Showing 1–50 of 543 results for author: Li, T

Universal Machine-learning Molecular Dynamics at the Speed of Empirical Potentials

Authors: Tiancheng Li, Jianming Xue, Linfeng Zhang, Duo Zhang, Han Wang

Abstract: No interatomic potential has offered universality across chemistry, near-first-principles accuracy and the speed of empirical potentials at once. Here we introduce DPA4C, an equivariant potential whose architecture and compressed CUDA operators are co-designed under deployment constraints to pursue accuracy and efficiency together. Five variants spanning a 49-fold parameter range form the high-thr… ▽ More No interatomic potential has offered universality across chemistry, near-first-principles accuracy and the speed of empirical potentials at once. Here we introduce DPA4C, an equivariant potential whose architecture and compressed CUDA operators are co-designed under deployment constraints to pursue accuracy and efficiency together. Five variants spanning a 49-fold parameter range form the high-throughput end of the measured accuracy--throughput frontier. The largest variant approaches the accuracy of the MACE-Omat models at about two orders of magnitude higher measured throughput. The most compact reduces the energy, force and stress errors of the fastest existing universal MLIP by 61.4%, 48.1% and 34.3% at 1.92 times its saturated throughput. All five variants complete multimillion-atom simulations on a single GPU and run molecular dynamics for 2.048 billion atoms on 1,024 16-GB NVIDIA V100 GPUs at 83.3--91.2% weak-scaling efficiency. Compared with the MEAM empirical potential, DPA4C-Nano reaches 1.8 and 2.5 times the saturated throughput in single-GPU scans on the same V100 hardware for diamond carbon and FCC copper, respectively. DPA4C therefore brings quantum-trained universal accuracy into a regime of speed and system size previously associated with empirical potentials. △ Less

Submitted 19 August, 2026; v1 submitted 19 August, 2026; originally announced August 2026.

Stochastic Liouville-transport theory of light-atom interaction noise in thermal atomic vapors

Authors: Shaoxin Yuan, Bin Wu, Mingyong Jing, Chaoyang Hu, Yan Peng, Tingting Li, Xingya Li, Wenguang Yang, Junyao Xie, Zongkai Liu, Hao Zhang, Linjie Zhang, Liantuan Xiao, Suotang Jia

Abstract: Atom-light interaction noise can limit thermal-vapor sensing. Existing theories often treat internal-state dynamics, finite-mode atomic motion, and stochastic renewal separately, obscuring their coupled contributions to measured noise. We develop a general stochastic Liouville-transport theory, tested against polarization-resolved resonant Cs D$_2$ spectra. Joint experiment-theory analysis identif… ▽ More Atom-light interaction noise can limit thermal-vapor sensing. Existing theories often treat internal-state dynamics, finite-mode atomic motion, and stochastic renewal separately, obscuring their coupled contributions to measured noise. We develop a general stochastic Liouville-transport theory, tested against polarization-resolved resonant Cs D$_2$ spectra. Joint experiment-theory analysis identifies atom-light noise below approximately 100 kHz as transit-dominated. Ballistic motion through the finite Gaussian mode modulates both the coupling-weighted effective atom number and trajectory-dependent Rabi coupling, producing predominantly common-mode noise. Boundary renewal introduces atoms with independently sampled ground-state sublevels, generating differential population fluctuations with opposite effects on the circular channels. Under an applied longitudinal magnetic field, experiment and theory show the same qualitative nonmonotonic change in common-mode suppression, supporting Zeeman redistribution of the channel responses. The framework can analyze noise in other thermal-atom sensors, including Rydberg-atom electric-field measurements. △ Less

Submitted 15 August, 2026; originally announced August 2026.

A 3-D Hybrid Numerical Method for Simulating Electromagnetic Fields in Structures with Multiple Inhomogeneous Layered Media

Authors: Jie Liu, Taihe Li, Ke Chen, Mingwei Zhuang, Qing Huo Liu

Abstract: A three-dimensional (3-D) hybrid numerical method (HNM) is presented for electromagnetic scattering in structures with multiple inhomogeneous layered media coupled to an arbitrary 3-D non-layered scattering region. It integrates the 3-D numerical mode-matching (NMM) method with a tree-cotree-based mixed finite element method (MFEM). In the NMM, the fields in the 3-D layered media are reduced to a… ▽ More A three-dimensional (3-D) hybrid numerical method (HNM) is presented for electromagnetic scattering in structures with multiple inhomogeneous layered media coupled to an arbitrary 3-D non-layered scattering region. It integrates the 3-D numerical mode-matching (NMM) method with a tree-cotree-based mixed finite element method (MFEM). In the NMM, the fields in the 3-D layered media are reduced to a superposition of 2-D waveguide eigenmodes, while the MFEM discretizes the 3-D scattering region. The HNM thus inherits the dimensionality-reduction advantages of both conventional hybrid MM/FEM and pure NMM. Numerical experiments show that the HNM provides an effcient alternative for scattering problems involving non-layered regions embedded in layered media. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: 21pages,12 figures

High resolution meta-stereomicroscope based on birefringent meta-optics

Authors: Liheng Yan, Haowen Liang, Emiliano R. Martins, Yikun Liu, Tao Li, Thomas F. Krauss, Juntao Li, Xue-Hua Wang

Abstract: Achieving both high lateral and high depth resolution is a longstanding goal in stereomicroscopy. Although meta-optics have revolutionized lens design by alleviating the physical constraints of conventional optical architectures, existing metalens-assisted stereomicroscopes still suffer from field of view (FOV) mismatch between meta-optical and conventional optical components in stereomicroscopes,… ▽ More Achieving both high lateral and high depth resolution is a longstanding goal in stereomicroscopy. Although meta-optics have revolutionized lens design by alleviating the physical constraints of conventional optical architectures, existing metalens-assisted stereomicroscopes still suffer from field of view (FOV) mismatch between meta-optical and conventional optical components in stereomicroscopes, thereby limiting their imaging performance. Here, we show that this mismatch can be fundamentally eliminated through a fully meta-optical architecture. The integrated system enables flexible control of numerical aperture and magnification with larger depth of field (DOF) and FOV than those of the metalens-assisted stereomicroscopes. Experimentally, we achieve an integrated meta-stereomicroscope with a lateral resolution of 435 nm, surpassing the performance of previously reported stereomicroscopes. Empowered by a stereo neural network, the system enables straightforward reconstruction of high-resolution three-dimensional surface morphology with a depth resolution of 1026 nm, demonstrating the capability to simultaneously achieve high lateral and depth resolution imaging. This integrated architecture operates in both transmission and reflection modes for biomedical imaging and industrial inspection, highlighting its broad applicability for real-time observation across biomedical and industrial scenarios. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: 14 pages, 5 figures

Performance appraisal promotes cooperation in spatial public goods games

Authors: Tianjiao Li, Qin Li, Kangxi Zhu, Minyu Feng, Manuel Chica

Abstract: In real organizations, performance appraisal serves as an important means of evaluating employees' work outcomes and behaviors, while performance pay directly links compensation to the evaluation results. However, in traditional spatial public goods games, the total payoff of a group is equally distributed among all participants without considering individual differences in contributions, an appro… ▽ More In real organizations, performance appraisal serves as an important means of evaluating employees' work outcomes and behaviors, while performance pay directly links compensation to the evaluation results. However, in traditional spatial public goods games, the total payoff of a group is equally distributed among all participants without considering individual differences in contributions, an approach that ignores the heterogeneity of individual efforts. To overcome this limitation, we propose a spatial public goods game model based on performance appraisal, in which individual payoffs are divided into two parts: an equally distributed component and a performance-weighted component. Specifically, each individual receives scores from its neighboring groups, and the individual's reputation is dynamically updated by accumulating these scores, which further modulates the fitness function during strategy imitation. Extensive numerical simulation results demonstrate that the performance appraisal mechanism significantly promotes the emergence of cooperative behavior. The reputation reinforcement mechanism amplifies this positive effect by creating fitness advantages for high-reputation individuals. These findings suggest that incorporating performance appraisal into payoff allocation helps mitigate social dilemmas and promote collective cooperation. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Generalized Temporal Coupled Mode Theory (g-TCMT) applied to Coupled Resonator Optical Waveguides with Exchange Symmetry (CROWe)

Authors: Tianrui Li, Matthew P. Halsall, Iain F. Crowe

Abstract: In this paper, we extend our generalized Temporal Coupled Mode Theory (g-TCMT) model, developed earlier [1], from a simple, dual-coupled micro-ring resonator (MRR) system to higher-order, n-serially coupled MRRs. By treating each pair of adjacent MRRs as a single `equivalent resonator', we demonstrate excellent agreement in spectral resonance position, between the g-TCMT and numerical results obta… ▽ More In this paper, we extend our generalized Temporal Coupled Mode Theory (g-TCMT) model, developed earlier [1], from a simple, dual-coupled micro-ring resonator (MRR) system to higher-order, n-serially coupled MRRs. By treating each pair of adjacent MRRs as a single `equivalent resonator', we demonstrate excellent agreement in spectral resonance position, between the g-TCMT and numerical results obtained using the transfer matrix method (TMM), for systems up to and including order n=4. The validity of this approach hinges on the existence, or otherwise, of exchange symmetry, and so we refer to these structures as Coupled Resonator Optical Waveguides with exchange symmetry (CROWe's). We explore the limitations of our approach with illustrative examples of strongly coupled systems of order n \geq 5. Finally, we explore the properties of such higher order CROWe's for applications in non-Hermitian photonics, i.e., in which gain in some of the component MRRs and loss in the others leads to Parity-Time (PT) symmetry. △ Less

Submitted 3 August, 2026; originally announced August 2026.

