Chemical Physics
ChemReporter: A Framework for Curating and Exporting Large-Scale Chemical Datasets for MLIP Training
Marie Bluntzer, Jules Tilly and Christoph Brunken
Training set quality and diversity are key determinants of the reliability of machine learning interatomic potentials (MLIPs), yet using massive
datasets in full is often impractical and redundant, making intelligent data selection essential. A major bottleneck, however, is the lack of infrastructure for uniformly accessing, curating, and subsampling heterogeneous large-scale chemical
datasets, which differ widely in structure, metadata, and file format. We address this gap with ChemReporter, a modular, method-agnostic framework that converts arbitrary molecular and materials
datasets into a unified, queryable representation and exports the results directly into MLIP-ready training data. ChemReporter operates in three decoupled stages: processing, which parses raw
datasets into a partitioned Apache Parquet repository enriched with structural, physical, and chemical metadata; querying, which filters and samples this repository via a CLI or Python API using arbitrary selection criteria, from simple physical constraints to custom, user-defined strategies; and exporting, which streams the selected subset into an HDF5 file ready for direct use in modern MLIP training frameworks. Throughout this process, every exported data point remains traceable to its original source entry, and
dataset exports can be reliably reproduced given the same configuration and query database version. Because data is stored in a queryable, disk-backed format, ChemReporter can process
datasets far larger than available
memory, allowing it to scale to billion-structure
datasets on standard compute infrastructure. ChemReporter is available on GitHub and PyPI under the Apache License 2.0.
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Optics · Cross-listed from astro-ph.SR
Machine Learning in Application to Automatic Noise Processing of Solar Spectrograms
I. I. Yakovkin, A. O. Bartenev and N. V. Petrova
In this paper, a machine learning approach for processing
solar spectral data were developed. Its performance was demonstrated using the example of the limb
solar flare on July 17th, 1981. The results indicated that machine learning can be effectively utilized to transform between differently digitized spectra, fill gaps in unique experimental data, and undertake spectrum cleaning. Specifically, convolutional neural networks were devised to transform between reflective and transmissive scans of a
solar flare spectrogram. This can be a convenient technique for treating the spectrograms of unique
solar events, most notably increasing the proportion of observational spectra that can be further analyzed. Namely, in subsequent research, we will be able to confidently incorporate data such as that captured on the edges of spectrograms, which was previously deemed insufficiently reliable due to limitations of available processing techniques. This will consequently increase the number of spectral lines studied for certain observed events, which is paramount for constructing physical models, as the spectral peculiarities are expected to manifest consistently across different spectral lines. The developed approach also notably facilitates the detection and removal of
impurities in the spectrograms. Previously, each distinct feature in the spectra was manually scrutinized to check its integrity in order to be excluded if identified as an
impurity, such as a scratch or a
dust particle. By employing the suggested protocol for treating the spectrogram, which includes scanning the spectrogram using multiple distinct techniques and then leveraging machine learning for comparison, the process of excluding
impurities can now be automated. Furthermore, the spectrum areas affected by such exclusions can be restored, enabling further analysis.
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Plasma Physics
Assessing the Projector Augmented-Wave Method for Stopping Power Calculations
Bryn Lloyd, Dirk O. Gericke, Gilles Rodway-Gant et al. (+1 more)
The stopping power of charged particles is investigated using time-dependent density functional theory (TDDFT). Such simulations are made possible by recent advances in computational resources and numerical implementations of this first-principles method. In practice, DFT simulations widely employ the projector augmented-wave (PAW) method to approximate all-electron behaviour, but the implications of the PAW approximation for non-adiabatic stopping simulations remain insufficiently explored. Here, the suitability of the PAW method for stopping power simulations is evaluated. A workflow for generating and selecting PAW
datasets tailored to these simulations is developed, enabling systematic optimisation of augmentation radii and projector constructions. The approach is applied to
proton stopping in FCC aluminium, demonstrating how
dataset design influences stopping predictions, and enabling an investigation of
crystal channelling effects on charged-particle transport.
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All authors Bryn Lloyd, Dirk O. Gericke, Gilles Rodway-Gant, Gianluca Gregori
Chemical Physics
Extracting a nitrile-centered, ether-assisted motif hierarchy for lithium-battery electrolyte design from billion-scale molecular space
Yifeng Xia, Guanghui Wang, Sining Wang et al. (+4 more)
Designing electrolyte molecules for lithium batteries requires balancing electronic stability with appropriate Li+ solvation, yet the structural basis remains unclear across chemically diverse molecules. High-throughput screening expands the searchable space, but ranked candidates alone do not reveal recurring motifs or their applicability limits. We searched nearly one billion GDB13 structures using electronic–solvation descriptors without explicit functional-group preferences or scaffold constraints. Across descriptor weights, high-ranking populations separated into a nitrile-dominant regime and a coexistence regime containing substantial fractions of both nitrile- and ether-containing molecules. These regimes together define a nitrile-centered, ether-assisted motif hierarchy: nitrile remains favored across broad weight ranges, whereas ether becomes prominent under stronger electrostatic and polarity constraints. Encoding this hierarchy in a generative model expands the candidate space beyond GDB13 and yields high-scoring fluorinated structures without an explicit fluorination reward. Explicit-solvent molecular dynamics simulations show weak, exchangeable coordination of representative candidates without displacing ethylene carbonate from the dominant first solvation shell around Li+; effects on ion association and transport depend on molecular structure and concentration. These results establish a quantitative, interpretable and physically bounded motif hierarchy that systematizes established nitrile and ether chemistry for lithium-battery electrolyte design.
