Optics
Computational analysis and performance optimization of SrScCu3Se4-based solar cells using COMSOL Multiphysics
Khalid Mahmud, Md. Nahid Hasan, Md. Islahur Rahman Ebon et al. (+2 more)
Transition metal-based quaternary
semiconductors show strong magneto-optical and thermoelectric properties, yet their photovoltaic performance in realistic three-dimensional device architectures remains underexplored. In this work, we investigate a n-ZnSe/p-SrScCu3Se4/p+-WSe2 quaternary chalcogenide
heterostructure using three-dimensional finite-element simulations in COMSOL Multiphysics. The model self-consistently couples wavelength-dependent optical generation, drift-
diffusion carrier transport, and thermal loss analysis under AM1.5G, 1-sun illumination within a fully coupled opto-electro-thermal framework. The effect of absorber width, acceptor level, and bulk defects on photovoltaic performance, carrier generation, and recombination profile is investigated in this study. Quaternary chalcogenide device produces an open
circuit voltage (VOC) of 1.02 V, short-
circuit current density (JSC) of 32.472 mA/cm2, fill factor (FF) of 88.212%, and power conversion efficiency (PCE) of 29.217% under optimized conditions. The
quantum efficiency (QE) results indicate that the device effectively transforms incident light into charge carriers within the visible spectrum, however absorption and carrier collecting performance diminish in the near-infrared range. The electrothermal simulations indicate modest, spatially non-uniform temperature increases relevant to Joule heating and nonradiative recombination heating within the active layers. Overall, these findings ensure that SrScCu3Se4, a quaternary chalcogenide, is a promising absorber material and offers a strong experimental design consideration for obtaining high-performance, thermally stable three-dimensional photovoltaic device
topologies.
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All authors Khalid Mahmud, Md. Nahid Hasan, Md. Islahur Rahman Ebon, Bipanko Kumar Mondal, Jaker Hossain
Data Analysis and Statistics · Cross-listed from cond-mat.stat-mech
Nonequilibrium stochastic thermodynamics of boundary functionals: From Zubarev's ensemble to stochastic particle separation
V. V. Ryazanov
This paper develops a generalization of Zubarev's nonequilibrium statistical operator method for the case of simultaneous inclusion of additive and nonlocal boundary functionals of trajectories. Using a unified thermodynamic approach, a three-parameter model of nonequilibrium systems is constructed, including the first-passage time, the dwell time above a given level, and the absolute extremum of the process. A correspondence is demonstrated between the maximum information
entropy method for trajectories and the Donsker-Varadan large deviation formalism. Using the Doob`s h-transform, it is demonstrated that fixing extremal functionals of history induces efficient non-Markovian transport in the system with dynamic adaptation to historical records. Criteria for the applicability of the developed apparatus in the "large time" domain and the possibilities of its use for optimizing
stochastic particle separation in periodic potentials are discussed.
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Computational Physics · Cross-listed from hep-ph
Nonperturbative functional renormalization group for Higgs-singlet models with physics-informed neural networks
Norimi Yokozaki
We develop a nonperturbative functional renormalization group framework within the LPA' to solve the Wetterich flow equation for the $Z_2$-symmetric real singlet extension of the Standard Model at finite temperature, without a low-order
polynomial truncation of the loop corrections to the effective potential, using a physics-informed neural network (PINN) representation. In contrast to conventional truncations based on low-order field expansions, our hybrid tree-level-plus-neural-network ansatz yields a continuous, mesh-free description of the effective potential over the full field and scale range. As a proof of concept, we apply the framework to the finite-temperature effective potential relevant to the electroweak phase transition: one-dimensional field-space slices at two
benchmark temperatures, and a two-dimensional reconstruction at T=100 GeV yielding a two-step bounce action. The flow is implemented in a multi-domain setup in scale and field space with derivative matching conditions ensuring smoothness and numerical stability. Gauge and Yukawa sectors are incorporated via independently computed perturbative two-loop running couplings, and anomalous dimensions are included in the flow at the LPA' level. We
benchmark the network against resummed perturbation theory and against a grid-based relaxation solver of the same equation. Finally, we introduce a soft consistency constraint that keeps the solution close, in sign and order of magnitude, to perturbation theory across the field-space domain. We find this constraint necessary, rather than merely helpful, for selecting a physically sensible solution of the flow equation. The converged result nevertheless retains a residual dependence on this guidance – through hand-tuned weights and an analytic thermal target – which we identify as the central open problem for mesh-free FRG treatments of this kind.
