Chemical Physics
LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms
Masaya Hagai, Yuta Suzuki, Tomoya Murata et al. (+2 more)
Quantum chemistry simulations underpin modern materials discovery, yet their impact is limited by steep computational cost and dependence on fixed approximation schemes. Foundation models, such as machine-learned interatomic potentials, have accelerated parts of this workflow, but their reliance on large-scale pretraining restricts adaptability at the frontier of chemical space, where methodological innovation and sparse data are the norm. Agentic AI systems can automate existing simulation pipelines, yet they remain constrained by the predefined tools and
algorithms they orchestrate. In response, we introduce LADeQ, an LLM-guided workflow that discovers, implements, and
benchmarks candidate approximation
algorithms at test-time within existing
quantum chemistry codes. Rather than selecting from a predefined repertoire, LADeQ constructs candidate approximation schemes on demand, drawing on techniques from disciplines such as spatial statistics,
circuit simulation, and kernel methods that have had little prior presence in electronic-structure theory. Because it builds on an out-of-the-box
language model, LADeQ requires no task-specific pretraining or curated data, and the resulting implementations are transparent and inspectable, with explicitly traceable approximation errors that enable principled control of accuracy–efficiency trade-offs. We show that LADeQ accelerates coupled cluster singles and doubles (CCSD) and configuration interaction singles and doubles (CISD) calculations while keeping correlation-energy errors within user-specified tolerances, demonstrating autonomous, objective-driven discovery of approximation
algorithms inside existing electronic-structure solvers.
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All authors Masaya Hagai, Yuta Suzuki, Tomoya Murata, Shuhei Kurita, Masaki Adachi
Instrumentation and Detectors · Cross-listed from quant-ph
Optimized Quantum States for Sensing in the Presence of Loss and Phase Noise
Shruti Maliakal, Zachary Mann, Christopher Wipf et al. (+3 more)
Squeezed
vacuum lets gravitational-wave detectors and other
quantum sensors surpass the standard
quantum limit, and is optimal in the loss-limited regime; phase noise breaks this optimality. Numerically optimizing the
quantum Fisher information across the loss and phase-noise landscape, we identify non-Gaussian states that outperform any Gaussian state. These fall into three classes: Fock-like, cubic-phase-like, and states with discrete rotational
symmetry. Limiting the average number of
photons in the input state to $\bar{n}=5$, with $1-\eta = 5\%$
photon loss and 200 mrad phase noise, the non-Gaussian advantage reaches up to 2.2 dB. Furthermore, we observe that the non-Gaussian advantage can persist even when the measurement strategy is homodyne detection.
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All authors Shruti Maliakal, Zachary Mann, Christopher Wipf, Rana X Adhikari, Su Direkci, Yanbei Chen
Atmospheric and Oceanic Physics
Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
Anna Asch, Raphael Rossellini, Pedram Hassanzadeh et al. (+1 more)
Probabilistic weather
forecasting is undergoing rapid transformation with artificial intelligence (AI). In traditional numerical weather prediction, computing power can limit how well ensemble forecasts approximate the unknown statistical distribution of future states. AI models facilitate larger ensembles and are trained with probabilistic considerations, ideally leading to better uncertainty quantification. Forecasts from these state-of-the-art models are often considered well-calibrated. However, here we show that the statistical coverage of such models, the ultimate measure of calibration, can struggle, especially on extreme events. To address this shortcoming, we employ conformal prediction, a class of statistical methods that mathematically guarantees coverage under no distributional assumptions, unlike previous post-processing techniques. We apply online conformal prediction to temperature and precipitation forecasts (including extremes) of three leading global weather models, GenCast, NeuralGCM, and AIFS-ENS, ensuring calibrated uncertainty at no expense to other probabilistic metrics. This post-processing method can be applied to any
forecasting model.
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All authors Anna Asch, Raphael Rossellini, Pedram Hassanzadeh, Rebecca Willett
Computational Physics
Machine-learning-driven kinetic discovery of carbon interstitial color centers in diamond
Xiaoya Chang, Nima Ghafari Cherati, Arsalan Hashemi et al. (+3 more)
Diamond hosts optically active point defects central to
quantum technologies, yet the carbon self-interstitials introduced during growth and irradiation compete with them and form new defects whose configurational landscape is poorly charted, as subtle energy differences govern the competing minima and pathways. Here we build an interstitial-focused
dataset by active learning and
benchmark three machine-learning interatomic potentials – GAP, NEP and the equivariant MACE – against density functional theory for energies, forces and migration barriers. MACE reproduces the reference energetics and relative stabilities, whereas the others can misorder the ground states. Annealing molecular dynamics with the validated potentials uncovers a series of previously unreported carbon interstitial clusters, from di- to octa-interstitials – several introducing in-gap states of interest as colour centres – and shows that their metastability is governed by kinetically accessible pathways rather than energetic ordering. These results chart the interstitial defect landscape and accelerate defect discovery for
quantum technologies.