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All authors Yifeng Xia, Guanghui Wang, Sining Wang, Wenting Chen, Zheng Cheng, Jinzhe Zeng, Qiangqiang Gu
Chemical Physics · Cross-listed from quant-ph
Optimizing Subspace Expansion in Quantum Chemistry through Operator Selection and Reference State Choice
Konstantin Lamp, Alejandro D. Somoza, Elias Walter et al. (+4 more)
The Virtual
Quantum Subspace Expansion (VQSE) extends the Variational
Quantum Eigensolver (VQE) by leveraging additional measurements on the reference state to capture the influence of excluded virtual orbitals. This makes VQSE attractive for chemical applications where accurate energy differences along potential energy surfaces are crucial for modeling reaction rates and kinetics. In this work, we analyze VQSE performance on H$_2$ dissociation including references that use Hartree–Fock molecular orbitals with broken
spin symmetry. We identify two mechanisms which affect accuracy: overlap of the reference state with the exact full configuration interaction (FCI) wavefunction and operator pool expressivity. We show these mechanisms are strongly co-dependent. When operators are restricted to act only from the active to the virtual space, results become highly sensitive to the reference, and enlarging the active space does not guarantee improved accuracy. In this case, prioritizing reference overlap over energy minimization is therefore essential. Adding single excitations and number operators within the active space recovers the accuracy of MR-CISD (multi-reference configuration interaction singles and doubles) regardless of the reference. In our noisy hardware experiments, we achieve chemical accuracy by adding additional operators and using strict regularization. These findings motivate careful co-design of reference fidelity, pool expressivity, and hardware constraints for practical VQSE deployment.
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All authors Konstantin Lamp, Alejandro D. Somoza, Elias Walter, Marina Walt, Michael Marthaler, Maria Fernanda Juarez, Birger Horstmann
Physics and Society
Exploring Initial CO2 Transport Topologies for Germany's Carbon Management
Toni Seibold, Luna Lütz and Tom Brown
Germany's
climate target requires carbon capture and sequestration for residual emissions from hard-to-abate sectors such as cement production and waste incineration. Planning this infrastructure is challenging because capture investments and CO2 transport networks are strongly interdependent. Existing energy system models capture system-wide interactions but provide limited insight into robust transport
topologies, while detailed infrastructure studies usually neglect feedbacks with the wider energy system. This study combines graph-theoretic
topology generation with a large-scale sector-coupled energy system model to evaluate alternative CO2 transport networks for Germany in 2035. We generate and evaluate 60 candidate
topologies that differ in network length, sink accessibility and source prioritization. The deployment of a domestic CO2 transport network reduces German consumer costs by around 22 bnEUR/a relative to a scenario without CO2 pipelines. The resulting network is primarily used by industrial point sources, including process emissions, cement production and biomass-based carbon dioxide removal, while contributions from backup power generation remain comparatively small. Transport corridors are repeatedly selected and utilized in north-western Germany, reflecting the concentration of industrial CO2 sources and access to international sequestration routes. Early access to Dutch sink infrastructure provides particularly high system value. Compared to 1500 km, as little as 500 km of CO2 pipeline infrastructure captures most of the economic benefit when North Rhine-Westphalia is connected to the Netherlands, while also limiting long-term transport infrastructure lock-in. These findings suggest that the availability of CO2 transport infrastructure is more important than the exact
topology once major industrial source regions and sink access points are connected.
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Computational Physics
Unlocking Multi-Component Bulk-Materials Molecular Dynamics with a Small-Footprint Machine Learning Interatomic Potential
Yucheng Ouyang, Xin Chen, Ying Liu et al. (+8 more)
Bulk materials, as opposed to nanomaterials, require molecular dynamics (MD) simulations on a large spatial scale (~10^9 atoms or more) to adequately capture their atomic-scale physical properties. Previously, the introduction of machine-learning interatomic potentials (MLIPs) has extended MD to this scale, but even single-component bulk systems require tens of thousands of GPUs on high-end supercomputers. However, multi-component bulk MD simulations remain barely achievable, as the HBM footprint of existing MLIPs - already substantial for single-component systems - grows explosively in multi-component scenarios. This paper proposes an MLIP with a small HBM footprint - less than 3% that of existing MLIPs - unlocking multi-component bulk MD using only hundreds of GPUs. This is achieved by first identifying feature vectors and intermediate tensors as the two primary contributors to HBM footprints in existing MLIPs. To address these two sources, the dimensionality of the feature vectors has been reduced by introducing physical and chemical knowledge, and intermediate tensors have been eliminated by aggressively fusing all kernels into a single mega-kernel. In evaluation, the proposed MLIP has used 144 NVIDIA A100 GPUs to perform MD simulations on a 6-component bulk system with 1.14x10^9 atoms, while previously such MD simulation spatial scale has been restricted to unary systems and typically achieved on high-end supercomputers equipped with tens of thousands of GPUs.