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Data Analysis and Statistics · Cross-listed from astro-ph.CO
Finding the distribution of matter using lenses - II: deconvolution-based reconstruction with 3x2pt measurements
Jun-Qian Jiang, Ajoy Dawn, Dhiraj Kumar Hazra et al. (+2 more)
We present a deconvolution-based framework for testing scale-dependent departures of the late-time matter power spectrum from a fiducial cosmological model using $3\times2$pt measurements. We introduce a free-form scale-dependent modulation $A(k)$ of the fiducial nonlinear matter power spectrum and construct the linear response of binned
galaxy-clustering,
galaxy-
galaxy-
lensing, and
cosmic-shear spectra to the discretized modulation $A(k)$. The response is evaluated with full-sky, beyond-Limber kernels including density,
redshift-space-distortion, gravitational-shear, and intrinsic-
alignment contributions. We reconstruct $A(k)$ using a regularized modified Richardson-Lucy
algorithm, with weak
diffusion in $\ln k$ and selection of the minimum-$\chi^2$ solution along the iteration history. Using Rubin/LSST Year 10-like synthetic data, we find that oscillatory modulations with amplitudes $\gtrsim1\%$ can be recovered over $0.1\lesssim k\lesssim0.5\,{\rm Mpc}^{-1}$, provided the oscillation frequency $f\lesssim10$ on $\log_{10}[k/(0.2\,{\rm Mpc}^{-1})]$. We further introduce a posterior-weighted consistency statistic calibrated with posterior-predictive null mocks, thereby accounting for cosmological and nuisance-parameter uncertainties without relying on Wilks'
theorem. The null case is consistent with $A(k)=1$, while a $1\%$ oscillatory modulation is detected at $\sim 2.6\sigma$. These results demonstrate the potential of regularized deconvolution as a model-independent consistency test of the matter power spectrum in future $3\times2$pt surveys.
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All authors Jun-Qian Jiang, Ajoy Dawn, Dhiraj Kumar Hazra, Benjamin L'Huillier, Arman Shafieloo
Data Analysis and Statistics · Cross-listed from astro-ph.CO
Finding the distribution of matter using lenses - I: deconvolution-based reconstruction with CMB lensing
Ajoy Dawn, Jun-Qian Jiang, Dhiraj Kumar Hazra et al. (+2 more)
The matter power spectrum is one of the primary statistical descriptors of the large-scale distribution of matter in the Universe and provides a powerful probe of
cosmic structure formation. Measurements of
cosmic microwave background (CMB)
lensing offer an integrated view of the matter distribution over a wide range of
redshifts, enabling the reconstruction of the underlying matter power spectrum. In this work, we reconstruct the reference linear matter power spectrum $ P_\text{lin}(k,0)$ from the baseline joint CMB
lensing measurements of Planck PR4, ACT DR6, and SPT-3G using a covariance-weighted modified Richardson-Lucy(MRL) deconvolution
algorithm. The reconstructed spectrum is found to be consistent with the fiducial linear prediction on large scales, while exhibiting a systematic enhancement for $k \gtrsim 0.1\,{\rm Mpc}^{-1}$, where nonlinear gravitational
evolution becomes important. To investigate this behavior, we introduce a scale-dependent correction factor, $A(k)$, defined through $P(k)=A(k)\,P_{\rm nl}(k),$ where $P_{\rm nl}(k)$ is the fiducial nonlinear matter power spectrum obtained from 2LPT simulations. The reconstructed correction factor remains consistent with unity within $2\sigma$ confidence over the reconstructed range, indicating that the observed enhancement is well explained by the standard nonlinear
evolution of the matter power spectrum. In addition, the reconstruction shows agreement with the fiducial BAO template around the BAO feature at $k\sim(0.04-0.06)\ {\rm Mpc}^{-1}$, indicating that some BAO-scale information survives the
lensing projection.
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All authors Ajoy Dawn, Jun-Qian Jiang, Dhiraj Kumar Hazra, Benjamin L'Huillier, Arman Shafieloo
Optics
Magic-free coexisting photonic and phononic moiré flat bands
Ziming Chen, Kaiyu Cui, Ning Wu et al. (+8 more)
Moiré flat bands enhance localization and interactions through suppressed group velocity, but existing approaches largely target a single physical field because distinct excitations generally require different, finely tuned magic configurations. Here we introduce a flat-band mechanism based on strong diffractive hybridization among moiré-folded bands. Period-mismatched modulations open distinct coupling channels whose hybridization renormalizes the band dispersion. An effective
Hamiltonian shows that increasing the diffractive coupling progressively suppresses the group velocity, driving the system toward a flat-band regime without field-specific magic configurations. This coupling-induced mechanism enables band flattening across distinct physical excitations. We demonstrate this mechanism in a single-layer moiré optomechanical
crystal, where photonic and phononic flat bands are simultaneously realized, and their localized modes and optomechanical interaction are experimentally observed. Beyond photonic and phononic systems, this mechanism may extend to other wave and quasiparticle platforms, providing a general route to co-localizing and coupling distinct physical fields in moiré systems.
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All authors Ziming Chen, Kaiyu Cui, Ning Wu, Shuyuan Li, Chenxuan Wang, Xue Feng, Fang Liu, Wei Zhang, Hao Sun, Yongzhuo Li, Yidong Huang
Computational Physics
A finite-strain logarithmic viscoelastic model for Antarctic ice shelves based on an additive split
Maxime Nutte, Sebastian Skatulla and Carlo Sansour
Ice shelves lose mass primarily by calving, a process controlled by the near-front stress field on timescales that span elastic flexure and viscous creep. We formulate a finite-strain Maxwell model for glacier ice in logarithmic strain space. The Hencky strain of a fixed reference configuration is split additively at the level of rates into elastic and viscous parts; the spring is isotropic Hencky elasticity and the dashpot is a Glen-type power law written on the logarithmic strain rate and its work-conjugate stress. At infinitesimal strain the dashpot coincides with Glen's flow law; the elastic strains in the ice-shelf configurations of this paper remain in that regime. The model is integrated with a midpoint evaluation and a backward-Euler correction of the trial dual, and implemented in a finite-element setting. After a viscoelastic column
benchmark, the formulation is applied to an idealised ice tongue, including depth-dependent density and
moduli, temperature-dependent fluidity, and cliff geometries with a frontal foot or basal undercutting. The resulting stress fields show how viscoelasticity and front morphology control tension near the terminus.