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All authors Xiaoya Chang, Nima Ghafari Cherati, Arsalan Hashemi, Mikko Karttunen, Ádám Gali, Tapio Ala-Nissila
Fluid Dynamics
Hypersonic Shock-Wave/Boundary-Layer Interaction on a Three-Dimensional Expansion-Compression Geometry
Anshuman Pandey, Katya Casper, Steven Beresh et al. (+3 more)
This experimental work explores the flow field around a three-dimensional expansion-compression geometry on a slender cone at Mach 5 and 8 using high-frequency pressure sensors, high-framerate schlieren, temperature-sensitive paint, shear-stress measurements and oil-flow visualizations. The $7^\circ$ cone geometry has a
hyperbolic slice acting as an expansion corner which is then followed by a $30^\circ$ finite-span compression ramp. The freestream Reynolds number was varied so that the boundary layer approaching the expansion corner was either laminar, transitional or turbulent. At laminar or early transitional conditions, the separation shock locks onto the expansion corner and the separation region encompasses most of the slice, with the separation shear layer flapping at a preferred frequency. As Reynolds number is increased, the separation shock moves downstream onto the slice, the separation bubble shrinks, and the shear layer flapping frequency increases while its amplitude drops. In all cases, large-scale low-frequency breathing motions are observed. The strong relaminarization across the expansion corner at Mach 8 prevents the shock/boundary-layer interaction from reaching truly turbulent conditions and fundamentally changes its behavior on this non-canonical geometry.
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All authors Anshuman Pandey, Katya Casper, Steven Beresh, Rajkumar Bhakta, Marie De Zetter, Russell Spillers
Atomic Physics · Cross-listed from quant-ph
Electrical Noise Produced by Micron-Sized Particles above a Surface Paul Trap
Ben Saarel, Ozgur Sahin, Hartmut Häffner et al. (+1 more)
Electric field noise produced by the surface of ion trap electrodes reduces the fidelity of
quantum computing operations. Despite decades of investigation its microscopic origins remain unclear. Here, we measure electric field noise at trapping locations along the
symmetry axis of a linear surface Paul trap. We find that noise levels vary by three orders-of-magnitude in one 600$\,\mu$m section of the trap. Optical and scanning electron microscope images show micron-sized particles close to the trapping locations with the highest noise levels. We find that modeling the particles as a lossy dielectric with a effective loss tangent $\tan\theta=0.33(0.06)$ describes the magnitude of the noise, as well as its spatial and frequency dependence. Our observations may explain the large variation of reported noise levels in literature.
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All authors Ben Saarel, Ozgur Sahin, Hartmut Häffner, Alpha T. N'Diaye
Atmospheric and Oceanic Physics
A Land-Sea Contrast Pattern in Surface Temperature and Atmospheric Circulation Trends in Recent Decades
Benjamin O. Johnson and Maria Rugenstein
Spatial patterns in observed
climate trends remain poorly understood. Here we argue that a warming of land relative to ocean has shaped observed surface temperature and atmospheric circulation trends, including the negative Inter-Decadal Pacific Oscillation (IPO)-like tendency across the Pacific basin. Observed and modeled trends display an overall decline in sea level pressure over the faster-warming land relative to ocean, with a spatial pattern that resembles the seasonal cycle and the response to land heating in idealized
climate model experiments. Coupled
climate model simulations with historical forcing underestimate the land-sea warming ratio. It is only in the early response of abrupt CO2 quadrupling
climate model simulations that
climate models are able to recreate the observed land-sea warming ratio, in which case a strengthening of oceanic surface highs and a negative IPO-like surface warming pattern over the Pacific comparable to observed trends are seen. We propose that discrepancies between modeled and observed trends in many
climate variables may be explained by the underestimation of the land-sea warming ratio by
climate models. Determining the cause of this discrepancy has the potential to constrain projections of future
climate change as the underlying mechanism causing
climate models to underestimate the land-sea warming ratio discrepancy will set the persistence of this problem.
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Fluid Dynamics · Cross-listed from cs.LG
Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport
Gabriel F. Barros, Rômulo M. Silva and Alvaro L. G. A. Coutinho
This chapter reviews recent advances in Scientific Machine Learning (SciML) for modeling coupled fluid flow and transport phenomena governed by the incompressible Navier-Stokes and scalar transport equations. Such systems, found in applications like turbidity currents and thermal convection, feature strong nonlinear coupling and multiscale behavior that make high-fidelity simulations computationally expensive. To address this, the chapter surveys state-of-the-art SciML methods for building efficient surrogate models, including linear reduced-order techniques based on Singular Value Decomposition (such as Dynamic Mode Decomposition) and nonlinear neural network approaches like Physics-Informed Neural Networks (PINNs) and $\beta$-Variational Autoencoders ($\beta$-VAEs). It first covers the authors' work combining these models with High Performance Computing strategies, including Adaptive Mesh Refinement/Coarsening (AMR/C) and scientific floating-point data compression. It then presents two new contributions: surrogate modeling of turbidity currents via PINNs, and the extraction of disentangled nonlinear modes from thermal flows using $\beta$-VAEs. Governing equations and representative
benchmarks, including lock-exchange flows and Rayleigh-Bénard convection, illustrate these methodologies. The chapter is intentionally long, covering both the mathematical and physical foundations of coupled fluid flow and the computational aspects of state-of-the-art modeling. Overall, it demonstrates how SciML enables fast, accurate approximations of complex coupled systems within the specific data regimes and modeling assumptions considered, while substantially reducing computational cost relative to full-order simulations. Broader capabilities such as real-time prediction and uncertainty quantification remain active research directions whose feasibility depends strongly on the problem at hand.