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All authors Yucheng Ouyang, Xin Chen, Ying Liu, Lifang Wang, Xingyu Gao, Xiawei Du, Jianierken Habudelihan, Haifeng Song, Huimin Cui, Xiaobing Feng, Jingling Xue
Classical Physics
Modeling Newtonian noise of acoustic origin in the Virgo gravitational wave detector
Lionel Maurin, François Gautier, Maël Brun et al. (+9 more)
Since the first gravitational-wave (GW) detection of September 14th 2015 and with hundreds of gravitational-wave sources identified by the LIGO-Virgo-KAGRA network, GW have produced many important results in astrophysics and fundamental physics. Along with planned new data takings, current detectors will be upgraded and new project, such as Einstein
Telescope and
Cosmic Explorer, are under study. Among noises limiting low frequency sensitivity, vibro-acoustic noises are particularly important. In this work, we focus on the
gravity gradient noise (also called Newtonian noise) of acoustic origin, which refers to the small fluctuations in the
gravity field resulting from the acoustic pressure field present in the experimental areas of the detector. The induced noise is quantified in an original way, using a detailed nu- merical acoustic model of the experimental room, when the pressure field is excited by the air conditioning system. The method is used for Virgo, but it can be easily extended for future detectors and used to guide the design of caverns and experimental areas.
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All authors Lionel Maurin, François Gautier, Maël Brun, Soizic Terrien, Matteo Barsuglia, Donatella Fiorucci, Irene Fiori, Maria Tringali, Roberto Passaquieti, Federico Paoletti, Mariusz Suchenek, Tomasz Bulik
Geophysics
Coupled Transient Processes Govern Intraplate Earthquake Swarm Evolution: Insights from the 2019-2020 Palghar Sequence
Ratna Bhagat, Pathikrit Bhattacharya, K. M. Sreejith et al. (+2 more)
Earthquake swarms provide a natural window into fault response to transient perturbations, yet their driving processes are commonly interpreted using fluid-driven and aseismic-slip-driven end-member models. Intraplate swarms offer a unique setting to test these models because low secular tectonic loading heightens the sensitivity of faults to transient, non-tectonic forcing. We use a machine-learning-enhanced catalog of $\sim$50,000 earthquakes from the 2019–2020 Palghar earthquake swarm in western India to test this end-member framework. High-resolution relocations, together with moment tensor solutions, reveal two shallow normal faults in a granitic basement, with seismicity sequentially migrating from the western to the eastern fault before expanding into the intervening damage zone. Although the swarm exhibits an overall
diffusion-like expansion, the relocated seismicity reveals a persistent $\sim$5-km-deep localized seismicity band and repeated migration fronts sometimes propagating faster than expected from fluid
diffusion alone. The velocity–duration scalings of these intermittent episodes span both fluid- and slow-slip-driven regimes. Sequential fault activation and contrasting migration styles throughout the swarm duration reveal dynamics that cannot be explained by either end-member mechanism alone. Instead, the Palghar swarm evolved through coupled fluid-assisted deformation and transient stress transfer within an interacting fault network, with migration episodes consistent with aseismic deformation. These observations reveal that high-resolution catalogs can disentangle transient processes hidden within apparently diffusive swarm behavior. More broadly, intraplate earthquake swarms provide powerful natural laboratories for resolving how coupled transient processes govern earthquake triggering and fault interaction in stable continental crust.
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All authors Ratna Bhagat, Pathikrit Bhattacharya, K. M. Sreejith, Harsha S. Bhat, Vineet K. Gahalaut
Optics · Cross-listed from nlin.PS
Pure-Quartic Optical Shock Waves
Sanjida Yeasmin, Sathyanarayanan Chandramouli, Ziad H. Musslimani et al. (+1 more)
In this paper, we study the emergence and dynamics of pure-quartic optical dispersive shock waves governed by the nonlinear Schrödinger (NLS) equation with self-defocusing Kerr nonlinearity and fourth-order dispersion. The corresponding dispersionless hydrodynamic system reveals a distinct mechanism for wave breaking, driven by the nonlinear self-steepening of the hydrodynamic velocity. We illustrate this mechanism through optical dam-break configurations described by Riemann problems and show that, in contrast to the classical quadratic NLS equation, wave breaking occurs prior to wave splitting even in the small amplitude regime. Numerical simulations of the pure-quartic NLS (PQNLS) equation and its hydrodynamic approximation confirm these qualitatively distinct dynamical regimes.
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All authors Sanjida Yeasmin, Sathyanarayanan Chandramouli, Ziad H. Musslimani, Andrea Blanco-Redondo
Optics
Minkowski and Abraham Momenta Revisited from the Perspective of Photon Dynamics
Lili Yang, Pengming Zhang and Longlong Feng
The distinction between Minkowski and Abraham momenta has traditionally been discussed in the context of electron dynamics. In this work, we revisit the problem from the perspective of
photon dynamics in a medium, and show that, within a field-theoretic formulation, their relation is naturally encoded through covariant and ordinary derivatives, together with a medium-induced gauge field. In this quantized framework, the microscopic origin of the optical
Hall effect is identified as a
spin-Zeeman-type coupling that emerges from the Dirac-like equation for
photons.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
Dimensionality Mismatch Enables Decoupled Heat and Charge Transport
Luman Shang, Shuming Zeng, Chenhan Liu et al. (+1 more)
Decoupling heat and charge transport is a key challenge in thermoelectrics. Here, we identify a route to spatially separate
phonon and carrier transport in quasi-one-dimensional materials through high-throughput screening of the Materials Project database. Representative Sn$_2$S$_3$ and SbTeI exhibit a strong-intrachain–weak-interchain bonding hierarchy that favors
phonon propagation along the chains while suppressing transverse
lattice heat transport. In contrast, transverse valence-band states provide effective interchain electronic coupling and relatively light hole transport. This mismatch between
lattice and electronic transport dimensionalities produces an inverted thermal–electrical anisotropy. Across the screened candidates, interchain
lattice thermal conductivity is strongly suppressed, whereas hole transport remains weakly anisotropic or even favors the interchain direction. For SbTeI, this decoupling yields a maximum $zT$ of approximately 2.1 near 900~K. These results establish dimensionality mismatch as a general strategy for decoupling
phonon and carrier transport in thermoelectric materials.