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Chemical Physics · Cross-listed from quant-ph
Explicit block encodings of rate matrices for simulating polymerization kinetics on quantum computers
Yuhei Ikeda, Hokuto Iwakiri, Soichiro Nishio et al. (+1 more)
Predicting how molecular weight distribution and monomer sequence evolve during polymerization is central to
polymer science, yet classical approaches face a trade-off between molecular resolution and computational cost: for copolymers, the number of distinguishable species grows exponentially with chain length.
Quantum computing offers a potential alternative, provided the non-
unitary rate
matrices governing the kinetics can be embedded into
unitary quantum circuits, a task known as block encoding. Here we construct explicit block-encoding
circuits for two kinetic models of living polymerization: Model A, single-monomer polymerization, whose lower-bidiagonal rate
matrix is encoded via a sparse-oracle construction and a two-term linear combination of
unitaries (LCU) decomposition; and Model B, two-monomer copolymerization, where a bijective labeling of
polymer species by an integer index (the m-index) yields a structured sparse
matrix encoded via either a five-term LCU or a sparse-oracle construction. Numerical simulations with the sparse-oracle encodings reproduce the classical time
evolution for reactivity ratios drawn from reported olefin copolymerization systems spanning near-random ($r_1 r_2 \simeq 1$) and blocky ($r_1 r_2 > 1$) microstructures, and the LCU encodings are verified by explicit reconstruction of the encoded
matrix block. Resource estimation shows that both implementations require only $O(\log N)$
qubits in the
matrix dimension $N$ (an exponential
memory saving over the classical state space), with gate counts growing gradually, reaching $10^4$ to $10^5$ gates at $10^3$ system
qubits. These results establish a concrete
quantum circuit foundation for simulating polymerization kinetics on fault-tolerant
quantum hardware, and a first step toward exploiting exponential state-space compression for high-dimensional
polymer reaction networks.
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All authors Yuhei Ikeda, Hokuto Iwakiri, Soichiro Nishio, Kentaro Matsumoto
Instrumentation and Detectors · Cross-listed from hep-ex
Readout electronics for SUBMET
Claudio Campagnari, Sungwoong Cho, Suyong Choi et al. (+20 more)
A dedicated data acquisition (DAQ) system has been developed for the SUB-Millicharge ExperimenT (SUBMET) at the Japan
Proton Accelerator Research Complex (J-PARC), a search for particles carrying a fractional electric charge $Q = \epsilon e$ with $\epsilon$ below $\mathcal{O}(10^{-3})$, hereafter referred to as millicharged particles (mCPs). Because such particles are expected to produce at most a few scintillation
photons, the system is optimized for single-photoelectron detection from the photomultiplier tubes (PMTs), combining high-speed waveform digitization with precise timing. To capture eight consecutive
proton bunches of the 30 GeV J-PARC beam within a single trigger, the eight channels of the Domino Ring Sampler 4 (DRS4) chip are cascaded in groups of four to form two readout inputs, each sampling 4096 points continuously at 820.5 MHz over an effective time window of 5 us. After calibration, timing differences between channels are within 1 ns on the same DRS4 chip, 2 ns on the same board, and 8 ns across different boards, well within the 30 ns coincidence window of the experiment. The front-end electronics achieve an RMS noise below 0.4 mV. The baseline is deliberately offset upward such that the negative-going pulses span a larger fraction of the digitizer range, improving voltage resolution and dynamic range. A trigger control board aggregates data from multiple readout boards and sustains the data-transfer rate required for beam operation. The measured performance confirms that the DAQ system meets the timing, noise, and throughput requirements of the experiment.
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All authors Claudio Campagnari, Sungwoong Cho, Suyong Choi, Seokju Chung, Matthew Citron, Albert De Roeck, Martin Gastal, Seungkyu Ha, Andy Haas, Christopher Scott Hill, Insung Hwang, Hoyong Jeong, Jaebak Kim, Jeonghwa Kim, Hyunki Moon, Ryan Schmitz, David Stuart, Eunil Won, Jae Hyeok Yoo, Jinseok Yoo, Ayman Youssef, Ahmad Zaraket, Haitham Zaraket
Chemical Physics · Cross-listed from cond-mat.mes-hall
From London to Morse via Binnig, Quate, and Gerber
Sofia Alonso Perez, Matthew O. Blunt, Frederick Carlisle et al. (+30 more)
In their landmark paper introducing the atomic force microscope [Phys. Rev. Lett. 56, 930 (1986)], Binnig, Quate, and Gerber presciently anticipated that the technique would ultimately be capable of probing interactions running the gamut from weak van der Waals interactions to strong covalent bonding. They also highlighted that the tip-sample forces central to AFM are present, and often highly influential, in scanning tunnelling microscopy; indeed, this realisation directly inspired the invention of the force microscope. In this perspective for the Forty Years of AFM special issue, we review selected aspects of two decades of work from our group at the University of Nottingham that span the force range highlighted by BQG and are united by a common, central theme: the probe as active participant rather than passive observer. Our selection of results also covers length- and correlation-scales from the microscopic right down to the single chemical bond limit, tracking a spectrum of interactions from van der Waals/Hamaker forces, through hydrogen bonding, to covalent bonds and, finally, atom-by-atom assembly of metal clusters via vertical tip-sample transfer. Echoing BQG's own observations on the prevalence of probe-sample forces in STM, we also discuss recent evidence that tip-induced heterogeneity underpins first-passage dynamics in molecular
diffusion and highlight the challenges in acquiring non-invasive measurements of
diffusion barriers for adsorbed molecules that are readily perturbed by the probe. We close with a perspective on machine learning's growing role in automating tip-driven atomic and molecular manipulation.
