, Constanza A. Soto-Suárez
Space Physics
Auroral signatures of ballooning instability and plasmoid formation processes in the near-Earth magnetotail
Ping Zhu, Jun Liang, Jiaxing Liu et al. (+2 more)
The nonlinear development of ballooning instability and the subsequently induced plasmoid formation in the near-Earth magnetotail demonstrated in MHD simulations has been proposed as a potential trigger mechanism for substorm onset over the past decade, and their connections to the in-situ satellite and ground all-sky auroral optical observations have been a subject of continued research. In this work, a set of THEMIS substorm onset events with good conjunction of auroral observations has been selected for comparative simulation study, whose pre-onset magnetotail configuration and conditions are inferred from in-situ data and compared with the onset conditions of ballooning instability obtained in our MHD simulations. The
evolution of the near-Earth magnetotail is followed, where the signatures of ballooning instability and the plasmoid formation are extracted from simulations and compared with the
magnetic fields and flow patterns within the magnetotail region from observation data. The field-aligned current (FAC) density is evaluated at the Earth side boundary of the magnetotail domain of simulation, which is further mapped along
magnetic field lines to the auroral ionosphere and compared with the auroral pattern and
evolution there in terms of growth rate, dominant wavenumber, and absolute auroral intensities. Such validation efforts are also the first step towards the development of a self-consistent coupling model that includes the magnetotail-ionosphere interaction in the substorm onset process.
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All authors Ping Zhu, Jun Liang, Jiaxing Liu, Sui Wan, Eric Donovan
Optics · Cross-listed from quant-ph
Quantum computational imaging and sensing
Mohan Sarovar
We present a new framework for imaging and sensing based on utilizing a
quantum computer to coherently process
quantum information in an electromagnetic field. We describe the framework, its potential to provide improvements in imaging and sensing performance and present an example application, the design of coherent receivers for optical communication. Finally, we go over the improvements in
quantum technologies required to fully realize
quantum computational imaging and sensing.
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Space Physics · Cross-listed from cs.AI
Evaluating Robustness and Adaptability in Learning-Based Mission Planning for Active Debris Removal
Agni Bandyopadhyay and Günther Waxenegger-Wilfing
Autonomous mission planning for Active Debris Removal (ADR) must balance efficiency, adaptability, and strict feasibility constraints on fuel and mission duration. This work compares three planners for the constrained multi-debris rendezvous problem in Low Earth Orbit: a nominal Masked Proximal Policy Optimization (PPO) policy trained under fixed mission parameters, a domain-randomized Masked PPO policy trained across varying mission constraints for improved robustness, and a plain Monte Carlo Tree Search (MCTS) baseline. Evaluations are conducted in a high-fidelity orbital simulation with refueling, realistic transfer dynamics, and randomized debris fields across 300 test cases in nominal, reduced fuel, and reduced mission time scenarios. Results show that nominal PPO achieves top performance when conditions match training but degrades sharply under distributional shift, while domain-randomized PPO exhibits improved adaptability with only moderate loss in nominal performance. MCTS consistently handles constraint changes best due to online replanning but incurs orders-of-magnitude higher computation time. The findings underline a trade-off between the speed of learned policies and the adaptability of search-based methods, and suggest that combining training-time diversity with online planning could be a promising path for future resilient ADR mission planners.
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Atmospheric and Oceanic Physics
Large-Ensemble Simulations Reveal Links Between Atmospheric Blocking Frequency and Sea Surface Temperature Variability
Zilu Meng, Gregory J. Hakim, Wenchang Yang et al. (+1 more)
Atmospheric blocking events drive persistent weather extremes in midlatitudes, but isolating the influence of sea surface temperature (SST) from chaotic internal atmospheric variability on these events remains a challenge. We address this challenge using century-long (1900-2010), large-ensemble simulations with two computationally efficient deep-learning general circulation models. We find these models skillfully reproduce the observed blocking climatology, matching or exceeding the performance of a traditional high-resolution model and representative CMIP6 models. Averaging the large ensembles filters internal atmospheric noise to isolate the SST-forced component of blocking variability, yielding substantially higher correlations with reanalysis than for individual ensemble members. We identify robust teleconnections linking Greenland blocking frequency to North Atlantic SST and El Niño-like patterns. Furthermore, SST-forced trends in blocking frequency show a consistent decline in winter over Greenland, and an increase over Europe. These results demonstrate that SST variability exerts a significant and physically interpretable influence on blocking frequency and establishes large ensembles from deep learning models as a powerful tool for separating forced SST signals from internal noise.