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All authors Luman Shang, Shuming Zeng, Chenhan Liu, Yu Wu
Instrumentation and Detectors
TARDIS Lab: infrastructure for characterization and assembly of highly granular silicon calorimeter modules for LUXE and future Higgs Factories
Melissa Almanza-Soto, César Blanch, Shan Huang et al. (+2 more)
The TARDIS Lab has been established at IFIC (CSIC-UV) to develop infrastructure and expertise in high-granularity silicon calorimetry. The laboratory provides an ISO7 clean-room environment and dedicated instrumentation for silicon sensor characterization and ultra-thin calorimeter module assembly. It supports developments of silicon-based high-granularity calorimetry concepts pursued within CERN DRD Calo (DRD6) and the FCAL Collaboration. These developments serve not only as technological prototypes of the electromagnetic calorimeters and luminometers at future
Higgs-factory proposals (FCC-ee, Linear
Collider Facility at CERN, International Linear
Collider, etc.) but are also essential for the strong-field QED program of the LUXE experiment. We describe the laboratory infrastructure, assembly workflows and DESY test-beam validation of fully assembled calorimeter modules. The assembled ECALp modules have an average thickness of approximately 760~$\mu$m, while laboratory tests of 4352 channels identified connectivity problems in only 0.5% of them. For the two SiWECAL modules assembled in 2025, connectivity problems were observed at the level of approximately $10^{-3}$ of the instrumented pads, with no mechanical failures or sensor delamination after transport and beam operation. We also present ongoing infrastructure upgrades and production milestones for 2026-2027.
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All authors Melissa Almanza-Soto, César Blanch, Shan Huang, Adrián Irles, Carlos Orero
Applied Physics
Topological Skyrmion-type microparticle manipulation based on surface acoustic wave phase modulations
Jiaqi Zhang, Decai Wu, Tingfeng Ma et al. (+3 more)
Surface acoustic wave (SAW) micromanipulation enables the precise, non-contact handling of microscale particles and has attracted considerable interest in microfluidics and biomedicine. However, conventional SAW platforms generally rely on simple interference fields which are susceptible to fabrication imperfections and environmental perturbations, resulting in limited trapping stability. Here, we develop a SAW-based acoustofluidic platform that generates an acoustic skyrmion
lattice through the coherent interference of three SAWs. The topologically structured field provides robust phase singularities and a stable gradient-force landscape, enabling microparticles to be localized at predefined
lattice sites and supporting controllable rotational manipulation. Independent modulation of the amplitude and phase of the electrical inputs allows the field strength to be tuned for particles of different sizes. Numerical simulations and proof-of-concept experiments confirm particle trapping and ordered
lattice assembly in the acoustic skyrmion field, demonstrating the feasibility of translating topological acoustic textures into practical on-chip manipulation functions. This reconfigurable strategy offers a route to robust SAW manipulation and may support applications in single-
cell analysis, three-dimensional
cell assembly, high-throughput screening, and microscale and nanoscale device assembly.
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All authors Jiaqi Zhang, Decai Wu, Tingfeng Ma, Chenbowen Lou, Bowei Wu, Shuanghuizhi Li
Optics
Collective Blinking of Upconversion Emission in Lanthanide-doped Nanocrystals
Tianzi Ma, Dingxin Huang, Yongjun Meng et al. (+6 more)
Fluorescence blinking, often regarded as a limitation for stable emitters, can enable super-resolution localization microscopy and serve as a versatile reporter of the photophysical states of
quantum emitters and their interactions with local environment. However, conventional blinking emitters are typically single
quantum systems with Stokes-shifted fluorescence, making them susceptible to autofluorescence background, weak signal, and irreversible photodegradation under prolonged excitation. In contrast, single lanthanide-doped upconversion nanocrystals are effectively background-free anti-Stokes emitters and demonstrate robust resistance to photodegradation, yet they are generally considered non-blinking owing to the presence of a large ensemble of uncorrelated emitting lanthanide ions within a single nanocrystal. Here we report the discovery and control of collective blinking in the upconversion luminescence of thousands of lanthanide ions within a single nanocrystal. The blinking exhibits on-off intensity ratio exceeding 10, persists for over 15 hours (over 10,000 cycles) without discernible photodegradation, and can be reversibly controlled by adjusting the excitation power. We elucidate a universal, activator-independent upconversion blinking mechanism, whereby a single quencher, stochastically generated via a cooperative multi-ion process, can intercept delocalized excitation energy within the Yb3+ sensitizer network and darken the whole nanocrystal. Benefiting from the high-contrast, long-term photostable blinking and background-free emission, we achieve robust super-resolution localization microscopy that resolves individual nanocrystals in aggregates with 1.2 nm precision. This work establishes a general strategy to realize and control collective blinking in photostable multi-emitter nanosystems, opening new opportunities in nanoscience, bioimaging, and
quantum technologies.