Comments: 9 pages, 11 figures

Nonlinear Fourier spectral signatures of rogue waves observed in Bose-Einstein condensates

Authors: Zhihao Zhang, Yankai Huang, Tiantian Li, Denglong Wang, Jie Peng

Abstract: Modulation instability provides an important framework for understanding rogue wave (RW) formation on continuous backgrounds. However, the formation mechanism and nonlinear spectral structures of RWs in Bose-Einstein condensate (BEC) matter-wave systems with vanishing boundary conditions remain largely unexplored. Here, we employ the nonlinear Fourier transform (NFT), based on the integrable struc… ▽ More Modulation instability provides an important framework for understanding rogue wave (RW) formation on continuous backgrounds. However, the formation mechanism and nonlinear spectral structures of RWs in Bose-Einstein condensate (BEC) matter-wave systems with vanishing boundary conditions remain largely unexplored. Here, we employ the nonlinear Fourier transform (NFT), based on the integrable structure of the focusing nonlinear Schrödinger equation and the Zakharov-Shabat scattering problem, to investigate two representative classes of first-order RWs in BEC systems. Through nonlinear spectral analysis and Darboux reconstruction, we demonstrate that both Gaussian-wave-packet-induced extreme localization events and experimentally observed Peregrine solitons are governed by the coherent dynamics of discrete soliton modes encoded in the nonlinear spectrum. For Gaussian initial states, increasing the initial width leads to an increasing number of discrete eigenvalues, resulting in a transition from fundamental solitons and bound states to Christmas-tree-like RW structures. For experimentally observed Peregrine solitons, localized perturbations reshape the discrete spectral configuration and phase evolution, enabling coherent focusing of multiple bound soliton modes. Furthermore, we reveal the spectral mechanism of higher-order RWs and propose an inverse spectral-engineering approach based on discrete-spectrum phase matching. Our results provide a nonlinear spectral perspective for understanding and controlling RW formation in matter-wave systems with vanishing boundary conditions. △ Less

Submitted 30 July, 2026; originally announced July 2026.

Geometric Superconducting Diode Effect in an NbN Nanoring

Authors: Tianyu Li, Peiyuan Huang, Jiong Li, Jiyao Shang, Nuo-Zhou Yang, Wuyue Xu, Wen-Cheng Yue, Yang-Yang Lyu, Chong Li, Yihuang Xiong, Xuecou Tu, Tao Tao, Xiaoqing Jia, Qing-Hu Chen, Huabing Wang, Peiheng Wu, Yong-Lei Wang

Abstract: Superconducting diodes, which exhibit nonreciprocal critical currents, are promising building blocks for low-power cryogenic electronics and superconducting circuits. Existing superconducting diode platforms commonly rely on Josephson junctions, multilayer heterostructures, ferromagnetic elements, gate-difined structures. Here, we demonstrate a geometrically induced superconducting diode effect re… ▽ More Superconducting diodes, which exhibit nonreciprocal critical currents, are promising building blocks for low-power cryogenic electronics and superconducting circuits. Existing superconducting diode platforms commonly rely on Josephson junctions, multilayer heterostructures, ferromagnetic elements, gate-difined structures. Here, we demonstrate a geometrically induced superconducting diode effect realized in a structurally minimal, single-materials NbN nanoring, where inversion-symmetry breaking is introduced solely by the asymmetric geometry. The device exhibits pronounced and polarity-switchable critical-current nonreciprocity. Systematic magnetic-field and temperature-dependent measurements reveal that, at low fields, the applied magnetic field redistributes the critical current asymmetrically between opposite bias directions without significantly reducing the overall superconducting current-carrying capability. Moreover, the maximal nonreciprocity and diode efficiency exhibit distinct temperature dependence: the maximal diode efficiency follows the evolution of the energy gap, whereas the maximal nonreciprocity is more closely associated with the superfluid density. These results establish asymmetric superconducting nanorings as a minimal geometric platform for studying nonreciprocal superconducting transport and provide a simple design principle for future superconducting electronics. △ Less

Submitted 22 July, 2026; originally announced July 2026.

Journal ref: Supercond. Sci. Technol. 39 (2026) 085013

Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields

Authors: Tianyu Li, Zhiwei Cao, Qingang Zhang, Ruihang Wang, Binyang Song, Yonggang Wen

Abstract: Neural surrogates are widely used in scientific machine learning for fast prediction of three-dimensional (3D) thermo-fluid fields. However, generating training data using conventional numerical solvers often incurs substantial computational and storage costs. We propose to train an attention graph neural network by minimizing the finite-volume method (FVM) residuals of the governing equations. Th… ▽ More Neural surrogates are widely used in scientific machine learning for fast prediction of three-dimensional (3D) thermo-fluid fields. However, generating training data using conventional numerical solvers often incurs substantial computational and storage costs. We propose to train an attention graph neural network by minimizing the finite-volume method (FVM) residuals of the governing equations. These residuals are evaluated directly on the mesh, requiring no labeled data. We evaluate the trained surrogates against computational fluid dynamics (CFD) references and a data-supervised baseline across four scenarios. On the two steady-state benchmarks, the FVM-loss model achieves an all-field normalized root-mean-square error (nRMSE) of 2.3-2.8%. It demonstrates close agreement with the CFD references, including the buoyancy-energy coupling. On the two parametric transient cases, the FVM-loss model outperforms the supervised baseline in terms of accuracy, while avoiding the data-generation cost entirely. These results indicate that the FVM loss can provide a practical training signal for neural surrogates and reduce the model development cost. △ Less

Submitted 23 July, 2026; v1 submitted 22 July, 2026; originally announced July 2026.

Comments: 51 pages, 27 figures

Final assessment of radioactive impurities in the JUNO detector

Authors: Thomas Adam, Fengpeng An, Costas Andreopoulos, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, João Pedro Athayde Marcondes de André, Didier Auguste, Nikita Balashov, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Beretta, Antonio Bergnoli, Nikita Bessonov, Daniel Bick, Lukas Bieger, Svetlana Biktemerova, Thilo Birkenfeld, Simon Blyth, Manuel Böhles, Anastasia Bolshakova, Mathieu Bongrand, Matteo Borghesi , et al. (549 additional authors not shown)

Abstract: The Jiangmen Underground Neutrino Observatory (JUNO) collaboration has completed the construction of the 20,000-ton liquid scintillator detector and the associated muon veto detector system. To meet the physics objectives, the materials used in the detector must exhibit low radioactive contamination. The single-event rate in the fiducial volume (R $<$ 17.2 m) of the scintillator is required to be… ▽ More The Jiangmen Underground Neutrino Observatory (JUNO) collaboration has completed the construction of the 20,000-ton liquid scintillator detector and the associated muon veto detector system. To meet the physics objectives, the materials used in the detector must exhibit low radioactive contamination. The single-event rate in the fiducial volume (R $<$ 17.2 m) of the scintillator is required to be approximately 7 Hz for energies above 0.7 MeV, resulting in an accidental coincidence background of about 1 event per day for reactor neutrino physics analyses. Since the beginning of the construction phase, we have screened the natural radioactivity content of thousands of materials, to select those that meet the design background budget. The radioactive impurity concentrations of the materials ultimately used in the JUNO detector are summarized in this paper. The construction of the entire detector and the subsequent filling of the liquid scintillator were completed in August 2025. From the initial data, the total count rate of natural radioactivity within the detector's fiducial volume has met the requirements and is sufficient to support the reactor antineutrino analysis. △ Less

Submitted 19 July, 2026; originally announced July 2026.

A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries

Authors: Li Xiao, Tianyu Li, Yiye Zou, Mingjie Zhang, Xiaogangd Deng

Abstract: Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs. Deep neural networks have shown promise in improving simulation efficiency, especially graph neural networks (GNNs), which demonstrate great potential due to their flexibility with unstructur… ▽ More Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs. Deep neural networks have shown promise in improving simulation efficiency, especially graph neural networks (GNNs), which demonstrate great potential due to their flexibility with unstructured data. However, GNNs face challenges when dealing with tasks involving complex geometries and large-scale meshes. In this paper, we propose the Multi-scale Feature Enhanced Graph Neural Network (ME-GNN) to tackle these challenges. ME-GNN employs a graph neural network with a two-step message-passing mechanism to capture detailed local features effectively. Additionally, it integrates an Attention U-Net with uniform grid discretization, enabling the extraction of both fine and coarse features. The model also utilizes K-hop sampling to construct subgraphs, facilitating efficient training on large datasets while preserving detailed local features. We evaluated ME-GNN on three benchmark datasets and achieved state-of-the-art results: a relative L2 error of 0.0196 for the velocity field and 0.0556 for the surface pressure on ShapeNet-Car, a normalized mean squared error of 0.0033 for the flow field on AirfRANS, and a relative L2 error of 0.1416 for the surface pressure on DrivAerNet. △ Less

Submitted 13 July, 2026; originally announced July 2026.