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All authors Zilu Meng, Gregory J. Hakim, Wenchang Yang, Gabriel A. Vecchi
Medical Physics
Laterally Oscillating Trajectory for Undersampling Slices: LOTUS
Mayuri Sothynathan, Paul I. Dubovan and Corey. A. Baron
Purpose: While spiral sampling offers SNR advantages for
diffusion MRI, its acceleration with simultaneous multislice remains relatively unexplored. This study introduces Laterally Oscillating Trajectory for Undersampling Slices (LOTUS), which is a 3D spiral-like k-space trajectory that aims to minimize g-factor via controlled incoherent aliasing. To aid in validation, we also introduce a robust method to estimate g-factor for iterative non-Cartesian reconstructions. Methods: Simulated data sampling of a numerical phantom was performed using LOTUS and several acquisition schemes proposed by others to quantitatively compare the resulting image quality when compared to a known ground truth.
Diffusion-weighted in vivo
brain data from two subjects was acquired with two in-plane acceleration factors (2x and 4x), and two slice acceleration factors (2x and 4x). Estimated g-factor maps and fractional anisotropy maps were calculated to quantitatively and qualitatively compare trajectory performance. For both simulation and in vivo, reconstructions both with and without compressed sensing were utilized. Results: Simulations generally showed decreased g-factor (20%-31%, depending on trajectory, at highest undersampling rate) and improved reconstruction accuracy (mean-square error, structural similarity index, and
entropy metrics) for LOTUS compared to the other trajectories. The in vivo acquisitions demonstrated g-factor benefits and qualitative image quality improvements that mirrored the simulation results. For both simulation and in vivo, improvements for LOTUS increased for higher numbers of simultaneous slices. Conclusion: By enabling higher rates of slice acceleration, LOTUS shows promise for decreasing scan time, which is especially beneficial for
diffusion MRI.
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Space Physics · Cross-listed from cs.AI
Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning with Refueling and Adaptive Collision Avoidance
Agni Bandyopadhyay and Gunther Waxenegger-Wilfing
As the orbital environment around Earth becomes increasingly crowded with debris, active debris removal (ADR) missions face significant challenges in ensuring safe operations while minimizing the risk of in-orbit collisions. This study presents a reinforcement learning (RL) based framework to enhance adaptive collision avoidance in ADR missions, specifically for multi-debris removal using small satellites. Small satellites are increasingly adopted due to their flexibility, cost effectiveness, and maneuverability, making them well suited for dynamic missions such as ADR. Building on existing work in multi-debris rendezvous, the framework integrates refueling strategies, efficient mission planning, and adaptive collision avoidance to optimize spacecraft rendezvous operations. The proposed approach employs a masked Proximal Policy Optimization (PPO)
algorithm, enabling the RL
agent to dynamically adjust maneuvers in response to real-time orbital conditions. Key considerations include fuel efficiency, avoidance of active collision zones, and optimization of dynamic orbital parameters. The RL
agent learns to determine efficient sequences for rendezvousing with multiple debris targets, optimizing fuel usage and mission time while incorporating necessary refueling stops. Simulated ADR scenarios derived from the Iridium 33 debris
dataset are used for evaluation, covering diverse orbital configurations and debris distributions to demonstrate robustness and adaptability. Results show that the proposed RL framework reduces collision risk while improving mission efficiency compared to traditional heuristic approaches. This work provides a scalable solution for planning complex multi-debris ADR missions and is applicable to other multi-target rendezvous problems in autonomous space mission planning.