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Computational Physics
TorchNEP: Ultra-Efficient and Accurate Training of Neuroevolution Potentials
Yong-Chao Wu, Xiaoya Chang, Tero Mäkinen et al. (+5 more)
Neuroevolution Potential (NEP) is one of the most efficient machine-learned interatomic potential frameworks for large-scale atomistic simulations. However, its original training strategy remains computationally demanding, limiting systematic exploration of model architectures and training protocols. Here, we present TorchNEP, a PyTorch-based implementation of NEP that combines analytically derived gradients, adaptive optimization, and a two-stage training strategy. TorchNEP accelerates training by more than two orders of magnitude while maintaining full compatibility with existing NEP models. We further show that the improvement in predictive accuracy primarily originates from the two-stage training protocol rather than the optimization
algorithm itself. Across diverse
benchmark datasets, TorchNEP consistently improves force and stress predictions while maintaining comparable or improved energy accuracy.
Benchmark evaluations on elemental and
alloy systems demonstrate enhanced predictive performance for both atomic configurations and key materials properties. Furthermore, we show that increasing model complexity does not necessarily improve predictive performance despite reducing training errors. Overall, TorchNEP provides an efficient and flexible training framework for developing more accurate and robust machine-learned interatomic potentials.
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All authors Yong-Chao Wu, Xiaoya Chang, Tero Mäkinen, Amin Esfandiarpour, Jian-Li Shao, Tapio Ala-Nissila, Zheyong Fan, Mikko Alava
Physics and Society
A Four-Section Bracket for the 48-team World Cup
Chong Qi
The expansion of the FIFA World Cup to 48 teams in 2026 introduces structural challenges in tournament design. To populate a 32-team knockout bracket from 12 groups of four, the current FIFA rules select the eight best third-placed teams using a global ranking across all groups. This global coupling creates several major problems: a combinatorial explosion of 495 possible bracket configurations; a fundamentally biased and unequal selection of third-placed qualifiers; lack of a clear path for group winners; vulnerability to collusion and ranking manipulation; and no guarantee of same-group separation beyond the first knockout round. We propose a simple unified solution called the four-section bracket (FSB) rule: split the 12 groups into four sections of three groups. All group winners, runners-up, and the two best third-placed teams in each section advance. Group winners remain in their home sections as local anchors, while lower-ranked qualifiers are transferred to other sections according to a fixed, symmetric rule. This structure guarantees same-group separation until the semifinal, protects the top eight group winners with a predictable knockout path, and reduces bracket complexity from 495 configurations to just one invariant
topology per section, recovering the
symmetry of the traditional 32-team format. We show substantial improvements in competitive
fairness and scheduling predictability.
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Chemical Physics
Multi-Equalization in Conceptual Density Functional Theory: Beyond Electronegativity Equalization
Jesús Sánchez-Márquez
The electronegativity equalization principle provides a simple framework to describe charge redistribution, yet its conventional formulation is limited to a first-order description based on chemical potential equalization. In this work, we introduce 'multi-equalization,' a generalized framework that extends this concept by incorporating higher-order responses within Conceptual Density Functional Theory. This approach represents molecules as sets of flexible electron density partitions, allowing different electronic descriptions (e.g., atomic densities or localized orbitals) to be treated within a unified formalism. We demonstrate that correlations between energy and density derivatives with respect to the number of electrons lead to the simultaneous equalization of multiple descriptors, including chemical hardness and Fukui indices. A constructive
algorithm is introduced to determine the optimal density partitions satisfying these multi-equalization conditions. This scheme provides a consistent description of both global charge transfer and local reactivity, overcoming the intrinsic limitations of traditional electronegativity equalization models. Notably, the inclusion of density response functions enables local hardness equalization, introducing spatial resolution into reactivity descriptions. Under multi-equalization, local reactivity descriptors become constrained functionals of the global electron density. This framework establishes a deeper connection between charge equalization models and formal density functional theory, offering a theoretically grounded route toward improved predictions of molecular reactivity.
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Physics and Society
Methodological guidelines for circadian modeling of Daylight Saving Time: application to the United States
Jose Maria Martin-Olalla and Jorge Mira
Modeling the circadian impact of seasonal clock changing requires precise synchronization between
solar and social time. This report critiques a recent study that associated disease prevalence in the United States with seasonal clock exposure. We identify a fundamental computational error in which a sign reversal of the longitudinal offset effectively inverted the US East-West axis, cross-correlating local health data with the circadian burden of hypothetical locations on the opposite side of a time zone. We outline the methodology for a correct modelization of the circadian process in the context of US geography.
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Physics Education
Vistas: A Visualization Interface for Particle Collision Simulations
Benoit Assi, Christan Bierlich, Rikab Gambhir et al. (+7 more)
We introduce Vistas, a tool for visualizing high-energy particle physics collisions simulated by the Pythia Monte-Carlo event generator. Vistas utilizes the browser-based event display framework Phoenix to show distinct computational stages of a high-energy collision event simulation: the hard process, parton shower, hadronization, and particle decays. Particles produced from each of these stages are represented as lines in an interactive three-dimensional graph structure, where each line is along the direction of its particle's three-momentum vector. The event can be rotated, translated and zoomed, and details for each particle can be accessed by selecting the relevant particle line. Additionally, particle lines from all stages of the simulation can be toggled on and off and can be filtered by particle-level kinematic selection requirements. This interactive environment provides an intuitive interpretation of Pythia simulation output, including detailed features such as color flow, beam remnants, and multiple parton interactions, making it a useful tool in physics education settings, from outreach activities to graduate particle-physics courses.