Comments: 14 pages

Coexistence and manipulation of multiple singularities in a reconfigurable non-Hermitian metasurface

Authors: Xintong Shi, Yanjie Wu, Rui Zhou, Zuxing Lu, Tengyu Li, Tingting Liu, Hai Lin, Qiegen Liu, Shuyuan Xiao

Abstract: Non-Hermitian frameworks extend conventional Hermitian physics, offering a powerful paradigm for describing open systems. Central to this field are various singularities within the complex parameter space, such as exceptional points (EPs) and scattering zeros, which dictate exotic physical behaviors. As research shifts from isolated singularities toward multi-singularity interactions, conventional… ▽ More Non-Hermitian frameworks extend conventional Hermitian physics, offering a powerful paradigm for describing open systems. Central to this field are various singularities within the complex parameter space, such as exceptional points (EPs) and scattering zeros, which dictate exotic physical behaviors. As research shifts from isolated singularities toward multi-singularity interactions, conventional planar metasurfaces remain constrained by limited tuning dimensions. Here, we propose a mirror-coupled design that maps a metasurface into a quasi-high-dimensional parameter space. By employing a metallic plane to generate image resonators, this scheme multiplies the system degrees of freedom without increasing the number of physical resonators. Its implementation on a reconfigurable platform integrated with PIN diodes yields the coexistence and manipulation of an EP and multiple reflection zeros. Through simulations and microwave experiments, we characterize the dynamic evolution of these singularities and exploit their synergistic effects for two distinct applications. First, for tunable absorption, multiple reflection zeros are spectrally coordinated to achieve a near-perfect absorption band exceeding $99.9\%$ across the X-band, thereby dynamically suppressing target scattering. Second, for enhanced sensing, a reflection zero couples with the EP to form a hybrid singularity. This hybrid state inherits the power-law sensitivity of the EP while substantially boosting robustness against fluctuations, resolving the conventional trade-off between sensitivity and stability and simplifying detection to direct peak tracking rather than complex multimode eigenvalue fitting. Our work provides a general methodology to circumvent parameter competition among non-Hermitian singularities, opening new avenues for multifunctional metadevices across the electromagnetic spectrum. △ Less

Submitted 8 July, 2026; originally announced July 2026.

Hidden Gauge Freedom in Complex-Pole Hierarchical Equations of Motion

Authors: Tianchu Li, Andrés Montoya-Castillo

Abstract: While complex-pole hierarchical equations of motion (HEOM) have dramatically expanded the reach of numerically exact quantum dynamics simulations of open quantum systems, they suffer from numerical instabilities rooted in the non-Hermitian structure of their Liouvillian. Yet, the origin of this structure remains obscure. Here, we report a previously unknown gauge freedom in complex-pole HEOM: a co… ▽ More While complex-pole hierarchical equations of motion (HEOM) have dramatically expanded the reach of numerically exact quantum dynamics simulations of open quantum systems, they suffer from numerical instabilities rooted in the non-Hermitian structure of their Liouvillian. Yet, the origin of this structure remains obscure. Here, we report a previously unknown gauge freedom in complex-pole HEOM: a continuous family of analytically equivalent Liouvillians, all encoding the same bath correlation function, whose numerical properties vary dramatically. This gauge controls both the eigenspectrum and non-normality of the hierarchy generator, revealing spectral divergence and non-normal error amplification as two distinct instability mechanisms. By optimizing this gauge, we introduce GO--HEOM, which eliminates divergences in strongly coupled Brownian oscillator environments and extends numerically exact simulations of sub-Ohmic dynamics -- including through the delocalized-to-localized quantum phase transition -- to previously inaccessible coupling strengths. Because this gauge transformation is independent of the bath-correlation decomposition scheme, our GO--HEOM becomes a general, broadly compatible strategy for accessing numerically exact quantum dynamics of open quantum systems over arbitrary coupling and highly non-Markovian regimes. △ Less

Submitted 6 July, 2026; originally announced July 2026.

Gappy Reconstruction of Bubbly Flows by Guided Diffusion Models

Authors: Hridey Narula, Tianyi Li, Michele Buzzicotti, Luca Biferale, Prasad Perlekar

Abstract: Experiments in multiphase flows are often limited in their ability to simultaneously obtain velocity measurements in different phases. At the same time, flow reconstruction from phase-limited measurements is a challenging problem due to the substantially different velocity statistics across the phases. We address this problem for buoyancy-driven bubbly flows in the pseudo-turbulence regime by usin… ▽ More Experiments in multiphase flows are often limited in their ability to simultaneously obtain velocity measurements in different phases. At the same time, flow reconstruction from phase-limited measurements is a challenging problem due to the substantially different velocity statistics across the phases. We address this problem for buoyancy-driven bubbly flows in the pseudo-turbulence regime by using a guided diffusion model. We train the model using two-dimensional slices of the velocity field extracted from fully resolved three-dimensional direct numerical simulations. The model generates physically realistic velocity fields both unconditionally and when conditioned on the surrounding liquid flow. The reconstructed bubble-phase velocity field accurately reproduces key statistical features of the flow. We further show that a simple patching procedure for adjacent two-dimensional slices enables a reasonable reconstruction of the three-dimensional flow inside a bubble. These results establish the potential of diffusion models to serve as generative priors for three-dimensional turbulent multiphase flows, opening a route toward the reconstruction of unobserved or experimentally inaccessible velocity fields from sparse, partial, or phase-limited measurements. △ Less

Submitted 29 June, 2026; originally announced June 2026.

Comments: includes supplementary material, 12 pages main text, 5 pages supplementary material

Nonlinear Freezing of Vibrational Polariton Transport via Mesoscale Simulations

Authors: Xinwei Ji, Tao E. Li

Abstract: Two-dimensional real-space imaging of vibrational polariton transport in planar Fabry--Pérot microcavities is numerically simulated via the mesoscale cavity molecular dynamics approach, which self-consistently propagates $\sim\!2\times10^4$ realistic molecular simulation cells on a two-dimensional grid coupled to the same number of cavity modes. Beyond the well-known polariton ballistic-to-diffusi… ▽ More Two-dimensional real-space imaging of vibrational polariton transport in planar Fabry--Pérot microcavities is numerically simulated via the mesoscale cavity molecular dynamics approach, which self-consistently propagates $\sim\!2\times10^4$ realistic molecular simulation cells on a two-dimensional grid coupled to the same number of cavity modes. Beyond the well-known polariton ballistic-to-diffusive turnover in the linear response regime, these atomistic simulations reveal a nonlinear freezing mechanism of vibrational polariton transport, i.e., under strong pumping of the upper polariton, the initially ballistically propagating upper polariton completely freezes and localizes energy to molecules at specific locations. This mechanism originates from pump-induced breaking of the in-plane translation symmetry: significant molecular excitations at the pulse hot spot broaden the polariton density of states, thus funneling population to the $k_{\parallel}\rightarrow 0$ band edge with vanishing group velocities. △ Less

Submitted 25 June, 2026; originally announced June 2026.

An Iterative Dual-Channel Neural Quantum State Algorithm for Selected Configuration Interaction

Authors: Jen-Yu Chang, Yi-Chun Chang, Yu-Jui Lin, Ming-Chun Yang, Hsiu-Chi Tsai, Tai-Yue Li, Nan Yow Chen, Tsung-Wei Huang, En-Jui Kuo

Abstract: Accurately solving the electronic Schrödinger equation for strongly correlated systems remains a central challenge in quantum chemistry, where the exponential growth of configuration space limits the applicability of exact methods. Selected Configuration Interaction (SCI) algorithms address this challenge by adaptively constructing compact determinantal expansions, yet their efficiency depends cri… ▽ More Accurately solving the electronic Schrödinger equation for strongly correlated systems remains a central challenge in quantum chemistry, where the exponential growth of configuration space limits the applicability of exact methods. Selected Configuration Interaction (SCI) algorithms address this challenge by adaptively constructing compact determinantal expansions, yet their efficiency depends critically on the quality of the sampling strategy used to identify chemically important configurations. Here we introduce the Handover Iterative Neural Quantum State (HI-NQS) algorithm, which embeds a classically trained autoregressive Transformer neural quantum state within the iterative sample--diagonalize--update framework of Sample-Based Quantum Diagonalization. A dual-channel Transformer architecture with explicit spin-up/spin-down cross-attention encodes fermionic spin structure as an architectural inductive bias, enabling expressive and physically informed wavefunction representations. After each subspace diagonalization, the resulting eigenvector is distilled back into the network through a factorized spin-marginal teacher signal, establishing a closed feedback loop between generative sampling and exact diagonalization. Benchmarks across a range of small molecules and a systematic nitrogen active-space series demonstrate that HI-NQS achieves chemical accuracy on all systems tested, with determinant-count scaling substantially more favorable than conventional CIPSI-based SCI for all but the smallest active spaces. All calculations are performed on GPU hardware without quantum computing resources, establishing HI-NQS as an efficient and scalable purely classical approach to the selected configuration interaction problem. △ Less

Submitted 25 June, 2026; originally announced June 2026.