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All authors Sofia Alonso Perez, Matthew O. Blunt, Frederick Carlisle, Neil R. Champness, Janette L. Dunn, Matthew Edmondson, Rowan Evers, Connor Fields, Subhashis Gangopadhyay, James Hayton, Samuel P. Jarvis, Filipe Junqueira, Lev Kantorovich, Brian Kiraly, Natalio Krasnogor, Ioannis Lekkas, Morten Møller, Philip Moriarty, Chris Pakes, Emmanuelle Pauliac-Vaujour, Oliver Phillips, Adrian Radocea, Philipp Rahe, Mohammad Abdur Rashid, Hongqian Sang, Alex Saywell, Nikhil Seeja Sivakumar, Peter Sharp, Andrew Stannard, Julian Stirling, Adam Sweetman, Simon Taylor, Richard A.J. Woolley
Computational Physics · Cross-listed from gr-qc
Picard Iteration for the Characteristic Initial Value Problem in Einstein Equations
Shengrong Wu
We present an iteration
algorithm for
vacuum and Einstein scalar-field equations in double-null gauge, which transform the non-linear PDE into systems of ODE. The numerical realization combines characteristic constraint solves, LGL spectral elements, pole-free spherical operators, Galerkin projection, and independent first-order residual and consistency checks.
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Optics
Fiber-based electro-optic dual-comb light source for fast linear and nonlinear spectroscopy
Debanuj Chatterjee, Fabio Motetta, Simon Boivinet et al. (+2 more)
Dual-comb spectroscopy (DCS) enables rapid, broadband and high-resolution optical measurements by mapping optical spectra into the
radio frequency (RF) domain. However, conventional DCS systems are fundamentally constrained by a tradeoff between optical bandwidth and interferogram acquisition speed, limiting their overall performance. Here, we demonstrate an all-fiber, polarization-maintaining (PM) frequency-agile electro-optic modulation (EOM) dual-comb source that simultaneously achieves a broad optical bandwidth of 10 THz and a high interferogram acquisition speed of up to 2.5 MHz. The high acquisition rate is enabled through an in-phase/quadrature (IQ) modulator-based architecture to shift the carrier frequency of one of the combs. We illustrate the performance of the source through proof-of-concept linear spectroscopy and nonlinear dual-comb coherent anti-Stokes Raman scattering (CARS) spectroscopy measurements. The combination of large spectral coverage, high refresh rate and an all-PM fiber configuration makes this dual-comb platform attractive for applications such as rapid molecular spectroscopy and high-speed nonlinear spectroscopic imaging.
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All authors Debanuj Chatterjee, Fabio Motetta, Simon Boivinet, Hervé Rigneault, Arnaud Mussot
Plasma Physics
Generalized Similarity Theory for Plasmas
Yangyang Fu
A generalized theory of
plasma similarity is established based on the scaling of the Boltzmann equation coupled with the full set of Maxwell's equations. The predicted similarity scalings are demonstrated through first-principles particle-in-
cell and fluid simulations across diverse operational regimes, including collisionless
plasmas with two-stream instabilities, pure electron diodes spanning the classical to relativistic regimes, electromagnetic-wave-driven
plasmas, and discharge
plasmas ranging from low to high ionization regimes. The generalized theory reveals the intrinsic scale-invariant nature of
plasmas under specified conditions, rooted in the
symmetry and scaling transformations of the governing equations.
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Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Stochastically Perturbed Weights: Ensembles from Deterministic Machine-Learning Weather Models
Simon Adamov, Oliver Fuhrer, Reto Knutti et al. (+1 more)
Machine-learning weather models (MLWMs) now match or outperform operational numerical weather prediction (NWP) at global medium-range
forecasting, at far lower inference cost. Many deployed MLWMs are deterministic, producing a single forecast with no estimate of its own uncertainty, whereas a growing family of trained-probabilistic models generate calibrated ensembles directly, at the price of a dedicated training run. We ask instead how much uncertainty can be extracted from a deterministic checkpoint that already exists, without retraining it. Where physical ensembles represent model uncertainty by stochastically perturbing parametrisation tendencies, we perturb the network's raw weight tensors at inference time, a scheme we call stochastically perturbed weights (SPW). We also ask whether it works, where and on which scales to inject the noise, and where it fails. A three-phase ablation across four deterministic backbones, Aurora, GraphCast, SFNO, and AIFS, selects one production baseline per model, benchmarked against the trained-probabilistic AIFS-ENS, FourCastNet 3 and Atlas as well as the operational ECMWF ensemble (IFS-ENS) over 112 initialisation times. At a 240 h (10-day) lead time the SPW ensembles reach continuous ranked probability skill scores (CRPSS) between 0.04 and 0.13 below the best trained-probabilistic baseline, at zero marginal training cost. No injection site works across models: the productive tensor group is architecture-specific, so SPW is at present a tuning procedure rather than a plug-and-play recipe. Its main failure mode is a coherent whole-field offset that overdisperses the domain mean, and restricting the noise to coarse scales or perturbing the initial conditions each repair part of it.