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All authors Benoit Assi, Christan Bierlich, Rikab Gambhir, Philip Ilten, Tony Menzo, Stephen Mrenna, Manuel Szewc, Michael K. Wilkinson, Ahmed Youssef, Jure Zupan
Plasma Physics
Bayesian optimization of stellarator alpha-particle confinement using data-informed parameter spaces and dimensionality reduction
Matt Landreman, Michael Czekanski, Andrew Giuliani et al. (+2 more)
Modern stellarators are typically designed by optimizing the shape of the
plasma boundary surface, with the parameters taken to be
Fourier amplitudes. Many promising optimization
algorithms such as
Bayesian methods require bound constraints on the parameters and are most efficient when each parameter is scaled similarly to the others. With the typical
Fourier parameterization, it is unclear how to set these bounds: wide constraints lead to self-intersecting boundaries and frequent failures of the MHD
equilibrium calculation, while tight bound constraints limit expressiveness. To address these issues, here we propose two new parameter spaces for stellarator optimization. Both begin with a
dataset of existing stellarator boundaries. In the first approach, a
quantile transformation is applied to each
Fourier degree of freedom, mapping the data distribution to a uniform distribution on the unit interval. In the second approach, principal component analysis (PCA) is applied to points on the boundaries, followed by a
quantile transformation. For both approaches, the transformed variables become the degrees of freedom, naturally bounded to [0, 1]. The PCA method has the additional benefit of dimensionality reduction, with high expressiveness for a small number of parameters. The methods are demonstrated via
Bayesian optimization for good alpha-particle confinement with guiding-center tracing inside the optimization loop, using asynchronous parallelization. These optimizations yield stellarator configurations with excellent fast-particle confinement in fields that can be far from quasisymmetric or quasi-isodynamic.
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All authors Matt Landreman, Michael Czekanski, Andrew Giuliani, Byoungchan Jang, Rory Conlin
Classical Physics · Cross-listed from astro-ph.CO
Reheating as a variational probe of cosmological observables
Jinn-Ouk Gong
We formulate reheating as a constrained variational problem in the space of equation-of-state histories, rather than attempting to describe it through microscopic models. We introduce a regularized functional framework that identifies reheating histories which extremize a given cosmological observable under minimal physical assumptions. As illustrative applications, we consider
prompt gravitational waves, induced gravitational waves, and
primordial black holes. We find that different observables select qualitatively different regions of reheating-history space. These examples demonstrate that cosmological observables define distinct extremal directions in reheating-history space and can therefore be used to systematically explore the space of post-inflationary expansion histories.
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Physics and Society
Narrative Structure in Tropes: A Computational Analysis of `Friends'
Shun Zhang, Tabia Tanzin Prama, Christopher M. Danforth et al. (+1 more)
Tropes are recurring narrative devices in television and film. We carry out a computational analysis of tropes in the sitcom Friends, using human-curated trope annotations from TVTropes, episode transcripts, and IMDb ratings. Because automatic trope detection remains challenging, we treat existing trope annotations as a curated analytical layer and focus on their downstream narrative and semantic functions. We first examine the relationship between episode-level trope frequency and audience reception. We find a statistically significant positive association between trope count and weighted IMDb ratings, although the modest explanatory power suggests that more than trope density alone explains audience evaluation. We then connect trope annotations to dialogue transcripts and represent trope-related dialogue using TF-IDF-based semantic features. Using PCA and k-means clustering, we group 1,954 distinct tropes into 15 semantically interpretable clusters. Chi-square analyses show that the six main characters are unevenly distributed across these clusters, with character-specific trope profiles that are broadly consistent with their established narrative identities. Finally, we project trope clusters into the ousiometric power-danger space to examine their semantic organization. The results show that "Physical and Sexual Comedy" occupies a region associated with relatively high danger, while "Revelation, Surprise, and Reaction" occupies a region associated with relatively high power. Overall, our work demonstrates a way to operationalize trope measurement and shows that identifiable trope clusters can provide holistic "distant reading" descriptions of characters and stories.
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All authors Shun Zhang, Tabia Tanzin Prama, Christopher M. Danforth, Peter Sheridan Dodds
Physics and Society
Networks of agglomeration: how population density rewires social networks and reshapes contagion dynamics
Christopher K. Tokita
From ancient Mesopotamia to modern cities, dense human settlements coincide with bursts of economic productivity, cultural innovation, and social change. But how does packing people more tightly together alter social organization in ways that reshape collective outcomes? Here, I use a minimal
agent-based model to isolate the effect of population density, holding population size and individual behavior fixed while varying only how closely individuals are placed in space. In the model, individuals form social ties gradually, favoring those nearby and those already well-connected. Under these simple rules, varying population density alone is sufficient to reorganize social network structure: sparse populations develop locally clustered communities, while denser ones form globally integrated networks with shorter social distances and a tightly interconnected core of popular individuals. This structural transition occurs sharply over a narrow range of densities and is governed by whether physical proximity or social popularity dominates tie formation. Simulating contagions on these networks reveals that the consequences of this shift depend on what is spreading. Simple contagions (e.g., information or disease) reach a majority of individuals more quickly in denser populations. Complex contagions (e.g., social norms or collective behaviors) do not spread faster, but instead achieve broader and more reliable adoption as density increases. Together, these results show that population density can act as a structural force independent of the economic and behavioral mechanisms typically invoked to explain why cities are engines of change.