Stochastic Multiscale Reconstruction of Lagrangian Turbulence via Guided Diffusion Models

Authors: Conghui Wang, Tianyi Li, Luca Biferale, Qinmin Zheng, Michele Buzzicotti, Fabio Bonaccorso

Abstract: Lagrangian turbulence is characterized by intermittent, fat-tailed fluctuations and nontrivial correlations across temporal scales, making a quantitative description of its full multiscale probability distribution a longstanding challenge. A particularly important question is whether unresolved fine-scale fluctuations can be inferred from coarse-grained trajectory information. Here, we address thi… ▽ More Lagrangian turbulence is characterized by intermittent, fat-tailed fluctuations and nontrivial correlations across temporal scales, making a quantitative description of its full multiscale probability distribution a longstanding challenge. A particularly important question is whether unresolved fine-scale fluctuations can be inferred from coarse-grained trajectory information. Here, we address this problem by sampling the conditional distribution of unresolved fluctuations using a diffusion-model prior conditioned on large-scale dynamics obtained through a wavelet-based coarse-graining of Lagrangian trajectories. Using tracer trajectories from direct numerical simulations of homogeneous and isotropic turbulence at $Re_λ\simeq 310$, we show that the reconstructed signals recover scale-dependent intermittent statistics, including high-order structure functions, flatness, and local scaling exponents, together with cross-scale temporal correlations between resolved and unresolved fluctuations. The method also reproduces the broad stochastic variability of intermittent acceleration fluctuations conditioned on the same coarse-grained trajectory, whereas Gaussian-process reconstructions in wavelet representation suppress rare events. Our results show that small-scale Lagrangian intermittency can be modeled as a non-Gaussian conditional stochastic process constrained by coarse-scale dynamics and quantitatively reproduced through data-driven generative sampling. △ Less

Submitted 4 June, 2026; originally announced June 2026.

Comments: 4 figures; Supplemental Material and two supplementary movies included

Perturbative Deformation Mechanism of Metasurface

Authors: Tuo Li, Xin Liu, Lin Zhu, Lei Liang

Abstract: Metasurfaces enable precise manipulation of light-matter interactions, and meta-atom coupling and scaling dominates their resonant properties and functional responses. Conventionally, although theories such as CMT and CDT can explain many metasurface phenomena, a unified mechanism that reveals how the deformation of metasurface affect its performance remains lacking. Here, by combining transformat… ▽ More Metasurfaces enable precise manipulation of light-matter interactions, and meta-atom coupling and scaling dominates their resonant properties and functional responses. Conventionally, although theories such as CMT and CDT can explain many metasurface phenomena, a unified mechanism that reveals how the deformation of metasurface affect its performance remains lacking. Here, by combining transformation optics and spatial perturbation, we proposed a universal deformation mechanism of metasurface, which establishes a general physical picture. Based on this mechanism, we interpret the resonance frequency drift caused by coupling of the meta-atoms, clarify the tuning law of resonant frequency via geometric scaling of unit structures, and further demonstrate the anisotropic shift of grating resonant peak. Theoretical predictions show consistency with full-wave simulation results in all three scenarios. Given the broad applicability of the transformation optics and perturbation theory, the universal mechanism with intuitive physical picture should be widely existed in diverse fields including photonics crystals, Bragg fibers, two-dimensional materials and crystalline optical properties. △ Less

Submitted 15 June, 2026; v1 submitted 3 June, 2026; originally announced June 2026.

Comments: v2 revised version: revised the title to better reflect the mechanism, improved figure quality, adjusted the l case order, and added two case studies in supplementary materialscorrected grammatical errors and typos, updated references and citations

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution

Authors: Tiancheng Li, Wentao Li, Anyang Peng, Jianming Xue, Linfeng Zhang, Duo Zhang, Han Wang

Abstract: Machine-learning interatomic potentials now approach quantum-mechanical accuracy, but the most expressive equivariant architectures are costly to evaluate, and the leading ones depend on auxiliary denoising or direct-force pretraining. We introduce DPA4, an SE(3)-equivariant architecture spanning six size classes from 0.48 to 25 million parameters and reaching the accuracy of the strongest publish… ▽ More Machine-learning interatomic potentials now approach quantum-mechanical accuracy, but the most expressive equivariant architectures are costly to evaluate, and the leading ones depend on auxiliary denoising or direct-force pretraining. We introduce DPA4, an SE(3)-equivariant architecture spanning six size classes from 0.48 to 25 million parameters and reaching the accuracy of the strongest published models at several-fold to an order-of-magnitude higher inference throughput. Its convolution couples edge and node features across all angular degrees in an edge-local frame, and its Wigner bilinear nonlinearity is universal in its full form and, on an exact quadrature grid, equivariant to machine precision. On Matbench Discovery, DPA4 leads every ranked metric, and every variant evaluated lies on the accuracy--throughput Pareto frontier of the compliant leaderboard. DPA4-Pro attains the lowest energy error on OMat24 and lower total-energy and force errors than the strongest conservative baseline on the OMol25 composition-validation split. All variants are trained through the conservative energy-gradient path alone, made practical by a threefold-faster compiled implementation. DPA4 thus brings leaderboard-class accuracy within the routine compute budgets of molecular-dynamics and materials-screening workflows, for both inorganic crystals and organic molecules. △ Less

Submitted 20 August, 2026; v1 submitted 1 June, 2026; originally announced June 2026.

A scalable Ewald-free BIE framework for periodic Stokes flow via hierarchical proxy sums

Authors: Tianyue Li, Dhairya Malhotra, Shravan Veerapaneni

Abstract: Particulate Stokes flow in confined, periodic geometries underlies a broad class of problems in biophysics, microfluidics, and the rheology of complex fluids. Boundary integral equation (BIE) methods are a natural tool for such problems, but existing periodization schemes rely either on periodic Green's functions, which are restrictive for complex confining geometries, or on free-space schemes tha… ▽ More Particulate Stokes flow in confined, periodic geometries underlies a broad class of problems in biophysics, microfluidics, and the rheology of complex fluids. Boundary integral equation (BIE) methods are a natural tool for such problems, but existing periodization schemes rely either on periodic Green's functions, which are restrictive for complex confining geometries, or on free-space schemes that solve auxiliary proxy strengths alongside the surface densities in an extended linear system whose cost scales unfavorably in three dimensions. We present a BIE framework for three-dimensional particulate Stokes flow in periodic pipes with circular cross-sections, wall-bounded doubly-periodic, and triply-periodic geometries that uses only the free-space Green's function and avoids both Ewald summation and the extended linear system. Proxy sources placed on equivalent surfaces of the kernel-independent FMM (KIFMM) form the auxiliary basis, and contributions from far image boxes are captured by a hierarchical proxy sum made absolutely convergent by a net-force-zero compatibility condition. The resulting periodization precomputation depends only on the periodic-box geometry, independent of the kernel and of the surfaces inside the box, and is reused verbatim across the Stokeslet, stresslet, and rotlet. Combined with high-order adaptive surface discretizations, the method achieves high-order accuracy at $\mathcal{O}(N)$ cost with a single layer of image boxes in the near field. Numerical examples on dense polydisperse suspensions with thousands of particles and on flow through complex periodic channels, together with strong and weak scaling studies, demonstrate efficient performance on systems with millions of degrees of freedom on distributed-memory architectures. △ Less

Submitted 28 May, 2026; originally announced May 2026.

Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning

Authors: Yunfei Liu, Hao Wang, Yuhang Qi, Hao Yue, Dehong Meng, Wei Li, Rui Wang, Tiejun Li, Jie Liu, Junwu Hong, Xinhai Chen

Abstract: High-fidelity computational fluid dynamics is essential for aerospace design, but engineering-scale simulations of practical three-dimensional aircraft remain computationally expensive. Learning-based flow-field initialization can improve efficiency by reducing the numerical distance between the initial and converged solutions, yet existing deep learning approaches remain difficult to scale to lar… ▽ More High-fidelity computational fluid dynamics is essential for aerospace design, but engineering-scale simulations of practical three-dimensional aircraft remain computationally expensive. Learning-based flow-field initialization can improve efficiency by reducing the numerical distance between the initial and converged solutions, yet existing deep learning approaches remain difficult to scale to large three-dimensional aircraft flows with multiscale regional heterogeneity. Most prior studies therefore focus on two-dimensional problems, surface quantities, integral aerodynamic coefficients, or simplified three-dimensional cases with limited grid resolution.Here we propose MHLF, a multigrid-hierarchical learning framework for accelerating engineering-scale aircraft flow simulations while preserving high-fidelity numerical accuracy. MHLF combines a topologically consistent geometric multigrid representation with a hierarchical strategy that captures regional flow heterogeneity during both prediction and subsequent CFD correction. Across three engineering-scale aircraft cases spanning Mach 0.15 to 6.0 and covering subsonic, transonic and supersonic regimes, MHLF accelerates convergence without sacrificing flow-field accuracy, achieving a 3 to 8 times efficiency improvement over conventional initialization. These results demonstrate practical full-flow-field prediction for large three-dimensional aircraft within the CFD domain and provide a foundation for data-driven acceleration of high-fidelity aircraft flow simulation. △ Less

Submitted 26 May, 2026; originally announced May 2026.

Hybrid Classical-Quantum Neural Networks for Multi-Characteristic Co-Optimization of Recessed-Gate AlGaN/GaN MIS-HEMTs

Authors: Rushat Rai, Pei-Jie Chang, Doan Viet Nguyen, Yuan-Chieh Chiu, Niall Tumilty, Yun-Yuan Wang, Simon See, Wen-Jay Lee, Tai-Yue Li, Nan-Yow Chen, Tian-Li Wu

Abstract: Optimizing recessed-gate AlGaN/GaN MIS-HEMTs requires accurate multi-characteristic models, but experimental semiconductor datasets remain costly and encode process-induced variability that simulations cannot faithfully reproduce. This work proposes a hybrid classical-quantum neural network (HQNN) for joint optimization of six electrical targets from a 24-dimensional fabrication/process vector. We… ▽ More Optimizing recessed-gate AlGaN/GaN MIS-HEMTs requires accurate multi-characteristic models, but experimental semiconductor datasets remain costly and encode process-induced variability that simulations cannot faithfully reproduce. This work proposes a hybrid classical-quantum neural network (HQNN) for joint optimization of six electrical targets from a 24-dimensional fabrication/process vector. We systematically screen quantum-circuit templates to extract circuit-design guidance, then select a final HQNN and compare it directly with classical baselines. On 468 experimental fabricated devices spanning 17 process splits, the selected HQNN, Circuit (13, 5) at L = 2, reduces overall normalized root mean square error (nRMSE) by 24.4% relative to ANN. Target-wise, the HQNN lowers Vth,lin RMSE from 0.297 V to 0.270 V, Vth,rev RMSE from 0.278 V to 0.263 V, DeltaVth RMSE from 0.049 V to 0.045 V, SS RMSE from 22.22 mV/dec to 19.87 mV/dec, and Id RMSE from 5.75 x 10^-8 A to 4.35 x 10^-8 A, while Ion RMSE remains competitive (0.053 A vs. 0.056 A). Controlled ansatz ablations further show that performance depends strongly on architecture: parameter count, depth, and two-qubit gate count correlate positively with accuracy, expressibility (DKL) correlates negatively, and controlled-rotation entanglers outperform static controlled-NOT (CNOT)-based circuits in aggregate. A depolarizing-noise study on a representative 4-qubit circuit further suggests that comparable HQNNs may be trainable or deployable on near-term quantum hardware. △ Less

Submitted 19 May, 2026; originally announced May 2026.

Comments: 13 pages, 9 figures

Dissipative acousto-mechanical parametric interface between high-overtone acoustics and flexural phonons

Authors: Xun Ji, Huanying Sun, Longhao Wu, Qichun Liu, Yulong Liu, Mika A. Sillanpää, Tiefu Li

Abstract: High-overtone bulk acoustic wave resonators (HBARs) promise advanced phononics, yet achieving nonlinearity remains challenging. We demonstrate a radiation-pressure-type parametric interaction between GHz HBARs and low-frequency flexural modes in a suspended silicon nitride membrane, where mechanical displacement modulates the external dissipation rate to enable dissipative acousto-mechanical coupl… ▽ More High-overtone bulk acoustic wave resonators (HBARs) promise advanced phononics, yet achieving nonlinearity remains challenging. We demonstrate a radiation-pressure-type parametric interaction between GHz HBARs and low-frequency flexural modes in a suspended silicon nitride membrane, where mechanical displacement modulates the external dissipation rate to enable dissipative acousto-mechanical coupling. Benefiting from the high quality factor, the system enters the resolved-sideband regime at room temperature, yielding acousto-mechanically induced transparency. We observe tunable Kerr nonlinearity and generate coherent HBAR frequency combs via two-tone driving. Notably, our dissipative coupling strength is 20 times larger than the dispersive coupling, the highest ratio among reported hybrid dissipative-dispersive coupling systems, resulting in the experimental observation of amplification in the reflection spectra under red-sideband driving. The ability to interface dense HBAR modes with a common mechanical resonator provides a scalable on-chip platform for multimode phononic information processing, with quantum phononics potentially achievable at sub-Kelvin temperatures. △ Less

Submitted 23 May, 2026; originally announced May 2026.

Comments: 7 pages, 4 figures

DeepFilters: Scattering-Aware Pupil Engineering with Learned Digital Filter Reconstruction for Extended Depth of Field Microscopy

Authors: Joseph L. Greene, Suet YIng Chan, Qilin Deng, Jeffrey Alido, Alexandra Lion, Guorong Hu, Ruipeng Guo, Tongyu Li, Kivilcim Kiliç, Ian Davison, Lei Tian

Abstract: Extended depth of field microscopy encodes axial information into a single acquisition through engineered point spread functions, but conventional and deep optics approaches are subject to degradation in scattering tissue. We introduce DeepFilters, a scattering-aware deep optics framework that jointly optimizes a parameterized pupil filter and a digital-filter-based reconstruction network through… ▽ More Extended depth of field microscopy encodes axial information into a single acquisition through engineered point spread functions, but conventional and deep optics approaches are subject to degradation in scattering tissue. We introduce DeepFilters, a scattering-aware deep optics framework that jointly optimizes a parameterized pupil filter and a digital-filter-based reconstruction network through a calibrated differentiable forward model to achieve broad generalization without retraining. Incorporating empirical scattering kernels, physics-guided regularization, and a hybrid genetic-gradient initialization strategy, DeepFilters extends the PSF from 16 micron to >400 micron in clear media and enables signal recovery beyond 120 micron deep in biological tissues, validated across fixed brain slices and sea urchin embryos. △ Less

Submitted 13 May, 2026; originally announced May 2026.

Comments: 38 pages (18 main text, 20 supplement), 23 Figures (7 main text, 16 supplement)

Dynamic Modulated Arc Therapy (DMAT): An Intent-Driven, Time-Aware Framework for Next-Generation Radiotherapy Delivery

Authors: Taoran Li, Esa Kuusela, Emmi Ruokokoski, Heini Hyvönen, Jerry Jaboin, Mirko Myllykoski, Jussi Nurminen, Riku Paananen, Jarkko Peltola, Marko Rusanen, Martin Sabel, Kevin Moore, Christopher Boylan

Abstract: Traditional VMAT optimization often ignores dynamic machine limits, treating delivery time as an emergent property rather than a steerable parameter. This work introduces Dynamic Modulated Arc Therapy (DMAT), an intent-driven framework that jointly co-optimizes dosimetric quality, delivery time, and modulation complexity. DMAT couples machine emulation accounting for axis synchronization and fin… ▽ More Traditional VMAT optimization often ignores dynamic machine limits, treating delivery time as an emergent property rather than a steerable parameter. This work introduces Dynamic Modulated Arc Therapy (DMAT), an intent-driven framework that jointly co-optimizes dosimetric quality, delivery time, and modulation complexity. DMAT couples machine emulation accounting for axis synchronization and finite acceleration with dynamic modulation control and clinical cost functions. A user-selected level (-3 to +3) governs leaf-travel, MU behavior, and CP density. Plans are created by initializing CP geometry, leaf positions, and MU, then iteratively alternating dosimetric updates with sequencing updates (penalties for motion, velocity changes, MU uniformity, and complexity), followed by post-processing. CP density is adapted by first optimizing with a uniform distribution and then redistributing CPs to arc sectors with higher complexity. DMAT was evaluated using a hypothetical system (2.5 RPM gantry, 6.25 cm/s MLC, 3000 MU/min) on H&N, lung SBRT, and prostate SBRT cases. Higher modulation levels produced increased MU/Gy and longer delivery times, while adaptive CP allocation concentrated resolution in high-reward sectors. H&N cases showed substantial quality gains with increased modulation, whereas prostate and lung SBRT exhibited smaller incremental improvements. When efficiency was prioritized (negative levels), DMAT reduced modulation and maintained a constant CP budget while shortening delivery time, producing quantifiable reductions in plan quality. DMAT enables intent-driven planning where quality and complexity are co-optimized via machine-aware timing. Accurate delivery time is exposed during planning, making trade-offs transparent and navigable for next-generation systems and time-constrained workflows like motion-sensitive or adaptive radiotherapy. △ Less

Submitted 12 May, 2026; originally announced May 2026.