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All authors Tianzi Ma, Dingxin Huang, Yongjun Meng, Jianwei Tang, Feng Li, Yihao Yu, Xiaorong Zhang, Guanying Chen, Xue-Wen Chen
Accelerator Physics
Booster-based beam recycling for swap-out injection at the High Energy Photon Source
Zhe Duan, Jinhui Chen, Yaoyao Du et al. (+23 more)
Fourth-generation synchrotron light sources employ ultralow-emittance storage rings with stringent injection requirements. On-axis swap-out injection alleviates the dependence on storage-ring dynamic aperture, but high-charge operation requires an efficient injector architecture capable of producing high-charge replacement bunches. This paper presents the accelerator physics design and performance analysis of a booster-based beam-recycling swap-out injection scheme implemented at the High Energy
Photon Source (HEPS). In this approach, the full-energy booster serves as both an injector and a high-energy accumulator. An extracted storage-ring bunch is returned to the booster, merged with a low-charge bunch previously injected from the linac and accelerated to full energy. Following high-energy damping, the merged bunch is reinjected into the original storage-ring bucket. The scheme avoids the need for a dedicated accumulator ring while enabling high-charge bunch replacement. The recycling scheme was commissioned through staged machine studies. Full recycling-chain simulations, commissioning studies, and measured performance analysis are presented. The measured results characterize the recycling operation and quantify the transmission efficiency and performance limitations of the complete recycling loop. These results demonstrate the feasibility of the booster-based beam-recycling architecture and establish its operational basis for high-charge swap-out injection in future fourth-generation synchrotron light sources.
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All authors Zhe Duan, Jinhui Chen, Yaoyao Du, Yuanyuan Guo, Jun He, Xiyang Huang, Daheng Jia, Jingyi Li, Fang Liu, Peng Liu, Zhi Liu, Xiaohan Lu, Yanhua Lu, Cai Meng, Yuemei Peng, Saike Tian, Guanwen Wang, Jiuqing Wang, Na Wang, Yuanyuan Wei, Gang Xu, Haisheng Xu, Yaliang Zhao, Ying Zhao, Yi Jiao, Weimin Pan
Chemical Physics
Nonadiabatic Dynamics near Multiple Light-Induced Conical Intersections under Bichromatic Driving: Quantum Wave-Packet Dynamics versus Floquet Surface Hopping
Jiayue Han, Feng An and Wenjie Dou
Light-induced conical intersections (LICIs) create externally tunable pathways for nonadiabatic transitions, enabling active control of molecular photophysical and photochemical processes. However, both the dynamics near multiple LICIs under bichromatic driving and the applicability of our recently developed two-mode Floquet fewest switches surface hopping (two-mode F-FSSH) method to this regime remain insufficiently understood. Here, we construct a minimal three-channel Floquet
Hamiltonian supporting two LICIs for Na2 interacting with a bichromatic field. We characterize its static Floquet properties and investigate the associated nonadiabatic dynamics using numerically exact
quantum wave-packet dynamics and two-mode F-FSSH. We find that the second
photon energy controls the relative positions of the LICIs, whereas the second-field intensity primarily redistributes and broadens the derivative-coupling landscape around the second LICI. Consequently, electronic population transfer and molecular
alignment exhibit distinct and often nonmonotonic responses to these two control parameters. Two-mode F-FSSH reliably captures the principal features of the early-time dynamics and provides a semiquantitative description of the post-transient time-averaged observables. These findings advance our understanding of LICI-mediated dynamics at both the physical and methodological levels.
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Computational Physics · Cross-listed from cond-mat.stat-mech
Rare-event sampling for stochastic dynamics in network systems using cluster updates
Jiazheng Sun, James Moody and Thomas Barthel
Understanding the
stochastic evolution in complex networks is a central challenge across physics, biology, engineering, social science, and finance. The most consequential macroscopic events, like cascading failures in communication networks, widespread epidemic outbreaks, and rapid shifts in societal opinions, often emerge from a confluence of rare, localized
stochastic processes and need to pass certain bottlenecks. Standard forward-time simulation
algorithms like the Gillespie method are inefficient for the investigation of such phenomena due to catastrophic rejection rates. Advanced rare-event techniques like splitting methods and transition-path sampling often suffer from kinetic trapping, path degeneracy, genealogical correlations, or critical slowing down when applied to complex heterogeneous networks. We propose to overcome this challenge by establishing a novel technique called conditional-path Monte Carlo (CPMC), inspired by loop
algorithms from
equilibrium condensed-matter physics. By employing non-local updates on spacetime clusters without rejections, CPMC generates a Markov chain of trajectories that all strictly respect the targeted macroscopic boundary conditions like the occurrence of a massive network failure. We demonstrate the framework's potential by performing a simple risk factor analysis for rare large-scale epidemic outbreaks in SIS dynamics on kinship networks.
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Optics · Cross-listed from math.NA
Asymptotics-guided learning and symbolic regression for dispersive resonances
Konstantinos Alexopoulos and Josselin Garnier
We study resonance prediction in dispersive media, formulated as nonlinear spectral problems for volume integral operators. The main idea is to use asymptotic analysis not only as a baseline approximation, but also as a guide for constructing predictive correction models. We learn the residual between asymptotic and reference resonances using features suggested by the subwavelength expansion, including the logarithmic scales specific to two dimensions. The resulting corrections substantially improve single-resonator and dimer predictions, and symbolic regression produces compact formulas for the learned residual. The results show that asymptotic analysis can be used not only to approximate resonances, but also to design the feature space in which data-driven corrections become accurate, low-dimensional, and interpretable.