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Optics
Record nonlinear conversion efficiency in the production of high spectral purity vacuum ultraviolet laser at 148 nm
Sergey Vasilyev, Tian Ooi, Igor Moskalev et al. (+14 more)
Coherent
vacuum-ultraviolet (VUV)
lasers are indispensable for precision measurement,
quantum optics, and materials science. Recent high-resolution spectroscopy of the Th-229
nuclear clock transition near 148 nm highlights the urgent demand for intense, narrow-linewidth VUV
lasers for advancing metrology and testing fundamental physics. However, existing VUV generation schemes typically require enhancement cavities [C. Zhang et al., Opt. Lett. 47, 5591-5594 (2022)], atomic resonances [Q. Xiao et al., Nature 650, 852-856 (2026)], or random quasi-phase-matched nonlinear
crystals [V. Lal et al., Optica 12, 1971-1974 (2025)]. Here, we demonstrate a VUV frequency comb via cascaded frequency doubling of a 2400 nm Cr:ZnS comb to its 16th harmonic in nonlinear
crystals. The final stage employs a bulk-grown, spatially uniform quasi-phase matched (QPM)
crystal developed by IPG, combining VUV transparency, high $\chi^2$ nonlinearity, and power scalability. Using this QPM
crystal we generate a VUV frequency comb with 40 $\mu$W average power (1 nW per mode at 80 MHz mode spacing) with a conversion efficiency order of magnitude higher than other known methods. These results establish a scalable route to compact VUV sources via direct frequency doubling, opening a path toward a robust continuous-wave
nuclear clock
laser.
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All authors Sergey Vasilyev, Tian Ooi, Igor Moskalev, Mike Mirov, Andrey Muraviev, Dmitrii Konnov, Victor Churikov, Viktor Sukharev, Evgeny Galenin1, Jack F. Doyle, Chuankun Zhang, Kai Li, Georgiy Seryogin, Dan Perlov, Igor Samartsev, Konstantin Vodopyanov, Jun Ye
Computational Physics
sft-wick: A formalism and package for Feynman-diagram expansion and evaluation in stochastic field theories
Zheng Zhang
When
stochastic field dynamics are cast into a path-integral formulation, perturbation theory becomes systematic but the resulting expansion quickly grows combinatorially large. The setting targeted here includes fields with several components and several dimensions,
matrix propagators, tensor-valued couplings, and non-Gaussian driving noise specified by arbitrary n-point cumulants. Wick pairings grow factorially, and component indices must be routed through the tensor-valued vertices. The useful output is not a raw contraction list, but a diagram table: one entry per
topology, with multiplicities, coupling sums, signs, and causal constraints resolved. We present sft-wick, an open-source Python package that constructs these diagram tables and computes their integrals numerically. Given an action and an observable, it enumerates topologically distinct Feynman diagrams, derives their algebraic coefficients, and evaluates the resulting diagram integrals from user-supplied response and cumulant functions. The core
algorithm enumerates spatial
topologies before routing component indices, avoiding contraction-by-contraction Wick expansion. Response-field constraints, including vanishing response-response contractions, the Ito prescription, and the absence of causal response loops, are enforced during enumeration. Predictions are validated against direct Langevin simulations in two worked examples, with Gaussian and with non-Gaussian driving noise, and agree with the simulations within stated error budgets.
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Chemical Physics · Cross-listed from math.NA
Moreau-Yosida-based Kohn-Sham Inversion for Periodic Systems
Vebjørn H. Bakkestuen, Michael F. Herbst, Vegard Falmår et al. (+2 more)
Density-potential inversion for periodic systems within Moreau-Yosida-regularised density-functional theory is investigated, both theoretically and numerically. We develop the framework in a periodic homogeneous Sobolev space and use it to recover the exchange-correlation potential of Kohn-Sham theory through a limiting procedure. A key analytical ingredient is the proof of lower semicontinuity of the non-interacting kinetic-energy functional in the chosen
topology. The proximal mapping, together with its algorithmic evaluation, plays a central role in the resulting inversion scheme. Numerical experiments illustrate the performance and properties of the method for both the Kohn-Sham and Gross-Pitaevskii equations.
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All authors Vebjørn H. Bakkestuen, Michael F. Herbst, Vegard Falmår, Markus Penz, Andre Laestadius
General Physics
Non-degenerate and degenerate wormholes: a unified approach
Juri Dimaschko
A generalized notion of degenerate wormholes is introduced, defined by the vanishing of the metric determinant g at the throat. It is described by the
polynomial, g^2 modified Einstein field equations. Building on this framework, we show that both the Einstein Rosen bridge and the Klinkhamer defect wormhole are exact
vacuum solutions of the g^2 modified equations, valid globally including at the degenerate throat, while the Klinkhamer configuration additionally admits traversable geometries with b>2M, where b sets the length scale of the wormhole throat and M is a mass parameter. In contrast, standard Morris Thorne and thin shell wormholes, governed by the conventional (non regularized) Einstein equations, are intrinsically non degenerate and necessarily supported by exotic stress energy. Within a unified regularized system with matter, both thin shell and Klinkhamer wormholes appear as two qualitatively distinct classes of states: non degenerate with exotic matter versus degenerate with
vacuum sharing the Einstein Rosen bridge as a common limiting configuration. This unified viewpoint clarifies why classical null energy condition no go
theorems apply only to the non degenerate sector and suggests the possibility of stationary degenerate traversable wormholes that do not require NEC violation.