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models

Authors: Chengqian Zhang, Yucheng Jin, Duo Zhang, Tiejun Li, Han Wang

Abstract: Crystal generative models mainly learn what stable crystals look like, with little explicit supervision for what makes them stable. We reveal a substantial representation gap between state-of-the-art crystal generative models and pretrained universal machine learning interatomic potentials (MLIPs) via energy probing, and show this gap can be closed by a simple training-time alignment. We propose C… ▽ More Crystal generative models mainly learn what stable crystals look like, with little explicit supervision for what makes them stable. We reveal a substantial representation gap between state-of-the-art crystal generative models and pretrained universal machine learning interatomic potentials (MLIPs) via energy probing, and show this gap can be closed by a simple training-time alignment. We propose Crystal REPresentation Alignment (CrystalREPA), a plug-and-play framework that aligns the atom-wise hidden states of generative encoders with frozen MLIP representations through an element-aware contrastive objective, transferring stability-aware atomistic priors with marginal training overhead and no additional inference cost. Across three generative frameworks, ten MLIP teachers, and two benchmark datasets, CrystalREPA consistently improves the thermodynamic stability, structural validity, and structural fidelity of generated crystals. Equally important, we find that an MLIP's transfer effectiveness is poorly predicted by its accuracy on standard leaderboards (e.g., Matbench Discovery) but strongly predicted by the distinguishability of its atom-wise representation space, yielding a practical, accuracy-independent criterion for selecting MLIP teachers for generative transfer. △ Less

Submitted 9 May, 2026; originally announced May 2026.

One-dimensional polarization-hybrid photonic crystal molecules

Authors: Tiantong Li, Katia Gallo

Abstract: Photonic molecules, i.e. artificial structures composed of coherently coupled optical cavities, are paradigmatic systems for investigating fundamental phenomena across photonics, quantum optics and topological physics. In recent years, photonic integrated circuits have emerged as a particularly powerful platform for their realization, exploiting also additional synthetic dimensions afforded by the… ▽ More Photonic molecules, i.e. artificial structures composed of coherently coupled optical cavities, are paradigmatic systems for investigating fundamental phenomena across photonics, quantum optics and topological physics. In recent years, photonic integrated circuits have emerged as a particularly powerful platform for their realization, exploiting also additional synthetic dimensions afforded by the degrees of freedom of light. To date, however, photonic molecule implementations have relied almost entirely on geometries defined by spatial coupling and lattice symmetries rather than polarization. Here, we introduce a fundamentally new class of photonic molecules in which polarization is exploited as the primary dimension in the device response. By harnessing fundamental guided-mode couplings sustained by engineered Bragg gratings in photonic waveguides, we establish a new paradigm to access in 1D formats the coupled-resonator physics traditionally associated with higher-dimensional or free-space systems, demonstrating prototypical devices which can support Fano resonances or resonance-splitting for signals in the telecom band. Besides corroborating the theoretical predictions, experimental realizations in thin film lithium niobate open new prospects for the further exploration of novel reconfigurable topological, non-Hermitian and quantum photonic circuits, relying on the intrinsic nonlinear and electro-optic functionalities of this platform. △ Less

Submitted 5 May, 2026; originally announced May 2026.

Longitudinal beam instability driven by coherent radiation in an SSMB laser modulator

Authors: Yingjie Dai, Zhuoyuan Liu, Tong Li, Xiujie Deng, Lixin Yan

Abstract: Storage ring-based steady-state microbunching (SSMB) is a promising approach for generating high-average-power coherent radiation, while the instabilities driven by coherent undulator radiation in the laser modulator (LM) is important for the ring performance. In this paper we investigate the longitudinal single-bunch multi-turn LM instability using cavity mode decomposition techniques. The evolut… ▽ More Storage ring-based steady-state microbunching (SSMB) is a promising approach for generating high-average-power coherent radiation, while the instabilities driven by coherent undulator radiation in the laser modulator (LM) is important for the ring performance. In this paper we investigate the longitudinal single-bunch multi-turn LM instability using cavity mode decomposition techniques. The evolution of the wakefield in the longitudinal beam dynamics equations are derived, and the instability growth rates are analyzed. Numerical simulations show excellent agreement with the theoretical model, validating the mode decomposition approach. These findings provide critical insights into the design and operation of SSMB storage rings, suggesting effective mitigation strategies to suppress the instability and enhance the overall performance. △ Less

Submitted 29 April, 2026; originally announced April 2026.

Comments: 17 pages, 15 figures

Spectral window engineering for synthetic wave compensation of plasmonic loss

Authors: Fuxin Guan, Nanyu Chen, Zemeng Lin, Wange Song, Shining Zhu, Tao Li, Shuang Zhang

Abstract: Synthetic complex-frequency excitations have emerged as a powerful tool for loss compensation and resolution enhancement. We show that, ideally, these excitations allow for the complete offsetting of intrinsic damping over long evolution times, governed by a universal inverse-time scaling law for residual damping under Nth-order synthetic illumination. However, in realistic experimental settings,… ▽ More Synthetic complex-frequency excitations have emerged as a powerful tool for loss compensation and resolution enhancement. We show that, ideally, these excitations allow for the complete offsetting of intrinsic damping over long evolution times, governed by a universal inverse-time scaling law for residual damping under Nth-order synthetic illumination. However, in realistic experimental settings, the achievable virtual gain is fundamentally restricted by the finite spectral measurement range, which introduces unwanted temporal artifacts and disrupts this ideal scaling. We demonstrate that the conventional rectangular spectral window creates a slowly decaying temporal kernel (1/t) that leaks unwanted early-time signals into the late-time regime, thereby masking the targeted response. To mitigate this constraint, we introduce a Hann-window filtering technique that yields a faster decaying temporal kernel (1/t)^3. This simple spectral engineering dramatically suppresses spurious contributions and extends the usable lifetime of the synthetic waveform. Experimental validation using coupled plasmonic resonators demonstrates that Hann-window filtering improves the loss-offsetting efficiency by nearly a factor of three compared with the standard rectangular window. Our results reveal the fundamental temporal limits of synthetic complex-frequency waves and provide a practical strategy to achieve long-lived, high-SNR loss compensation in nanophotonic systems. △ Less

Submitted 29 April, 2026; originally announced April 2026.

Comments: 4 Figures

Embedded underwater front-end electronics for the 3-inch photomultipliers in the JUNO experiment

Authors: Cédric Cerna, Miao He, Xiaoshan Jiang, Juan Pedro Ochoa-Ricoux, Frédéric Perrot, Angel Abusleme, Thomas Adam, Fengpeng An, Costas Andreopoulos, Giuseppe Andronico, João Pedro Athayde Marcondes de André, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Didier Auguste, Nikita Balashov, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Beretta, Antonio Bergnoli, Nikita Bessonov, Daniel Bick, Lukas Bieger, Svetlana Biktemerova , et al. (576 additional authors not shown)

Abstract: The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kton liquid scintillator-based, low-radioactivity, multi-purpose neutrino detector located 693 meters (1800 m.w.e.) underground in the Guangdong province, China. To detect scintillation light produced in the target, the detector is equipped with 17,612 20-inch photomultipliers (PMTs), forming the Large PMT system (LPMT). In addition, 25,… ▽ More The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kton liquid scintillator-based, low-radioactivity, multi-purpose neutrino detector located 693 meters (1800 m.w.e.) underground in the Guangdong province, China. To detect scintillation light produced in the target, the detector is equipped with 17,612 20-inch photomultipliers (PMTs), forming the Large PMT system (LPMT). In addition, 25,600 3-inch photomultipliers (the Small Photomultiplier System or SPMT) are deployed in the gaps between the LPMTs. This paper presents the design and performance of the underwater front-end electronics developed for the SPMT system. It details the individual electronics boards and their key components, the inter-board interfaces, the system-level design, and the firmware architecture that supports data acquisition and control. It also outlines mechanical and thermal integration, board validation procedures, and system performance metrics. The readout chain includes digitization of 128 PMT channels per unit, synchronized time-stamping, charge measurement, event packaging, and bandwidth management. Comprehensive validation confirms the system's readiness to meet JUNO's stringent physics goals. The underwater electronics achieve noise levels as low as 0.04 photoelectrons with minimal crosstalk (below 0.4%) and a bandwidth of 57 MB/s, ensuring reliable single photo-electron detection and operation under high-rate conditions. The SPMT system has now been fully integrated and installed in JUNO. Its commissioning and physics performance will be reported in a future publication. △ Less

Submitted 1 June, 2026; v1 submitted 28 April, 2026; originally announced April 2026.