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Chemical Physics
Highly Efficient 3-in-1 FPGA-Based Unit Combining an ADC, DAC, and Multichannel Pulse Generator for Pulsed EPR Spectroscopy
N. P. Isaev, A. R. Melnikov, A. V. Vedkal et al. (+4 more)
A hardware and software system for pulsed electron paramagnetic resonance (EPR) spectroscopy is presented, built around a single PCI Express (PCIe) card that combines a 16-bit digital-to-analog converter (DAC) with a 10 GS/s effective sample rate, a 2.5 GS/s 14-bit analog-to-digital converter (ADC), and an 11-channel pulse generator on board. Custom firmware reloads the pulse-generator and DAC buffers and streams the digitized data in parallel with a running experiment, so that no time is lost on data input or output. The unit is integrated into the open-source Atomize software and controlled at the level of pulse sequences through a high-level Python application programming interface (API). Benchmarking with a four-pulse double electron-electron resonance (DEER) sequence shows that the card reaches close to 100% time efficiency for accumulation times of a few milliseconds per pulse sequence, even for demanding arbitrary waveform generator (AWG) protocols. Although demonstrated for a single sequence, the efficiency proved insensitive to the number of pulses, the number of measurement points, or the phase-cycling depth, so that comparable performance is expected for any protocol whose shot repetition time exceeds the ~2 ms reprogramming time of the card. The on-the-fly reprogramming, which makes every successive sequence fully independent, also enables nonuniform sampling and acquisition. The system design, its firmware, the Python API, and its performance in pulsed EPR applications are described.
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All authors N. P. Isaev, A. R. Melnikov, A. V. Vedkal, A. S. Ishchenko, A. M. Sokolov, E. A. Voronkov, S. L. Veber
Fluid Dynamics
Pattern Formation in Bioconvection of Thiovulum in a Hele-Shaw Chamber
Trey Johnson, George A. Schaible, Yujia Qi et al. (+4 more)
This paper is associated with a
video winner of a 2025 American Physical Society's Division of Fluid Dynamics (DFD) Gallery of Fluid Motion Award for work presented at the DFD Gallery of Fluid Motion. The original
video is available online at the Gallery of Fluid Motion, https://doi.org/10.1103/APS.DFD.2025.GFM.V045. We investigate bioconvection in a colony of Thiovulum sp. ST bacteria, a recently isolated enrichment culture, confined within a Hele-Shaw chamber. Driven by chemotactic and gravitactic responses, the
cells collectively develop striking emergent patterns and convection-like dynamics. Starting from a dense, homogeneous suspension, the swimming bacteria generate large-scale bioconvective flows within minutes. Although these flows resemble thermal convection, they arise in the absence of an imposed temperature gradient. Instead, they arise from the collective swimming of bacteria responding to oxygen gradients and
gravity.
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All authors Trey Johnson, George A. Schaible, Yujia Qi, Fridtjof Brauns, Alexander Cohen, Jean-Marie Volland, Ousmane Kodio
Optics
Nanocavity Confinement by Orthogonal Valley- and SSH- Topological Interfaces In Glide-Symmetric Photonic Crystal Structures
Takahiro Uemura, Wei Dai, Yuto Moritake et al. (+3 more)
Valley photonic
crystals enable valley-dependent transport and chirality-selective emission, but incorporating wavelength-scale localization remains challenging. Existing valley-photonic-
crystal cavities rely on finite defects or local
lattice modifications that require structure-specific optimization and offer limited continuous control. Here, we theoretically and experimentally demonstrate two-dimensional nanocavity confinement using two orthogonal domain walls in a glide-symmetric valley photonic
crystal. A valley domain wall confines the guided interface mode transversely, while an SSH-like domain wall localizes it longitudinally. Starting from a glide-
symmetry-protected Dirac point in a bearded-interface waveguide, controlled displacements of adjacent triangular holes open a topological gap in the continuous guided-mode dispersion. The displacement amplitude $\Delta R$ tunes the gap, mode volume, and intrinsic radiative $Q$ factor. Implemented in a silicon photonic-
crystal slab, the structure exhibits localized resonances within the topological mode gap and systematic spectral tuning with $\Delta R$. The maximum measured loaded $Q$ factor is $1.2\times10^{4}$. This approach enables continuously tunable, high-$Q$ nanocavities integrated into topological waveguide networks for compact resonant devices and enhanced light–matter interactions.
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All authors Takahiro Uemura, Wei Dai, Yuto Moritake, Masaaki Ono, Eiichi Kuramochi, Masaya Notomi
Optics
Hyperbolic Metamaterials for Enhanced Scintillation
Priyankar Pandey, Subrahmanyam Mantha and Harish N S Krishnamoorthy
Scintillators are indispensable for the detection of X-rays, $\gamma$-rays, and energetic particles in applications ranging from medical imaging and security screening to high-energy physics. A major limitation of conventional scintillators is the inefficient extraction of scintillation
photons caused by isotropic emission and total internal reflection at the scintillator–detector interface. Here, we propose and numerically investigate a grating-coupled
hyperbolic metamaterial (HMM) in which the scintillation medium CsPbBr$_3$ forms one of the constituent layers. The proposed architecture simultaneously enhances spontaneous emission through broadband Purcell enhancement enabled by high-$k$ modes and improves optical extraction by directing the emitted radiation along the
hyperbolic resonance cone, with a bullseye grating providing efficient far-field outcoupling. Finite-difference time-domain simulations predict broadband enhancement of the detected scintillation signal exceeding threefold across the CsPbBr$_3$ emission band, with a peak enhancement of approximately fivefold around the emission wavelength of 523 nm compared with an equivalent bulk scintillator. The proposed multilayer architecture is compatible with established thin-film deposition and nanofabrication techniques, robust against fabrication-induced layer-thickness variations, and scalable to accommodate larger volumes of scintillating material, providing a practical route toward high-performance scintillators for radiation detection and imaging applications.