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Chemical Physics · Cross-listed from quant-ph
Efficient classical representation and quantum state preparation of complete active space wavefunctions
Hamza Jnane
Quantum computers promise to solve the electronic structure problem for a large class of molecules. However, the performance of relevant
quantum algorithms hinges on preparing initial states with substantial overlap with the target eigenvector. For classically challenging molecules with strong electron correlation, starting from multi-reference states, such as complete active space (CAS) wavefunctions is necessary. Unfortunately, the most advanced state preparation protocols applied to such states result in a gate complexity that scales exponentially with the active space size $d$. In fact, even encoding a CAS state classically is traditionally believed to be intractable for chemically relevant systems. Here, we draw insights from the recently introduced
Quantum Paldus Transform (QPT) to show that there exists an efficient classical representation of CAS states and to design a new state preparation routine outperforming previous ones. The QPT represents a transformation from the Fock basis to a friendlier
symmetry-adapted basis. Our main contribution consists in showing that CAS states expanded in this basis can efficiently be represented as a
matrix product state (MPS) with a bond dimension scaling as $O(d^2)$. One can then efficiently load the MPS on a
quantum computer and use the inverse QPT to transform the state to the Fock basis. Moreover, our method can easily be extended to the efficient preparation of CAS states in first quantisation with similar complexity. Crucially, we demonstrate that the complexity of both state preparation protocols only grows polynomially as $O(d^3)$ , which constitutes to the best of our knowledge an exponential improvement over the state of the art.
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Fluid Dynamics · Cross-listed from cond-mat.stat-mech
Odd fluids from chiral cellular automata
Andrew A. Allocca, Shiva Heidari, Thomas Iadecola et al. (+3 more)
Cellular automata are discrete dynamical systems defined on a
lattice, in which each site carries a finite set of states that evolve in time according to local deterministic rules. An important application of cellular automata is in
lattice gas models of fluids, where the cellular automaton framework provides a particle-based microscopic description of hydrodynamic behavior. The macroscopic fluid equations emerge after coarse-graining over many
lattice sites and time steps, offering a bottom-up route to hydrodynamics. A celebrated example is the Frisch-Hasslacher-Pomeau (FHP) model, an automaton defined on a two-dimensional triangular
lattice that yields the two-dimensional Navier-Stokes equations upon coarse-graining. In this work, we construct a parity-breaking generalization of the FHP model through two modifications: introducing
chiral two-body collision rules and systematically rotating particle velocities to mimic the effect of a background
magnetic field. We show that this automaton yields a hydrodynamic model with odd viscosity, a transverse transport coefficient that is a hallmark of odd fluids. We verify the analytical transport coefficients using Poiseuille-flow simulations of the
chiral FHP automaton. Our results demonstrate that the
chiral automaton introduced here provides a bridge between microscopic parity-breaking scattering processes and macroscopic odd-fluid hydrodynamics.
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All authors Andrew A. Allocca, Shiva Heidari, Thomas Iadecola, Armin Rahmani, Pouyan Ghaemi, Sriram Ganeshan
Computational Physics · Cross-listed from astro-ph.CO
Physics-guided discovery of dynamical dark-energy equations of state through iterative AI reasoning
Clecio R. Bom, Bernardo M. Fraga, Miguel A. Sabogal et al. (+3 more)
Phenomenological model building has traditionally relied on human
reasoning: equations are proposed from theoretical intuition, analogy, or empirical convenience, and only then tested against data. Here we show that this cycle can be recast as an iterative AI
reasoning process for dynamical dark energy. Our framework uses a large
language model to propose equations of state together with cosmological rationales, grounded by
retrieval from the dark-energy literature and refined through autonomous evaluation. Each candidate is embedded in a cosmological model, optimized against observations, and assessed using likelihood performance and theoretical consistency. An independent
language-model critic scores the physical motivation, novelty, clarity, stability and implementation validity of both the equation and its rationale, allowing subsequent proposals to evolve jointly in mathematical structure and physical
reasoning. Applied to cosmological data combinations including
supernovae,
baryon acoustic oscillations and Planck likelihoods, the framework identifies two parameterizations that, to the best of our knowledge, have not previously been explored and that are competitive with established forms. For Pantheon+
supernovae, DESI DR2
baryon acoustic oscillations and the full Planck 2018 temperature, polarization, and
lensing likelihoods, the best AI-selected model attains larger
Bayesian evidence than the traditional parameterizations considered here by more than one unit. These results show that AI-guided
reasoning can complement physical model building by proposing and evaluating interpretable phenomenological parameterizations for dynamical dark energy.