Comments: Submitted to Nucl. Instrum. Methods Phys. Res. A

Lift and leading-edge suction parameter of separated flows over an NACA0012 at high angles of attack

Authors: Ching Chang, You-Peng Shih, Tang-An Li

Abstract: The flow condition at the leading edge governs the dynamics of the leading-edge vortex, which is crucial for understanding the separated flow over an airfoil at high angle of attack. Furthermore, with extensive applications in biomimetic flight, the wings encountering high-angle-of-attack situations in an unsteady manner are of great interest. The leading-edge suction parameter (LESP) is a dimensi… ▽ More The flow condition at the leading edge governs the dynamics of the leading-edge vortex, which is crucial for understanding the separated flow over an airfoil at high angle of attack. Furthermore, with extensive applications in biomimetic flight, the wings encountering high-angle-of-attack situations in an unsteady manner are of great interest. The leading-edge suction parameter (LESP) is a dimensionless metric proposed to quantify the leading-edge flow condition, and is implemented in the LESP-modulated discrete vortex method, which successfully predicts aerodynamics of airfoils in motion. To discern the timing of leading-edge vortex formation, a critical threshold for LESP is chosen to control the onset of separation. However, it is not obvious that the same strategy could be applied to a stationary wing where the separation is not dominated by the motion of the airfoil. We conduct computational fluid dynamics (CFD) simulations for a stationary NACA0012 airfoil at high angles of attack, and extract the leading-edge flow quantities from the CFD data. In addition, vorticity flux, which contributes to the formation of vortices above the top surface of the airfoil, is also investigated to reveal the vorticity budget and its relevance to aerodynamic performance. We show that for the laminar case ($Re=1000$), the instantaneous LESP is well correlated with the lift, while for the turbulence ($Re=10^5$), the time-averaged LESP is well correlated with the lift. The result would provide insights into future improvements for vortex-based models of separated flows. △ Less

Submitted 24 April, 2026; originally announced April 2026.

MSC Class: 76 ACM Class: J.2

Turbulent pair dispersion with Stochastic Generative Diffusion Models

Authors: Andrei Pantea, Luca Biferale, Michele Buzzicotti, Guillaume Charpiat, Sergio Chibbaro, Tianyi Li

Abstract: Recent advances in data-driven modeling have shown that diffusion models can successfully generate synthetic Lagrangian trajectories in turbulent flows. Building on this progress, we extend the method to the joint generation of pairs of Lagrangian velocity trajectories, enabling a fully data-driven representation of turbulent pair dispersion, a long-standing fundamental problem with broad relevanc… ▽ More Recent advances in data-driven modeling have shown that diffusion models can successfully generate synthetic Lagrangian trajectories in turbulent flows. Building on this progress, we extend the method to the joint generation of pairs of Lagrangian velocity trajectories, enabling a fully data-driven representation of turbulent pair dispersion, a long-standing fundamental problem with broad relevance in fluid dynamics. We demonstrate that diffusion models accurately reproduce the evolution of particle-pair separation, including deviations from Richardson's classical scaling law, while simultaneously preserving all key single-particle statistical properties reported in previous studies. These findings underscore the potential of diffusion-based generative models to emulate high-dimensional, multi-scale turbulent dynamics, further establishing them as a powerful tool for scientific modeling and for future geophysical and astrophysical applications. △ Less

Submitted 14 April, 2026; originally announced April 2026.

Projection of purification performance for the RELICS experiment

Authors: Jiachen Yu, Kaihang Li, Jingfan Gu, Chang Cai, Guocai Chen, Jiangyu Chen, Huayu Dai, Rundong Fang, Hongrui Gao, Fei Gao, Xiaoran Guo, Jiheng Guo, Chengjie Jia, Gaojun Jin, Fali Ju, Yanzhou Hao, Xu Han, Yang Lei, Meng Li, Minhua Li, Shengchao Li, Siyin Li, Tao Li, Qing Lin, Jiajun Liu , et al. (25 additional authors not shown)

Abstract: The RELICS (REactor neutrino LIquid xenon Coherent elastic Scattering) experiment employs a dual-phase liquid xenon time projection chamber to search for Coherent Elastic Neutrino-Nucleus Scattering (CE$ν$NS) induced by reactor neutrinos. To detect these sub-keV nuclear recoils and minimize signal attenuation, it is critical to maintain a sufficiently low impurity concentration in the detector. Th… ▽ More The RELICS (REactor neutrino LIquid xenon Coherent elastic Scattering) experiment employs a dual-phase liquid xenon time projection chamber to search for Coherent Elastic Neutrino-Nucleus Scattering (CE$ν$NS) induced by reactor neutrinos. To detect these sub-keV nuclear recoils and minimize signal attenuation, it is critical to maintain a sufficiently low impurity concentration in the detector. This work presents a comprehensive purity evolution model developed to describe impurity migration inside the detector. Utilizing measured material outgassing rates as input parameters, the model incorporates non-uniform transport mechanisms of the impurities, including circulation, vaporization, and condensation. The model is validated using data from a dedicated prototype detector. Based on this validated model, projections for the purification performance of the upcoming RELICS-10 and RELICS-50 detectors are provided. △ Less

Submitted 14 April, 2026; originally announced April 2026.

Nanoscale electrothermal-switch superconducting diode for electrically programmable superconducting circuits

Authors: Tianyu Li, Jiong Li, Chong Li, Peiyuan Huang, Nuo-Zhou Yang, Wuyue Xu, Wen-Cheng Yue, Yang-Yang Lyu, Yihuang Xiong, Xuecou Tu, Tao Tao, Xiaoqing Jia, Qing-Hu Chen, Huabing Wang, Peiheng Wu, Yong-Lei Wang

Abstract: Superconducting diodes enable dissipationless directional transport, yet achieving electrical tunability and scalability remains a major challenge for circuit-level integration. Here, we demonstrate an electrothermal-switch superconducting diode in which a gate-controlled nanoscale hotspot dynamically breaks inversion symmetry in a superconducting nanowire. This mechanism gives rise to two coexist… ▽ More Superconducting diodes enable dissipationless directional transport, yet achieving electrical tunability and scalability remains a major challenge for circuit-level integration. Here, we demonstrate an electrothermal-switch superconducting diode in which a gate-controlled nanoscale hotspot dynamically breaks inversion symmetry in a superconducting nanowire. This mechanism gives rise to two coexisting nonreciprocal transport regimes-one associated with a nonreciprocal superconducting-to-normal transition and the other with ratchet-like vortex dynamics-both originating from the same electrothermal-switch process. The diode exhibits efficiencies up to 42% and 60% for the two regimes, respectively, and can be electrically switched on, off, or reversed in polarity in situ by applying a small gate current. These capabilities enable programmable superconducting circuits that realize electrically reconfigurable full-wave and half-wave rectification. The lithography-compatible design, high performance, and gate-controlled functionality establish a scalable platform for programmable superconducting electronics and hybrid quantum systems. △ Less

Submitted 14 April, 2026; originally announced April 2026.

Comments: To appear in Nano Letters

Journal ref: Nano Lett. 26, 5411-5417 (2026)

Global near-real-time daily emissions of atmospheric pollutants from power plants

Authors: Tao Li, Lixing Wang, Biqing Zhu, Zhu Liu

Abstract: The power sector is a major source of fossil fuel use and air pollutant emissions, making high-spatiotemporal-resolution emission accounting essential for effective mitigation policy and air quality management. Yet existing public inventories are often limited by low timeliness and coarse resolution. Here, we develop a global, plant-level, daily, multi-pollutant emission database for the power sec… ▽ More The power sector is a major source of fossil fuel use and air pollutant emissions, making high-spatiotemporal-resolution emission accounting essential for effective mitigation policy and air quality management. Yet existing public inventories are often limited by low timeliness and coarse resolution. Here, we develop a global, plant-level, daily, multi-pollutant emission database for the power sector by integrating nearly 3 million hourly-to-daily near-real-time power generation records from 57 countries, representing about 81% of global fossil-fuel-based electricity generation, with fundamental information for more than 10,000 power plants worldwide, including location and installed capacity. The dataset substantially improves the timeliness and granularity of global power-sector emission estimates. From 2019 to 2025, emissions of most pollutants increased, with 2025 daily mean emissions reaching 0.274 kt/d for BC, 45.1 kt/d for CO, 0.418 kt/d for NH3, 52.2 kt/d for NOx, 3.01 kt/d for NMVOC, 0.418 kt/d for OC, 6.76 kt/d for PM10, 5.11 kt/d for PM2.5, and 78.5 kt/d for SO2. Compared with 2019, NMVOC showed the largest increase, whereas SO2 was the only pollutant to decline overall. Coal remained the dominant source of sulfur-, nitrogen-, and particulate-related emissions, while gas and biomass contributed more to carbonaceous species and reduced nitrogen. The dataset also captures pronounced seasonal, regional, and short-term variability. Against EDGAR for 2019-2022, our estimates agree well, with Pearson correlations of 0.92-0.99 and mean relative deviations of 8.8%-28.1%. This near-real-time, high-resolution dataset provides a strong foundation for air pollution control, carbon mitigation, emission monitoring, and satellite-based inversion. △ Less

Submitted 8 April, 2026; originally announced April 2026.