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Chemical Physics · Cross-listed from quant-ph
Near-frustration-free electronic structure Hamiltonian representations and lower bound certificates
Nicholas C. Rubin, Guang Hao Low and A. Eugene DePrince III
Hamiltonian representations based on the sum-of-squares (SOS) hierarchy provide rigorous lower bounds on ground-state energies and facilitate the design of efficient classical and
quantum simulation
algorithms. This work presents a unified framework connecting SOS decompositions with variational two-particle reduced density
matrix (v2RDM) theory. We demonstrate that the “weighted” SOS ansatz naturally recovers the dual of the v2RDM program, enabling the strict enforcement of
symmetry constraints such as particle number and
spin. We provide explicit SOS constructions for the Hubbard model and electronic structure
Hamiltonians, ranging from
spin-free approximations to full rank-2 expansions. We also highlight theoretical connections to block-invariant
symmetry shifts. Numerical
benchmarks on molecular systems and Iron-Sulfur clusters validate these near frustration-free representations, demonstrating their utility in improving spectral gap amplification and reducing block encoding costs in
quantum algorithms.
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History and Philosophy of Physics
Intereses convergentes: los años previos a la visita de Einstein a la Argentina en 1925
Alejandro Gangui
We analyze in some detail the years leading up to Albert Einstein's visit to Argentina, from the moment he rose to fame after the publication of the results of the 1919
solar eclipse, until his arrival at the port of Buenos Aires in March 1925. We will see that this major event in the country's cultural life did not arise from a
vacuum but was the product of diverse interests and unique international and local circumstances. The social and political situation surrounding the eminent scientist and the impetus provided by individuals and institutions in Buenos Aires played a decisive role, ultimately converging in the "successful" reception of this extraordinary figure in a country on the scientific periphery of the first quarter of the 20th century. We also review the written references and knowledge circulating in the local milieu on topics related to the theory of relativity in the years prior to his arrival in the country.
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Biological Physics · Cross-listed from cond-mat.soft
Metastability and ripening of multi-component liquid mixtures
Giacomo Bartolucci and Fabrizio Olmeda
Understanding how multi-component
liquid mixtures undergo phase separation is central to elucidating biophysical organization in the
cell. Here, combining analytical and numerical results, we characterise the dynamics of mixtures with disordered interactions among the components. We first study how two coexisting phases become unstable, leading to multiphase coexistence. We then show that the scaling of droplet radius as $t^{1/3}$ and droplet number as $n^{-2/3}$, characteristic of Ostwald ripening in two dimensions, can be severely delayed. This delay arises from glass-like relaxation and the emergence of long-lived metastable states characterized by different wetting angles.
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Instrumentation and Detectors
The Remote Analog to Digital Conversion DAQ System for the TRISTAN Detector Upgrade
Andrew S. Gavin, Matthias Balzer, Suren Chilingaryan et al. (+10 more)
The TRISTAN detector is an upgrade to the KATRIN experiment to enable a differential measurement of the tritium $\beta$-decay spectrum to search for sterile
neutrinos with keV masses. This entails performing precision electron spectroscopy with over one thousand silicon drift detector pixels, each responsible for recording incident electron rates of $10^5$ counts per second. A project specific data acquisition (DAQ) system is developed to meet the experimental challenges through a remote analog to digital conversion (RADC) design. In this work, the conceptual design of the RADC DAQ is presented along with the built system for operating the TRISTAN detector upgrade. The system includes flexible signal processing logic and data management that is optimized for the high-rate precision measurement.
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All authors Andrew S. Gavin, Matthias Balzer, Suren Chilingaryan, Reyco Henning, Andreas Kopmann, Susanne Mertens, Jalal Mostafa, Frank Simon, Nicholas Tan Jerome, Denis Tcherniakhovski, Korbinian Urban, John F. Wilkerson, Sascha Wüstling
Chemical Physics · Cross-listed from cs.LG
Stochastic hierarchical data-driven optimization: application to plasma-surface kinetics
José Afonso, Vasco Guerra and Pedro Viegas
This work introduces a
stochastic hierarchical optimization framework inspired by Sloppy Model theory for the efficient calibration of physical models. Central to this method is the use of a reduced Hessian approximation, which identifies and targets the stiff parameter subspace using minimal simulation queries. This strategy enables efficient
navigation of highly anisotropic landscapes, avoiding the computational burden of exhaustive sampling. To ensure rigorous inference, we integrate this approach with a probabilistic formulation that derives a principled objective loss function directly from observed data. We validate the framework by applying it to the problem of
plasma-surface interactions, where accurate modelling is strictly limited by uncertainties in surface reactivity parameters and the computational cost of kinetic simulations. Comparative analysis demonstrates that our method consistently outperforms baseline optimization techniques in sample efficiency. This approach offers a general and scalable tool for optimizing models of complex reaction systems, ranging from
plasma chemistry to biochemical networks.