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All authors Simon Adamov, Oliver Fuhrer, Reto Knutti, Sebastian Schemm
Optics · Cross-listed from quant-ph
Performance of the subcarrier-wave quantum key distribution in the presence of spontaneous Raman scattering noise generated by classical DWDM channels
Fedor Kiselev, Roman Goncharov, Natalia Veselkova et al. (+3 more)
In this paper we study performance of the subcarrier-wave
quantum key distribution system (SCW QKD) in the presence of spontaneous Raman scattering (SpRS) noise generated by classical channels of dense wavelength division multiplexing (DWDM) network within a single-mode optical fiber. We present the mathematical model for evaluation of the
quantum bit error rate (QBER) and the secure key generation rate with the SpRS noise taken into account. We consider two regimes of the SCW QKD system: the continuous wave regime that uses continuous wave
laser and the pulsed regime. For these regimes, performance of the system is analyzed depending on receiver sensitivity of classical DWDM. It is found that the pulsed regime outperforms the continuous wave regime in both the secure key generation rate and the maximum achievable distance.
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All authors Fedor Kiselev, Roman Goncharov, Natalia Veselkova, Eduard Samsonov, Alexei D. Kiselev, Vladimir Egorov
Chemical Physics · Cross-listed from cs.LG
MLIP Detective: Active Failure Mode Discovery Beyond Benchmark Scores for Machine-Learning Interatomic Potentials
Ryuhei Okuno, Nontawat Charoenphakdee, Kaoru Hisama et al. (+1 more)
Universal machine-learning interatomic potentials (u-MLIPs) aim to generalize across diverse configurations.
Benchmarks enable reproducible evaluation but may not expose failures outside their predefined scope. Here, we show that physics-informed search can complement
benchmark-based evaluation by uncovering hidden failure modes. We introduce MLIP Detective, an agentic framework for active failure mode discovery. Starting from
benchmark evidence, MLIP Detective generates falsifiable, physics-informed failure hypotheses, screens them with inexpensive simulations, and escalates only the most suspicious cases to human experts together with proposed verification protocols. Without issue-specific prompting, MLIP Detective identified and characterized a systematic anomaly in MACE-MPA-0: the model predicted some relaxed adsorbate-surface systems involving O- or F-containing adsorbates to be higher in energy than their corresponding separated fragments. Using cross-model comparisons, MLIP Detective further inferred a likely training-data origin for the anomaly, consistent with recent reports.
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All authors Ryuhei Okuno, Nontawat Charoenphakdee, Kaoru Hisama, Yuta Tsuboi
Optics · Cross-listed from quant-ph
A theoretical study of subcarrier-wave quantum key distribution system integration with an optical transport network utilizing dense wavelength division multiplexing
Fedor Kiselev, Natalia Veselkova, Roman Goncharov et al. (+1 more)
In this paper we study the performance of subcarrier-wave
quantum key distribution (SCW QKD) in the presence of classical channels of optical transport network utilizing dense wavelength division multiplexing (DWDM). We consider the impact of spontaneous Raman scattering noise as well as the four-wave mixing and channel isolation efficiency. We calculate
quantum bit error rate as well as the secure key generation rate of SCW-QKD protocol for different parameters of DWDM system and
quantum channel allocations. Our calculations show, that
quantum channel allocation at the wavelength of 1310 nm is preferable to allocation at C-band due to lower nonlinear noise, despite fiber's larger attenuation at this wavelength.
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All authors Fedor Kiselev, Natalia Veselkova, Roman Goncharov, Vladimir Egorov
Optics
BRAHMS: A cross-platform graphical toolkit for (3+1)D simulation of three-wave mixing in $\chi^{(2)}$ nonlinear media, with GPU and CPU backends
Alfredo Daniel Sanchez Rossi and Daniel Alejandro La Valle Huignard
We present BRAHMS, a cross-platform (Linux/Windows) graphical user interface, with GPU (CUDA) and CPU (OpenMP) backends, for the efficient and accurate simulation of three-wave mixing processes involving focused and pulsed Gaussian beams. The package solves the coupled $\chi^{(2)}$ nonlinear Schrödinger equations, including diffraction, dispersion, walk-off, and phase-mismatch effects simultaneously, in full (3+1)D. To our knowledge, this is the first open-source package that solves the complete second-order (three-wave-mixing) nonlinear-optics problem in (3+1)D while fully exploiting the parallel capabilities of modern GPUs, with an equivalent CPU backend available for users without GPU hardware. The GPU implementation is inherently scalable thanks to its Thrust-based design. The package provides a valuable tool for experimental design and for studying three-dimensional field propagation in nonlinear three-wave interactions, supporting applications such as second-harmonic generation (SHG), sum-frequency generation (SFG), and optical parametric generation (OPG). Its GUI requires no programming experience, so that users can readily set up simulations and interpret their results.