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All authors Clecio R. Bom, Bernardo M. Fraga, Miguel A. Sabogal, Armando Bernui, Phelipe Darc, Gustavo Schwarz
Optics · Cross-listed from astro-ph.IM
Characterization of a symmetric-facet dual-ruled grating for spatial heterodyne spectroscopy
Cole Meyer, Joanne Flores, Jason Corliss et al. (+1 more)
Dual-bandpass spatial heterodyne spectrometers (DB-SHS) enable simultaneous high-resolution measurements of widely separated passbands, providing powerful diagnostics of astrophysical and planetary environments. However, DB-SHS instruments require a single incident beam to span two adjacent diffraction gratings with distinct ruling densities and blaze angles, resulting in a large gap between ruled sections that reduces throughput. Dual-ruled gratings solve this problem by integrating multiple ruled panels onto a single substrate, minimizing the dead space between ruled sections. We present experimental validation of a first-generation symmetric-facet dual-ruled grating manufactured by Bach Research, mechanically ruled at $800$ and $\mathrm{2000\;gr\;mm^{-1}}$ with a $13.8^\circ$ blaze angle. Using a stabilized deuterium source alongside a Czerny-Turner monochromator, we measured diffraction efficiencies into the $m = 0, \pm1, \pm2$ orders from $200$ to $\mathrm{700\;nm}$. We compare these results with theoretical predictions from rigorous coupled-wave analysis (RCWA), inferring a facet asymmetry of $\lesssim1^\circ$ and $\sim70\%$ facet duty cycle indicative of minor manufacturing defects. This work demonstrates the viability of mechanically ruled, symmetric-facet, dual-ruled gratings and lays the foundation for laboratory validation of the first DB-SHS, ultimately enabling high-resolution spectroscopy of distinct spectral regions relevant to astrophysical and planetary remote sensing.
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All authors Cole Meyer, Joanne Flores, Jason Corliss, Walter Harris
Chemical Physics · Cross-listed from cond-mat.soft
Sequential replica exchange with solute tempering for atomistic modeling of supramolecular polymer structures
Hadi H. Arefi and Takeshi Yamamoto
Predicting detailed atomistic structures of self-assembling systems remains a challenge for all-atom molecular dynamics simulations. Replica exchange with solute tempering (REST) has been used to study those systems by accelerating all monomers in a global and uniform manner. While such a global approach can in principle predict any morphology of the system, it has computational drawbacks such as inefficient replica traversal due to order-disorder transitions and the growing number of replicas with system size. To address these issues, here we propose an alternative, stepwise construction approach to modeling supramolecular
polymers under the assumption of one-dimensional polymerization. Specifically, we generate
polymer structures by adding new monomers one by one to the system and applying REST to the new monomers to find their optimal binding positions based on an energy-based scoring function. The monomer addition and enhanced sampling are repeated sequentially until a
polymer of desired length is obtained. We test the above procedure using a model supramolecular
polymer in explicit solvent, and show that it can generate a
polymer structure with characteristic H-bonding patterns at reduced computational costs, while also improving the efficiency of replica traversal significantly. We thus expect that the sequential REST will be useful for modeling supramolecular
polymers, particularly for cases where global REST simulations are too demanding computationally.
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General Physics
Cosmic Structure Formation in a Viable Power-Law f(R) Gravity Model: Growth Dynamics, Stability, and Observational Signatures
Murli Manohar Verma
We investigate the
evolution of
cosmic structures in the power-law modified
gravity model $f(R)=R+R^{1+\delta}/R_c^\delta$, where the dimensionless parameter $\delta$ characterizes deviations from General Relativity. The background cosmological dynamics and the
evolution of linear matter density perturbations are studied within the framework of metric $f(R)$
gravity. The modified perturbation equation is derived by introducing an effective gravitational coupling associated with the additional scalar degree of freedom, and the
evolution of the growth factor, logarithmic growth rate, growth index, and the observable quantity $f\sigma_8(z)$ are investigated. The results show that the
curvature correction enhances the growth of matter perturbations while remaining compatible with the observed late-time accelerated expansion for suitable values of the model parameter. The theoretical viability of the model is established through the ghost-free condition, Dolgov–Kawasaki stability criterion, positive scalaron mass, stable de Sitter solution, and chameleon screening mechanism. Comparison with representative viable $f(R)$
gravity models shows that the present theory achieves a consistent cosmological
evolution with a single deviation parameter. The predicted modifications in the growth of structures and the effective gravitational coupling provide observable signatures that can be tested by forthcoming large-scale structure and weak-
lensing surveys, providing a means to test
curvature-induced modifications of
gravity.
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Instrumentation and Detectors
The Silicon Tracking System of the E16 experiment at J-PARC: construction, installation and commissioning in beam test experiments
Dairon Rodríguez Garcés, Rento Yamada, Kazuya Aoki et al. (+37 more)
The J-PARC E16 experiment aims to search for signatures of
chiral symmetry restoration. It studies in-medium modifications of vector
mesons that decay via the dielectron channel. The measurements use a high-intensity 30 GeV
proton beam with C and Cu targets at rates up to 10 MHz. To achieve this, the experiment upgrades its tracking, by introducing innermost detector modules constructed with the same technology and procedures as the modules of the Silicon Tracking System (STS) of the Compressed Baryonic Matter (CBM) experiment at Facility for Antiproton and Ion Research (FAIR). A total of 15 modules were assembled, tested, characterized and then installed in the E16 detector setup. The detector was commissioned in a beam test experiment at Tsukuba, where the detector modules could be exposed to a 3 GeV electron beam. In preparation for the beam test the modules were characterized and calibrated, and performance studies were accomplished to assess the quality of the setup. During beamtime, three modules were operated and illuminated in two planes by the electron beam. This paper presents the results of the construction, characterization, commissioning, and operation of the E16-STS modules in beam test experiments.