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Applied Physics
Epitaxial growth optimization, measurement and theoretical analysis of strain-compensated QCL grown on (511)A InP
Alessio Cargioli, Mattias Beck and Jerome Faist
Interface roughness scattering is an important limiting factor for achieving high performance
Quantum Cascade
Lasers. Following recent results, we study the growth conditions for a strain-compensated QCL emitting around 4.6 {\mu}m grown on a (511)A InP substrate using AFM and XRD measurements. We find that modulating the arsenic flux and correctly tailoring the III/V ratio is fundamental to achieve a good quality material. We report the first lasing device on such a platform with a current density threshold of 1.34 kA/cm2 and a slope efficiency of 1.1 W/A, which result suboptimal compared to the (100) reference. Finally, we find a 7%
redshift of the (511)A spectrum which we attribute to an
impurity scattering due to the increased incorporation along the exposed (111) direction. We validate this statement by verifying that the change in CBO and effective electron mass due to strain along a non-trivial direction cannot cause such a shift by using the k-p method generalized to arbitrary growth directions
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Chemical Physics · Cross-listed from cs.LG
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
Ryan Liu, Eric Qu, Tobias Kreiman et al. (+2 more)
Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the
quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss. Existing evaluations, such as microcanonical molecular dynamics (MD), are computationally expensive and primarily probe near-
equilibrium states. To improve evaluation metrics for MLIPs, we introduce the Bond Smoothness Characterization Test (BSCT). This efficient
benchmark probes the PES via controlled bond deformations and detects non-smoothness, including discontinuities, artificial minima, and spurious forces, both near and far from
equilibrium. We show that BSCT correlates strongly with MD stability while requiring a fraction of the cost of MD. To demonstrate how BSCT can guide iterative model design, we utilize an unconstrained
Transformer backbone as a testbed, illustrating how refinements such as a new differentiable $k$-nearest neighbors
algorithm and temperature-controlled
attention reduce artifacts identified by our metric. By optimizing model design systematically based on BSCT, the resulting MLIP simultaneously achieves a low conventional E/F regression error, stable MD simulations, and robust atomistic property predictions. Our results establish BSCT as both a validation metric for practitioners to assess MLIP utility and as an "in-the-loop" model design proxy that alerts MLIP developers to physical challenges that cannot be efficiently evaluated by current MLIP
benchmarks. The BSCT
dataset and evaluation are available on https://github.com/ryanliu30/bsct.git
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All authors Ryan Liu, Eric Qu, Tobias Kreiman, Samuel M. Blau, Aditi S. Krishnapriyan
Chemical Physics
El Agente Quntur: A research collaborator agent for quantum chemistry
Juan B. Pérez-Sánchez, Yunheng Zou, Jorge A. Campos-Gonzalez-Angulo et al. (+12 more)
Quantum chemistry is a foundational enabling tool for the fields of chemistry, materials science, computational biology and others. Despite of its power, the practical application of
quantum chemistry simulations remains in the hands of qualified experts due to methodological complexity, software heterogeneity, and the need for informed interpretation of results. To bridge the accessibility gap for these tools and expand their reach to chemists with broader backgrounds, we introduce El Agente Quntur, a hierarchical, multi-
agent AI system designed to operate not merely as an automation tool but as a research collaborator for computational
quantum chemistry. Quntur was designed following three main strategies: i) elimination of hard-coded procedural policies in favour of
reasoning-driven decisions, ii) construction of general and composable actions that facilitate generalization and efficiency, and iii) implementation of guided deep research to integrate abstract
quantum-chemical
reasoning across subdisciplines and a detailed understanding of the software's internal logic and syntax. Although instantiated in ORCA, these design principles are applicable to research
agents more generally and easily expandable to additional
quantum chemistry packages and beyond. Quntur supports the full range of calculations available in ORCA 6.0 and reasons over software documentation and scientific literature to plan, execute, adapt, and analyze in silico chemistry experiments following best practices. We discuss the advances and current bottlenecks in agentic systems operating at the research level in computational chemistry, and outline a roadmap toward a fully autonomous end-to-end computational chemistry research
agent.