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Atmospheric and Oceanic Physics
GCMagicc v1: a fast generative emulator for multivariate climate-impact ensembles
Nicolai Meinshausen, Malte Meinshausen, Jared Lewis et al. (+6 more)
Projecting the impacts of
climate change requires large ensembles of
climate variables that match historical observations, align with the warming ranges assessed by the IPCC, and can efficiently run new future emissions scenarios, including the newest generation of
climate model scenarios (CMIP7) and pathways consistent with countries' Paris Agreement pledges. Generating such ensembles at the scale needed for impact studies is normally computationally prohibitive. We close this gap with GCMagicc, a hybrid model that pairs a simple physical
climate model with machine learning to generate ensembles of 10
climate variables at the resolution of full-scale Earth system models, without relying on GPU resources or retraining for new scenarios. Trained on 32 CMIP6 Earth system models and observational/reanalysis data, GCMagicc complements rather than replaces Earth system models. We apply it to a range of future pathways: the canonical SSP scenarios of the latest IPCC report (1.2-6.1{\deg}C warming, min-max across scenarios of 5-95 percentile ranges), current policies (2.3-4.0{\deg}C), national pledges under the Paris Agreement (1.5-3.3{\deg}C) and the CMIP7 range from the 'VL' to 'H' scenarios (1.2-4.2{\deg}C), releasing a large public
dataset. As an illustration, we perform an attribution analysis of the severe 2025 Iranian drought using GCMagicc ensembles, with three CMIP6 large ensembles for comparison, with and without anthropogenic forcings. The results suggest a strong anthropogenic signal: a median probability of drought at least as severe as observed of 29% with anthropogenic forcing, and zero under natural-forcing-only simulations. In the future, drought conditions are projected to materially worsen, amplifying the potential for agricultural and food security impacts and geopolitical conflicts that use water scarcity as a weapon. GCMagicc data is available at https://gcmagicc.org.
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All authors Nicolai Meinshausen, Malte Meinshausen, Jared Lewis, Zebedee Nicholls, Sarah Schöngart, Alister Self, Xinwei Shen, Karla Spiller, Elisabeth Vogel
Atomic Physics
Nonlinear dynamics in an artificial feedback spin maser II: dependence on feedback configurations and time crystal effects
Lan Wu, Qingchang Li, Weiyu Wang et al. (+2 more)
Artificial feedback
spin masers exhibit rich nonlinear dynamics, yet a systematic framework for understanding and controlling their behaviors remains elusive. In this paper, we consider the polarity and
geometric configuration of the feedback field as important control parameters for studying the nonlinear dynamics of
spin masers. By cooperatively tuning the strength and phase delay of feedback field, we investigate the
spin dynamics at various configurations of the feedback field. Periodic, quasiperiodic and chaotic
spin oscillations are uncovered. Specifically, a unique
spin oscillation mode akin to the time
crystal effects emerges within a certain parameter regime. This work presents a different viewpoint understanding the nonlinear dynamics of
spin masers including the time
crystal.
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All authors Lan Wu, Qingchang Li, Weiyu Wang, Qianjin Ma, Guobin Liu
Applied Physics · Cross-listed from quant-ph
Instantaneous-Frame Theory of Strongly Driven Parametric Gates
Kentaro Kubo, Shinichi Inoue, Jushin Tei et al. (+3 more)
Parametric two-
qubit gates using tunable couplers are a promising approach to scalable superconducting
quantum processors. However, conventional theories formulated in the idle eigenbasis break down in the large-amplitude regime required for fast gate operations. Here, we show that parametric gates are more naturally and accurately described as coherent rotations between instantaneous eigenstates rather than idle eigenstates. This approach may appear counterintuitive, since instantaneous eigenbases are usually associated with adiabatic dynamics. However, they also form a natural moving frame for driven dynamics, where the non-Abelian Berry connection governs transitions and provides a compact description of the exchange and effective $ZZ$ couplings. We show that this theory achieves quantitative agreement with exact numerical simulations across representative tunable-coupler architectures, including double-transmon, capacitively shunted double-transmon, and single-transmon couplers. The description remains accurate even in regimes where idle-frame descriptions fail, while substantially reducing the reliance on computationally demanding exact simulations. These results establish an instantaneous-frame theory of parametric gates beyond conventional idle-frame models and provide a new perspective on driven
quantum dynamics.
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All authors Kentaro Kubo, Shinichi Inoue, Jushin Tei, Yinghao Ho, Yasunobu Nakamura, Hayato Goto
Optics
Thresholdless corner vortex solitons in fractal Sierpiński topological insulators
Yiqi Zhang, Alexander V. Kireev, Victor O. Kompanets et al. (+12 more)
Quantized
vortices are ubiquitous in physics, spanning superconductivity, astrophysics, superfluid condensed matter systems, and nonlinear optics. Yet
embedding vorticity into topologically protected nonlinear states has remained a major challenge, with all previously observed corner
solitons in higher-order topological
insulators (HOTIs) exhibiting only trivial phase distributions. Here, we report on the first realization of stable topological corner
vortex solitons in a photonic fractal HOTI. Using an array of
laser-written waveguides in the shape of Sierpiński gasket with a controllable distortion, we design linear topological
vortex modes, from which nonlinear corner
vortex solitons bifurcate. Moreover, we demonstrate that these
solitons exhibit exceptional robustness across a broad power range and, unlike
vortex solitons in topologically trivial
lattices, form without a power threshold. Our results introduce the angular momentum degree of freedom into the physics of topological corner modes, opening prospects for topologically protected
vortex-based photonics.