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Computational Physics · Cross-listed from nlin.CD
When More Data Become Less Informative: Finite-Precision Periodicization and Collapse of Forecast-Error Lyapunov Estimates
Andrei Velichko and Viet-Thanh Pham
Largest Lyapunov exponents (LLEs) quantify exponential sensitivity, but data-driven estimates are often obtained from finite-precision trajectories. We show that increasing the length of a single reduced-precision chaotic record can eventually degrade a forecast-error LLE estimate. Using the logistic map at r=4, an ESP32 single-precision trajectory is reproduced bit-for-bit by NumPy float32. Across 10,000 random float32 initial conditions, every trajectory reaches an exact recurrence before iteration 7612. For one long float32 record, the estimated LLE changes from 0.6853 at N=15,000 to 0.1827 at N=20,000 and approximately zero at N=30,000 as exact train-test histories saturate. At N=100,000, the long float32 record gives 0.0016, whereas independently restarted length-100 trajectories give 0.6917; matched float64 controls remain near ln(2)=0.6931. The collapse is reproduced for 28 representative initial conditions, and its onset is strongly correlated with the recurrence scale set by transient length and digital period (Pearson r=0.982). Thus, finite-state recurrence can turn additional samples into duplicate futures rather than new dynamical information, while independent restarts substantially delay this saturation.
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Optics
Topologically Configurable Nonlinear Vortex Generation at van der Waals Heterostructures
Hongwei Wang, Yuda Wan, Kai Wang et al. (+8 more)
van der Waals (vdW) materials offer a highly tunable and efficient platform at nanoscale for nonlinear and
quantum optics. Twist-stacked vdW
heterostructures enable elegant control of
symmetry and interlayer coupling. Prior studies mainly focus on planar twisted interfaces, while neglecting the naturally formed and mandatory defects in such vdW
heterostructures. Here, we demonstrate nonlinear singular optics with topologically configurable nonlinear
vortex generation at the corner singularity of vdW
heterostructures. By tailoring azimuthally discrete second-harmonic phase gradients at each interface, we obtain programmable nonlinear
vortex emitters with dominant target OAM components. Nonlinear OAM beams with topological charge $\ell = 1$ and $\ell = -2$ are experimentally realized, respectively. Our work unlocks the untapped potentials of nonlinear singular optics in twisted vdW materials as a reconfigurable and lithography-free platform for nonlinear structured light generation, important in
quantum nonlinear optics and related fields.
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All authors Hongwei Wang, Yuda Wan, Kai Wang, Shuzheng Chen, Hao Yan, Xu Jiang, Xiaodan Lyu, Chang-Yin Ji, Weibo Gao, Peixiang Lu, Guangwei Hu
Computational Physics · Cross-listed from cs.AI
Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics
Conrad Ainslie, Pedram Hassanzadeh, Michael W. Mahoney et al. (+1 more)
Neural autoregressive models have rapidly emerged as powerful emulators of high-dimensional chaotic systems, yet their long-term instability and error growth remain poorly understood, leading to ad-hoc solutions. Here, we develop an eigenanalysis framework that reveals the dynamical origin of this error growth. By analyzing the Jacobian of the learned one-step update map with respect to the state, we show how inference-time error growth, and thus model stability, is governed by its spectral radius. Direct-step architectures (models that predict the next state from the previous one) generically admit unstable
eigenvalues with magnitudes exceeding one, explaining the rapid divergence of these widely used models. In contrast, integration-constrained models (where the time derivative is estimated and integrated with a higher-order integrator) collapse their eigenspectrum onto the unit circle, yielding neutral stability and a universal linear error-scaling law. The largest
eigenvalue of this Jacobian provides an architecture-agnostic, a priori diagnostic of short-term skill, long-term stability, and spectral bias, without requiring an expensive rollout. Leveraging this theory, we introduce a stability-promoting loss that explicitly regularizes Jacobian-driven error amplification, improving both forecast accuracy and dynamical robustness. Demonstrated across $29$ models spanning two architectures, several explicit and implicit integrators, and multiple loss functions on the Kuramoto-Sivashinsky system, our results establish a theoretical foundation for the design and evaluation of neural emulators of chaotic multi-scale dynamics. More broadly, our framework is a step toward the kind of a priori stability analysis that numerical analysis provides for discretizations of differential equations and that scientific machine learning currently lacks.
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All authors Conrad Ainslie, Pedram Hassanzadeh, Michael W. Mahoney, Ashesh Chattopadhyay
Data Analysis and Statistics · Cross-listed from cs.LG
DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design
Xinling Yu, Yixing Li, Ziyue Liu et al. (+4 more)
Thermal-aware optimization of multi-die 3D integrated
circuits evaluates many designs, each a costly heat-equation solve. Operator-learning surrogates replace this solve with a fast forward pass, ideally trained from physics alone, without labeled data. DeepOHeat-v1 made such surrogates fast and trustworthy, but only on low-contrast geometries. High-contrast multi-die stacks break it in two ways: discontinuous conductivities make the continuous physics loss ill-defined at material interfaces, and ill-conditioning ($\kappa_2(A_h) \approx 6 \times 10^4$) puts the discretized strong-form loss beyond first-order optimization. We propose DeepOHeat-v2 to overcome both. First, we train on a discretized physics loss that handles the discontinuities natively; its energy form reduces the prediction-space loss-Hessian conditioning from $\kappa^2$ to $\kappa$, and a
matrix-preconditioned optimizer cuts the mean peak temperature error from over 30 K to 0.55 K. Second, because optimization leaves the training distribution, we propose a self-improving framework: a hotspot trust gate sends flagged placements to a reference solver, and the surrogate incrementally retrains on the refined solutions, keeping an update only when it improves held-out validation error. On a multi-die
benchmark, the surrogate-true peak gap on the returned design falls from 1.12 K to 0.11 K, matching a solve-at-every-step optimizer while running $56\times$ faster.