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All authors Juan B. Pérez-Sánchez, Yunheng Zou, Jorge A. Campos-Gonzalez-Angulo, Marcel Müller, Ignacio Gustin, Andrew Wang, Han Hao, Tsz Wai Ko, Changhyeok Choi, Eric S. Isbrandt, Mohammad Ghazi Vakili, Hanyong Xu, Chris Crebolder, Varinia Bernales, Alán Aspuru-Guzik
Chemical Physics
El Agente Estructural: An Artificially Intelligent Molecular Editor
Changhyeok Choi, Yunheng Zou, Marcel Müller et al. (+10 more)
We present El Agente Estructural, a
multimodal, natural-
language-driven geometry-generation and manipulation
agent for autonomous chemistry and molecular modelling. Unlike molecular generation or editing via generative models, Estructural mimics how human experts directly manipulate molecular systems in three dimensions by integrating a comprehensive set of domain-informed tools and
vision-
language models. This design enables precise control over atomic or functional group replacements, atomic connectivity, and stereochemistry without the need to rebuild extensive core molecular frameworks. Through a series of representative case studies, we demonstrate that Estructural enables chemically meaningful geometry manipulation across a wide range of real-world scenarios. These include site-selective functionalization, ligand binding, ligand exchange, stereochemically controlled structure construction, isomer interconversion, fragment-level structural analysis, image-guided generation of structures from schematic reaction mechanisms, and mechanism-driven geometry generation and modification. These examples illustrate how
multimodal reasoning, when combined with specialized geometry-aware tools, supports interactive and context-aware molecular modelling beyond structure generation. Looking forward, the integration of Estructural into El Agente Quntur, an autonomous multi-
agent quantum chemistry platform, enhances its capabilities by adding sophisticated tools for the generation and editing of three-dimensional structures.
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All authors Changhyeok Choi, Yunheng Zou, Marcel Müller, Han Hao, Yeonghun Kang, Juan B. Pérez-Sánchez, Ignacio Gustin, Hanyong Xu, Andrew Wang, Mohammad Ghazi Vakili, Chris Crebolder, Alán Aspuru-Guzik, Varinia Bernales
Atomic Physics
Radiation of relativistic electrons created in tunnel ionization of atomic gases by laser beams of extreme intensity
N.V. Makarenko and S.V. Popruzhenko
We consider tunnel ionization of atomic argon in a femtosecond
laser pulse of intensity $10^{21}-10^{22}{\rm W/cm}^2$ aiming to investigate the relativistic dynamics and radiation of photoelectrons released from their parent ions inside the
laser focus. Radiation of such electrons accelerated along the
laser field propagation direction appears to have moderate power but can be considerably enhanced by a collision with a relatively weak counter-propagating
laser pulse. Using the theory of
laser-induced tunneling in atoms and ions and that of nonlinear Thomson scattering, we demonstrate that angular distributions and spectra of emitted
photons can serve as a probe of the peak intensity in the focus. The angular distribution of emitted radiation in the plane of polarization and propagation of the ionizing
laser beam appears narrow and peaked around an intensity-dependent angle, making this ionization setup a source of collimated XUV radiation.
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Optics
ConvRML: high-quality lensless imaging with random multi-focal lenslets
Leyla A. Kabuli, Clara S. Hung, Vasilisa Ponomarenko et al. (+2 more)
Mask-based lensless imagers use simple optics and computational reconstruction to design compact form factor cameras with compressive imaging ability. However, these imagers generally suffer from poor reconstruction quality. Here, we describe several advances in both hardware and software that result in improved lensless imaging quality. First, we use a precision-manufactured random multi-focal lenslet (RML) phase mask to produce improved measurements with reduced multiplexing. Next, we implement a ConvNeXt-based reconstruction architecture, which provides up to 4.6 dB improvement in peak signal-to-noise ratio over state-of-the-art
attention-based architectures. Finally, we establish a parallel imaging setup that simultaneously images a scene with RML, diffuser, and lens systems, with which we collect
datasets with 100,000 measurements for each system, to be used for reconstruction model training and evaluation. Using this standardized system, we quantify the improved measurement quality of the RML compared to a diffuser using the modulation transfer function and mutual information. Our ConvRML system benefits from both the optical and the computational developments presented in this work, and our contributions establish resources to support the continued development of high-quality, compact, and compressive lensless imagers.