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All authors Dairon Rodríguez Garcés, Rento Yamada, Kazuya Aoki, Lady Maryann Collazo Sánchez, David Emschermann, Hideto En'yo, Jürgen Eschke, Ulrich Frankenfeld, David Gutiérrez Menéndez, Johann M. Heuser, Masaya Ichikawa, Ralf Kapell, Irakli Keshelashvili, Jörg Lehnert, Tomoki Murakami, Shunnosuke Nagafusa, Wataru Nakai, Satomi Nakasuga, Megumi Naruki, Frederike Nickels, Shuta Ochiai, Kyoichiro Ozawa, Darío Alberto Ramírez Zaldívar, Adrian Rodríguez Rodríguez, Katia Santos Marrero, Christian Joachim Schmidt, Hans Rudolf Schmidt, Kerstin Schuenemann, Patryk Semeniuk, Mehulkumar Shiroya, Carmen Simons, Sindhu Subramanya, Oleksandr Suddia, Tomonori Takahashi, Maksym Teklishyn, Alberica Toia, Oleg Vasylyev, Robert Visinka, Yorito Yamaguchi, Wojciech Zabolotny
Instrumentation and Detectors
Design and Commissioning of a Deuterium-Tritium Gas Delivery System for Muon Catalyzed Fusion in a Diamond Anvil Cell
Elena Koukina, Cody Fagan, Christopher Robert Shmayda et al. (+46 more)
We report the design, commissioning, and operation of deuterium-deuterium (DD) and deuterium-tritium (DT) gas delivery systems developed to load a diamond anvil
cell (DAC) beam target for
muon-catalyzed fusion (muCF). The DAC approach enables DT fuel to be compressed to GPa pressures at more than twice the
liquid density and heated from cryogenic temperatures through 500 K, opening access to a substantially expanded parameter range for muCF kinetics and yield measurements. In this approach, DT is cryo-condensed to a
liquid in a minichamber and then compressed in the DAC using a helium-driven pneumatic membrane, achieving high pressures in a millimeter-scale DT sample volume. A DD gas delivery system was designed and used to validate the experimental apparatus, measure the gas quantities needed for filling, develop operational experience, and collect kinetics and yield data with DD targets. The DT gas delivery system adds tritium-specific capabilities for inventory minimization, secondary containment, and activity monitoring. The DT system integrates depleted uranium storage beds and a
liquid helium cryogenic condenser used for pressure building and cryopumping. High-purity delivery is provided by a rapid-response palladium permeator. The system is housed in a helium-atmosphere glovebox held at negative pressure with continuous cleanup. We present the process and instrumentation design, a failure modes and effects analysis (FMEA), and data from the experiment's in situ Raman spectrometer, which provides direct confirmation of target loading and composition through the optically clear diamond anvils. The 2024 and 2025 DT campaigns achieved repeatable target fills and operation with no measurable tritium releases to the stack, demonstrating safe, high-purity DT loading at novel density-temperature conditions for muCF studies.
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All authors Elena Koukina, Cody Fagan, Christopher Robert Shmayda, Jonathan D Kalow, Demetrious M Harrington, George Harris, Kaylee McCormack, Munin Mundt, Kevin Lau, Dominik Zajac, Michael W. Koch, Sofia Varner, Alexander Golossanov, Stephen Bull, Robert Buxbaum, Walter Stadolnik, Joseph A Allen, Jose Betances, Nicholas Brennan, Rachel M Chaney, William Reuel Cutler, Jonathan Davies, Chad Forrest, Parth Gandhi, John Thomas Hinchen, Carol Johnstone, Katy Kem, Musheera Khandaker, Mandy Kiburg, Isaac Kiniti, Aaron D Knaian, Linda Knaian, Nate James Lewkowicz MacFadden, Daniel Mayer, Patrick C McDaniel, Evan D Niner, Karl Payne, Claude Petitjean, Robert Ridgway, Matt Russell, Anuj Sampat, Jeffrey Simon, Ira Spool, Ana Tejeda, Aldo Antognini, Kevin Lynch, Seth Newburg, Walter T Shmayda, Ara N Knaian
Atmospheric and Oceanic Physics
Optimal scenario design for climate emulation
Christopher B. Womack, Shahine Bouabid, Andrei Sokolov et al. (+4 more)
As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints. However, for machine-learning surrogate
climate models (emulators), we show that the low structural diversity in existing scenarios commonly used to generate training data places a ceiling on predictive skill. Here, we examine whether training
datasets themselves can be optimized to improve generalization. We introduce a method to create
datasets that produce emulators capable of generalizing to new, structurally different scenarios absent from the training data. We use a differentiable Simple
Climate Model (SCM) to calculate the sensitivity of emulator loss to perturbations in the training data, iteratively updating the training data to maximize emulator skill. For an SCM, training on one scenario optimized in this fashion outperforms an emulator trained on six standard ScenarioMIP pathways. We achieve this higher predictive skill despite training on a smaller
dataset, finding that our emulator successfully isolates distinct physical behaviors of different
climate forcing
agents (e.g., greenhouse gases vs. aerosols) without single-forcing runs. We then demonstrate that scenarios optimized using an SCM, when used to drive an intermediate-complexity
climate model, produce a training
dataset that yields a more skillful emulator than training on ScenarioMIP outputs. Our results suggest that, in the compute-constrained environment of running full-scale
climate models, generating a small number of dynamically rich scenarios provides greater marginal value for emulation and characterizing system responses than expanding the suite of traditional emissions pathways.
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All authors Christopher B. Womack, Shahine Bouabid, Andrei Sokolov, Popat Salunke, Glenn Flierl, Sebastian D. Eastham, Noelle E. Selin