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All authors Xinling Yu, Yixing Li, Ziyue Liu, Xin Ai, Zhiyu Zeng, Hai Li, Zheng Zhang
Fluid Dynamics
Measuring Mean Spanwise Flow with a Rotating Single Hot-Wire Probe over a Wide Yawed Riblet-Textured Surface
Yu Xia, Sandesh Parajuli, Hafiz Aliffrananda et al. (+2 more)
This study investigates the spatial development and recovery of near-wall flow steering induced by yawed widely-spaced riblets. To accurately measure the mean spanwise velocity in the near-wall region, we developed a unique, customised rotating probe equipped with a single hot-wire. This novel technique demonstrates high sensitivity, reliably detecting small spanwise velocity components. We applied this probe to map the flow over straight-to-yawed and yawed-to-straight riblet configurations. For the straight-to-yawed transition, the downstream distribution of the mean spanwise velocity closely aligns with the step-forced spatial Stokes layer (SSL) solution, achieving an equivalent active wall motion of $V^+_\mathrm{equ} \approx 2.0$. This offers a simplified analytical pathway for future passive flow manipulation studies. Conversely, flow recovery over the yawed-to-straight configuration diverges from the SSL step-down phase prediction. Instead, a spatial lag was observed where crest-adjacent flow recovered quickly, but significant residual spanwise flow ($V^+ > 0.5$) persisted far downstream ($\widehat{x}{}^{+} > 1000$).
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All authors Yu Xia, Sandesh Parajuli, Hafiz Aliffrananda, Daniel Chung, Nicholas Hutchins
Optics
In-situ adjoint protocols for nonlinear PT-symmetric self-optimizing machines
Zheming Li, Lucas J. Fernández-Alcázar, Zin Lin et al. (+1 more)
Adjoint methods provide a powerful route for gradient-based optimization, but their physical implementation is obstructed in generic nonlinear systems because the adjoint dynamics requires backward-time
evolution, Jacobian transposition, and terminal-value constraints. Here we show that nonlinear parity-time ($\mathcal{PT}$)-symmetric systems overcome this obstruction. Using a class of nonlinear non-Hermitian resonator networks, we establish
symmetry relations that map the formal adjoint dynamics onto experimentally accessible forward-time evolutions supplemented by controlled injections. This construction enables exact in-situ evaluation of adjoint gradients without requiring explicit backward propagation or
matrix transposition. We demonstrate the approach in nonlinear $\mathcal{PT}$-symmetric resonator chains, where the resulting optimization protocol autonomously discovers parameter configurations that realize prescribed spatio-temporal functionalities, including uniform energy redistribution and targeted wave transport at predefined time windows. Our results identify $\mathcal{PT}$
symmetry as a resource for implementing computational sensitivities within physical systems and establish a route toward self-optimizing nonlinear machines.
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All authors Zheming Li, Lucas J. Fernández-Alcázar, Zin Lin, Tsampikos Kottos
Computational Physics · Cross-listed from nucl-th
Deep-learning classification of physically admissible nuclear-matter equations of state
Ahmed Abuali, Micheal Kahangirwe, Francesco Di Clemente et al. (+3 more)
Thermodynamic stability and causality impose fundamental constraints on the equation of state (EoS) of
nuclear matter. Verifying these constraints conventionally requires calculating quantities such as the specific heat,
baryon-number susceptibility, and speed of sound, which can become computationally expensive when many candidate EoSs must be examined. We investigate whether the normalized pressure surface, $Q(T,\mu_B)=P(T,\mu_B)/T^4$, alone contains sufficient information to determine the physical admissibility of an EoS. We develop a supervised convolutional neural network (CNN) that uses only this pressure representation to classify EoSs as physically admissible or inadmissible. The network is provided with training labels obtained from direct thermodynmaic stability and causality check and its does not get any information about the parameters of the underlying EoS framework. For EoSs generated within an Ising-mapping framework, the model achieves $97.65%$ accuracy on unseen test data. Applied independently to EoSs from a distinct
holographic framework, it achieves perfect classification of the test set. These results show that pressure surfaces contain
geometric signatures of thermodynamic stability and causality violations that can be learned directly by a CNN. Because the classifier relies only on the pressure surface, it avoids evaluating higher-order thermodynamic observables during inference and is largely independent of the EoS-generation framework. When the pressure surface is supplied as a two-dimensional array, the machine-learning validation is approximately 20 times faster than direct validation. Our results establish a fast, framework-independent approach for identifying physically admissible EoSs directly from their pressure surfaces.
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All authors Ahmed Abuali, Micheal Kahangirwe, Francesco Di Clemente, Vianney E Diaz-Barraza, Jorge A Munoz, Claudia Ratti