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All authors Leyla A. Kabuli, Clara S. Hung, Vasilisa Ponomarenko, Eric Markley, Laura Waller
Physics and Society
Opinion dynamics under electoral shocks in competitive campaigns
Jaime L. C. da C. Filho and Nuno Crokidakis
We propose a computational framework for modeling opinion dynamics in electoral competitions that combines two realistic features: voter
memory and exogenous shocks. The population is represented by a fully-connected network of
agents, each holding a binary opinion that reflects support for one of two candidates. First, inspired by the classical voter model, we introduce a
memory-dependent opinion update: each
agent's probability of adopting a neighbor's stance depends on how many times they agreed with that neighbor in the
agent's past $m$ states, promoting inertia and resistance to change. Second, we define an electoral shock as an abrupt external influence acting uniformly over all
agents during a finite interval $[t_0, t_0+\Delta t]$, favoring one candidate by switching opinions with probability $p_s$, representing the impact of extraordinary events such as political scandals, impactful
speeches, or sudden news. We explore how the strength and duration of the shock, in conjunction with
memory length, influence the transient and stationary properties of the model, as well as the candidates' advantage. Our findings reveal a rich dynamical behavior:
memory slows down convergence and enhances system resilience, whereas shocks of sufficient intensity and duration can abruptly realign collective preferences, particularly when occurring close to the election date. Conversely, for long
memory lengths or large election horizons, shock effects are dampened or delayed, depending on their timing. These results offer insights into why some sudden political events reshape electoral outcomes while others fade under strong individual inertia. Finally, a qualitative comparison with real electoral shocks reported in opinion polls illustrates how the model captures the competition between voter inertia and abrupt external events observed in actual elections.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
Atomistic and data-driven insights into the local slip resistances in random refractory multi-principal element alloys
Wu-Rong Jian, Arjun S. Kulathuvayal, Hanfeng Zhai et al. (+5 more)
Refractory multi-principal element
alloys (RMPEAs) have attracted growing interest for their exceptional high-temperature strength, yet their complex compositions hinder a mechanistic understanding of plastic deformation. Here, we perform atomistic simulations to determine local slip resistances (LSRs) of edge and screw dislocations on primary BCC slip planes in 12 equal-molar RMPEAs. Machine learning is employed to uncover relationships between LSR and underlying material properties, enabling systematic assessment of compositional effects on dislocation behavior. Based on these insights, we develop a thermally activated, dislocation-based model to predict macroscopic yield stress. We find that increasing the fraction of hexagonal close-packed elements above 50% significantly reduces unstable stacking fault energy, ideal shear strength, and screw LSR across all slip planes. Higher elastic anisotropy further lowers these quantities, while
lattice distortion modifies relative slip resistances between dislocation characters and slip systems. By combining an autoencoder with a random forest model, we identify elastic constants and
lattice distortion as the dominant factors controlling LSR. The resulting framework accurately predicts tensile yield stress in BCC RMPEAs and provides guidance for
alloy design.