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All authors Yiqi Zhang, Alexander V. Kireev, Victor O. Kompanets, Sergey Y. Alyatkin, Nikita S. Kostyuchenko, Sergei A. Zhuravitskii, Nikolay N. Skryabin, Khalil Sabour, Alexander A. Kalinkin, Yongdong Li, Sergei P. Kulik, Pavlos G. Lagoudakis, Sergey V. Chekalin, Yaroslav V. Kartashov, Victor N. Zadkov
Atomic Physics
Ultralow Frequency Magnetic Comb Using Pulse Modulated Spin Maser
Weiyu Wang, Qingchang Li, Erwei Li et al. (+4 more)
Frequency combs are widely used in fundamental physics and practical applications at various areas. Here we report the realization of an ultralow frequency
magnetic comb using a pulse modulated
spin maser in the Rb-Xe hybrid gaseous
spin system. The
magnetic frequency comb has a spectral distribution depending mainly on the pulse duration, the strength and phase shift of the feedback field. A simple theoretical model is given in terms of the
spin echo chain and agrees well with the experimental results. The
magnetic frequency comb works in an ultralow frequency range and reaches a frequency resolution down to tens of nanohertz.
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All authors Weiyu Wang, Qingchang Li, Erwei Li, Lan Wu, Qianjin Ma, Shougang Zhang, Guobin Liu
Chemical Physics
Fixed-Dimensional Latent Flow for Generating Variable-Size 3D Molecules
Weichi Yao, Cameron Gruich, Bryan R. Goldsmith et al. (+1 more)
Molecular size is coupled to composition, structure, and function, yet most 3D molecular generators require a predefined atom count. We introduce Equivariant-Free
Transformer-Autoencoded Latent Flow Matching, a two-stage framework that samples a fixed-dimensional latent vector using flow matching and uses an autoregressive
Transformer to determine molecular size, atom types, coordinates, and chemical attributes. Canonical atom ordering and rigid-pose
alignment enable
Transformers without equivariant layers, while decoded attributes guide bond reconstruction. On PCQM4Mv2, unconditional generation yields 87.9% unique, novel molecules passing sanitization and PoseBusters checks, exceeding baselines with lower end-to-end training and sampling time and higher end-to-end throughput. Across ten target HOMO-LUMO gaps, internal ranking retains 30% of screened candidates and increases the density functional theory-verified hit rate within 0.1 eV from 25.0% to 52.4%, while largely preserving novelty and diversity. These results demonstrate fixed-dimensional latent generation with autoregressive decoding as a practical approach to molecular design without prespecifying size.
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All authors Weichi Yao, Cameron Gruich, Bryan R. Goldsmith, Yixin Wang
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Band Structure Modulation of ZrO2 Nanoparticles for Control of CO Adsorption Properties: A Combined Density Functional Theory - Density Functional Tight Binding Study
Kexin Chen, William Dawson, Aulia Sukma Hutama et al. (+4 more)
We present a combined density functional theory (DFT) and density functional tight binding (DFTB) study of zirconia (ZrO2) nanoparticles of experimentally relevant sizes of several nanometers and their interactions with the CO molecule. A hybrid DFTB - Force Field (DFTB-FF) framework is developed, whereby band structure calculations rely on an existing Slater-Koster framework, while the accuracy of structural optimization and adsorption properties is controlled by the introduction of classical long-range interatomic potentials into DFTB instead of the traditional repulsive potentials. Additionally, coordination-dependent Zr-C potentials are introduced to account for the distinct local chemical environments of bulk-like facet sites and under-coordinated tip and edge sites, thereby improving the description of CO adsorption. This hybrid DFTB-FF approach substantially improves the robustness of geometry optimization and provides a practical strategy for extending the applicability of DFTB to complex
oxide nanostructures. The calculations reveal that termination stoichiometry can be used to engineer intrinsic, p-type, or n-type electronic structures and thereby tune the adsorption activity of zirconia nanoparticles. While stoichiometric nanoparticles do not activate the C-O bond, low-coordinated sites in Zr-rich (n-type) nanoparticles exhibit chemisorption accompanied by charge donation into a CO antibonding LUMO-derived orbital, resulting in C-O bond activation. These results demonstrate that stoichiometry-controlled electronic structure and under-coordinated surface sites introduced by nanostructuring play a key role in governing the adsorption strength and reactivity of zirconia nanoparticles.
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All authors Kexin Chen, William Dawson, Aulia Sukma Hutama, Takahito Nakajima, Keisuke Kameda, Manabu Ihara, Sergei Manzhos
Atomic Physics
Nuclear slowing-down factors in alkali-metal vapors
Vasiliki Koutrouli, Georgios Vasilakis and Kostas Mouloudakis
Nuclear slowing-down factors account for the sharing of angular momentum between the electron and the
nucleus in the effective Bloch description of alkali-metal
spin dynamics in the
spin-exchange-relaxation-free (SERF) regime. For collinear optical pumping and
magnetic field, we find that longitudinal and transverse
spin dynamics are characterized by different polarization-dependent slowing-down factors. Transverse dynamics are governed by the conventional factor $q(p)$, with $p$ the electron
spin polarization, whereas longitudinal relaxation is governed by $q(p)+p\,dq(p)/dp$. The two factors coincide at zero polarization but differ substantially at high polarization, where the single-factor description overestimates the underlying pumping and relaxation rates inferred from longitudinal transients by factors approaching two to four, depending on the
nuclear spin. We further derive closed-form expressions, valid at arbitrary polarization, for the residual
spin-exchange relaxation of the transverse
spin components at finite
magnetic field along the pumping axis. This contribution is quadratic in the
magnetic field and can become comparable to the zero-field linewidth at fields well within the SERF regime. The results are obtained by perturbation theory on the density-
matrix dynamics linearized around the stationary state and are verified by numerical solutions of the microscopic density-
matrix equation. These findings refine the effective Bloch description of alkali-metal
spin dynamics and are directly relevant to atomic magnetometers and alkali-metal–noble-gas comagnetometers, including those used in precision searches for physics beyond the Standard Model.
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Classical Physics
Projection Angles of Projectiles in Sports: Qualitative Assessment of the Effects of Aerodynamic Forces or Run-Up
Kazuhiro Tsuboi
We examine two major factors that influence the optimum projection angle: aerodynamic forces and the effect of run-up. With respect to aerodynamics, we consider not only the drag but also the lift generated by
spin during flight. By linearizing the equations of motion that include these forces, we derive perturbation solutions with respect to drag and lift coefficients and clarify their qualitative effects. The results show that both drag and lift reduce the optimum projection angle, with the latter exerting a stronger influence. To investigate the effect of run-up, we use an extended projection model in which the initial speed depends on the initial angle. Analysis of this model reveals that a stronger run-up increases the relative projection angle but decreases the launch angle observed from the ground. These findings provide a mechanical explanation for the release angle in shot put and the takeoff angle in long jump. The present study establishes a simple theoretical framework for clarifying the respective roles of aerodynamic and run-up effects in determining the optimum projection angles in sports.
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Atmospheric and Oceanic Physics
EastAsiaClimateExtremes: An AI-Ready Dataset of Weekly Atmospheric and Oceanic Extremes over East Asia for Subseasonal Prediction Research
Miae Kim, Yun-Young Lee and Uran Chung
Despite growing interest in AI-based prediction of
climate extremes, event- or label-based AI-ready extreme
climate datasets remain limited, constraining efforts to systematically characterize and forecast such phenomena. To address this gap, we present EastAsiaClimateExtremes, an open
dataset that provides ERA5/OISST reanalysis-based weekly extreme labels and event-based metrics for anomalously high temperature (AHT), heavy rainfall (HR), and marine heatwaves (MHW) over East Asia, together with analysis workflows hosted on GitHub to facilitate reproducibility and adaptation. The
dataset is fully documented and co-registered with ECMWF S2S hindcast outputs on a common spatial grid and temporal framework, thereby enabling direct comparison between reanalysis-derived labels and dynamical model forecasts. This unified
dataset serves as a reference framework for East Asian
climate extreme research and AI-based subseasonal-to-seasonal prediction. It supports both quantitative characterization of the spatiotemporal occurrence of regional extremes and systematic diagnosis of S2S model skill in reproducing extreme signals. Beyond these immediate applications, the
dataset enables a broader range of studies such as extreme event attribution and compound risk analysis. The accompanying analysis workflows characterize the historical statistics of reanalysis-based weekly extremes-including occurrence frequency and mean and maximum intensity-together with their climatological means and trend characteristics, and further assess the skill of the ECMWF hindcast.
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Computational Physics
Beyond Residuals: Energy based solutions of partial differential equations using scientific machine learning
Timon Rabczuk and Yizheng Wang
Energy-based approaches provide a natural and physically consistent framework for a large class of partial differential equations arising in solid and fluid mechanics, where the governing equations follow from variational principles. In contrast to residual-based physics-informed neural networks (PINNs) and their weak-form variants, which enforce the strong or weak form of the equations through loss minimization, the Deep Energy Method (DEM) directly computes the solution as the minimizer of an energy or incremental potential functional. This eliminates the need for residual weighting, avoids high-order derivatives, and enables the direct enforcement of physical constraints through the variational structure.In this work, we systematically revisit the Deep Energy Method, placing it in the broader context of physics-informed learning and variational modeling. We clarify the relationship between DEM, PINNs, and VPINNs, and identify the class of problems for which energy minimization provides intrinsic advantages in terms of stability, robustness and interpretability. Particular emphasis is placed on incremental variational formulations, which allow DEM to be applied to nonlinear, history-dependent and time-dependent problems, including phase-field fracture and dissipative systems. The variational structure underlying DEM further provides a natural foundation for optimization and inverse problems, where the energy functional acts as a physics-based constraint rather than a residual penalty. Through a series of numerical examples, we demonstrate that DEM offers a principled and effective alternative to residual-based methods for variational problems, highlighting its strengths and limitations relative to existing physics-informed approaches.
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Fluid Dynamics · Cross-listed from cond-mat.soft
A unified description of flow-induced scission of wormlike micelles under shear and extensional flows
Yusuke Koide, Takato Ishida, Takashi Uneyama et al. (+1 more)
We investigate flow-induced scission of wormlike micelles under different flow kinematics using dissipative particle dynamics simulations of surfactant solutions in shear, uniaxial extensional, planar extensional, and biaxial extensional flows. The average lifetime of wormlike micelles is used to quantify the degree of scission. While flow-induced scission occurs in all flow types when the deformation rate is sufficiently high, the dependence of the average lifetime on the deformation rate differs among flow types. To provide a unified description, we introduce an effective extension rate determined by the velocity gradient tensor and micellar orientation. When expressed in terms of this effective extension rate, the average lifetimes obtained under all flow types considered collapse onto a single curve. These results demonstrate that a unified description of flow-induced scission requires not only the strength and kinematics of the imposed flow, but also the micellar orientation relative to the extensional direction.
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All authors Yusuke Koide, Takato Ishida, Takashi Uneyama, Yuichi Masubuchi