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All authors Wu-Rong Jian, Arjun S. Kulathuvayal, Hanfeng Zhai, Anshu Raj, Xiaohu Yao, Yanqing Su, Shuozhi Xu, Irene J. Beyerlein
Chemical Physics
Reducing the Cost of Unitary Coupled Cluster via Active Space Partitioning
Prateek Vaish and Brenda Rubenstein
Unitary Coupled Cluster (UCC) theory is a promising variational method for electronic structure calculations, especially for strongly correlated systems and
quantum computers. However, its practical application is limited by the steep scaling of its non-terminating Baker-Campbell-Hausdorff expansion. We introduce an active space UCCSD(4)/MP2 approach that leverages a fourth-order many-body perturbation theory truncation of UCCSD within a selected active space, while treating external excitations at the MP2 level. We explore two variants: a composite method that sums separate internal and external contributions and an interacting method that couples the amplitudes for greater accuracy. We test our approach on the GW100
dataset, a metaphosphate hydrolysis reaction, and the strongly correlated torsion of ethylene. Our results suggest that the interacting method with canonical orbitals is robust for weakly and moderately correlated systems and accurately reproduces the full UCCSD(4) potential energy curves using only 15-25% of the virtual orbitals in its active space. In comparison, the composite formulation exhibits greater sensitivity to the orbital basis and active space size, leading to less systematic behavior across the
benchmark set. For ethylene torsion, a system dominated by strong static correlation, both composite and interacting formulations employing canonical orbitals closely track the full UCCSD(4) reference but do not alleviate the unphysical features inherited from the underlying single-reference UCCSD(4) description. This active space framework offers a tractable approach for modeling correlated molecules and reactions on classical computers and provides a viable path for scaling UCC calculations for resource-constrained
quantum hardware.
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Plasma Physics
Physics-Informed Neural Compression of High-Dimensional Plasma Data
Gianluca Galletti, Gerald Gutenbrunner, Sandeep S. Cranganore et al. (+6 more)
High-fidelity scientific simulations are now producing unprecedented amounts of data, creating a storage and analysis bottleneck. A single simulation can generate tremendous data volumes, often forcing researchers to discard valuable information. A
prime example of this is
plasma turbulence described by the gyrokinetic equations: nonlinear, multiscale, and 5D in phase space. It constitutes one of the most computationally demanding frontiers of modern science, with runs taking weeks and yielding tens of terabytes of data dumps. The increasing storage demands underscore the importance of compression. However, reconstructed snapshots do not necessarily preserve essential physical quantities. We present a spatiotemporal evaluation pipeline, accounting for structural phenomena and multi-scale transient fluctuations to assess the degree of physical fidelity. Indeed, we find that various compression techniques lack preservation of both spatial mode structure and temporal
turbulence characteristics. Therefore, we explore Physics-Informed Neural Compression (PINC), which incorporates physics-informed losses tailored to gyrokinetics and enables extreme compressions ratios of over 70,000x.
Entropy coding on top of PINC further pushes it to 120,000x. This direction provides a viable and scalable solution to the prohibitive storage demands of gyrokinetics, enabling post-hoc analyses that were previously infeasible.
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All authors Gianluca Galletti, Gerald Gutenbrunner, Sandeep S. Cranganore, William Hornsby, Lorenzo Zanisi, Naomi Carey, Stanislas Pamela, Johannes Brandstetter, Fabian Paischer
Fluid Dynamics · Cross-listed from nlin.CD
Semiclassical Structure of the Advection–Diffusion Spectrum in Mixed Phase Spaces
Christopher Amey, Bala Sundaram and Andrew C. Poje
We examine the spectral structure of the two-dimensional advection-
diffusion operator in flows with mixed phase space at very large Peclet number. Using
Fourier discretization combined with
symmetry reduction and Krylov-Arnoldi methods, we compute on the order of one hundred leading eigenpairs reliably in the asymptotic, weak-
diffusion regime. While the principal
eigenvalue is asymptotically diffusive and localized on the largest regular region, the broader spectrum exhibits a rich organization controlled by local Lagrangian phase-space geometry. In particular, exponential mixing in chaotic regions rapidly suppresses correlations, whereas algebraic mixing in integrable regions generates long-lived coherent structures that dominate the slow and intermediate parts of the spectrum. We identify three distinct classes of eigenmodes: advective modes associated with transport on invariant
tori, diffusive modes and, within the duffusive branch, tunneling modes arising from weak coupling between dynamically separated regular regions. Drawing on a semiclassical analogy, we assign
quantum-number-like labels to these families and predict the appearance, scaling, and ordering of their sub-spectra directly from the
Hamiltonian phase-space structure. The coexistence of these families implies that no uniform control of the spectral gap exists across the full spectrum: although the slowest mode is diffusive, arbitrarily small gaps arise between competing families at higher mode numbers. As a result, finite-time advection-
diffusion dynamics is generically governed by persistent modal competition rather than single-mode dominance, even at asymptotically large Peclet number.
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