Near real-time monitoring of global land-ocean cover dynamics

Authors: Lixing Wang, Tao Li, Xinyu Dou, Zhu Liu

Abstract: Monitoring the dynamics of global land-ocean cover is fundamental for regulating the Earth's climate and sustaining terrestrial and marine ecosystems. However, existing datasets and research often exhibit limitations in temporal resolution and timeliness, lack coupled analysis of land cover and sea ice dynamics, and fail to incorporate the perspective of Earth system safety thresholds. Here, we de… ▽ More Monitoring the dynamics of global land-ocean cover is fundamental for regulating the Earth's climate and sustaining terrestrial and marine ecosystems. However, existing datasets and research often exhibit limitations in temporal resolution and timeliness, lack coupled analysis of land cover and sea ice dynamics, and fail to incorporate the perspective of Earth system safety thresholds. Here, we developed an integrated monitoring framework by fusing multi-source remote sensing and reanalysis data, generating a 5-day resolution time series (2018-2025) of global land cover and sea ice coverage with near-real-time update capability. Our analysis reveals distinct latitudinal and regional patterns, with forests dominating (27.0% of global land area) tropical and subtropical regions. At the national scale, land cover composition and seasonal rhythms vary significantly, with countries like China, India, and the US exhibiting divergent patterns such as bimodal cropland fluctuations and alternating snow/ice dominance. Temporally, vegetated cover types exhibit seasonal cycles peaking during Northern Hemisphere summer, and a pronounced anti-phase seasonal pattern is observed between Arctic and Antarctic sea ice coverage. Crucially, safety threshold analysis indicates the global forest cover indicator (~60%) is approaching the 54% lower safe limit, with a declining trend in recent years. Concurrently, Arctic sea ice coverage in September occasionally drops to 23%, below its critical upper limit of 27.6%. Temperature presents a significant negative correlation with sea ice cover (R = -0.78, p < 0.001), with asymmetric freezing and melting rates. By quantifying the proximity of key indicators to their safety thresholds, this study provides a robust, integrated framework for early-warning assessment, thereby offering vital scientific support for global climate adaptation and sustainable policymaking. △ Less

Submitted 6 April, 2026; originally announced April 2026.

FermiLink: A Unified Agent Framework for Multidomain Autonomous Scientific Simulations

Authors: Gang Meng, Andres Felipe Bocanegra Vargas, Xinwei Ji, Federico Garcia-Gaitan, Felipe Reyes-Osorio, Jalil Varela-Manjarres, Yafei Ren, Mohammadhasan Dinpajooh, Branislav K. Nikolić, Tao E. Li

Abstract: Artificial-intelligence (AI) agent frameworks have been developed for autonomous scientific simulations, but most current agent frameworks are tailored to a single or a small set of software packages. Herein, FermiLink, a unified and extensible open-source agent framework is introduced for multidomain scientific simulations. Its key design principle is the separation of package knowledge bases fro… ▽ More Artificial-intelligence (AI) agent frameworks have been developed for autonomous scientific simulations, but most current agent frameworks are tailored to a single or a small set of software packages. Herein, FermiLink, a unified and extensible open-source agent framework is introduced for multidomain scientific simulations. Its key design principle is the separation of package knowledge bases from simulation workflows, so that simulation workflows in FermiLink, from figure-level simulations to full-paper-level research on high-performance computing clusters, operate uniformly among supported packages via a four-layer progressive disclosure mechanism. Using OpenAI Codex as the agent provider, the capabilities of FermiLink are demonstrated across approximately 50 scientific software packages spanning nine research domains from physics to engineering. Systematic benchmarks on 132 real-world figure-level reproduction tasks with 44 packages show that FermiLink reproduces 74 (56.1%) of published figures with simulations, among which 30 achieve high-fidelity agreement and 35 reach qualitative agreement with the target figures. A smaller set of human expert-guided reproduction benchmarks with 10 packages further highlights the importance of expert insights for improving the simulation fidelity. Beyond reproduction, a single-blinded study demonstrates that FermiLink can produce research-grade results on unpublished polariton physics problems when provided with sufficiently detailed research objectives and source code, even in the absence of external documentation or tutorials. Overall, FermiLink provides a scalable research infrastructure that may accelerate the path from scientific questions to computational results across diverse domains. △ Less

Submitted 7 April, 2026; v1 submitted 3 April, 2026; originally announced April 2026.

Comments: Simulation data available at https://www.taoeli.org/publications; source code available at Github https://github.com/TaoELi/FermiLink

A MIDAS-based Data Acquisition System for Gaseous Detectors

Authors: Yuanchun Liu, Tao Li, Yu Chen, Ke Han, Leyan Li, Shaobo Wang, Wei Wang

Abstract: We present a data acquisition~(DAQ) software based on the MIDAS framework, specifically for gaseous detectors to support the detector deployments and applications. It implements a comprehensive suite of functions, including parameter configuration, data acquisition, decoding, and storage, alongside web-based operation and real-time monitoring capabilities. We establish a fully unified workflow spa… ▽ More We present a data acquisition~(DAQ) software based on the MIDAS framework, specifically for gaseous detectors to support the detector deployments and applications. It implements a comprehensive suite of functions, including parameter configuration, data acquisition, decoding, and storage, alongside web-based operation and real-time monitoring capabilities. We establish a fully unified workflow spanning data acquisition to offline analysis, enabling real-time visualization of signal waveforms and energy spectra. The system has been successfully deployed in the PandaX-III experiment, which utilized a high-pressure gaseous detector to search for neutrinoless double beta decay. Its performance and stability have been validated through tests involving two distinct electronics setups and joint commissioning with the detector. △ Less

Submitted 13 April, 2026; v1 submitted 1 April, 2026; originally announced April 2026.

Shining light on short-range atomic ordering in semiconductors alloys

Authors: Anis Attiaoui, Shunda Chen, Joseph C. Woicik, J. Zach Lentz, Liliane M. Vogl, Jarod E. Meyer, Kunal Mukherjee, Andrew Minor, Tianshu Li, Paul C. McIntyre

Abstract: The functional properties of semiconductors are typically controlled by tailoring their chemical composition and their state of strain, and by controlling their long-range structural order, including the presence of extended defects such as dislocations. In addition to these approaches, theoretical predictions suggest that short-range order (SRO) of atoms in group-IV semiconductor alloys can modif… ▽ More The functional properties of semiconductors are typically controlled by tailoring their chemical composition and their state of strain, and by controlling their long-range structural order, including the presence of extended defects such as dislocations. In addition to these approaches, theoretical predictions suggest that short-range order (SRO) of atoms in group-IV semiconductor alloys can modify the bandgap, a defining property of any semiconductor. Herein, a new machine learning enabled, computation-guided methodology for extended X-ray absorption fine structure (EXAFS) analysis of SRO is used to quantify the effects of local atomic order on the bandgap of germanium-tin (GeSn) alloy single crystal nanostructures with well-controlled strain and composition. Correlative analysis of EXAFS and photoluminescence (PL) establishes the relationship between bandgap and the Warren-Cowley short-range order (WC-SRO) parameter of the GeSn alloys. It is further demonstrated that SRO can be tuned over a broad range by post-deposition annealing of the alloy crystals. This work establishes control of SRO as an important design parameter for semiconducting properties and suggests the potential for quantitative measurement and tuning of SRO in other semiconductor alloy systems. △ Less

Submitted 7 April, 2026; v1 submitted 29 March, 2026; originally announced March 2026.

Comments: 10 pages in the main draft, with 4 pages in Methods section. 4 Figures in total

Beam Test Characterization of Silicon Microstrip Detector Flight-Model Ladders for the AMS-02 Upgrade

Authors: Dexing Miao, Giovanni Ambrosi, Mattia Barbanera, Baasansuren Batsukh, Hengyi Cai, Mengke Cai, Xudong Cai, Yuman Cai, Yuan-Hann Chang, Shanzhen Chen, Hsin-Yi Chou, Xingzhu Cui, Mingyi Dong, Matteo Duranti, Ke Gong, Mingjie Feng, Valerio Formato, Yisheng Fu, Daojin Hong, Maria Ionica, Xiaojie Jiang, Yaozu Jiang, Liangchenglong Jin, Shengjie Jin, Vladimir Koutsenko , et al. (34 additional authors not shown)

Abstract: The AMS-02 experiment plans to install a new silicon microstrip tracker layer (Layer-0) on top of the existing detector, increasing the cosmic-ray acceptance by a factor of 3. Layer-0 employs a design in which multiple silicon microstrip detectors (SSDs) are connected in series to form long detector ladders. We present a detailed performance study of the flight-model ladders using a 350~GeV mixed… ▽ More The AMS-02 experiment plans to install a new silicon microstrip tracker layer (Layer-0) on top of the existing detector, increasing the cosmic-ray acceptance by a factor of 3. Layer-0 employs a design in which multiple silicon microstrip detectors (SSDs) are connected in series to form long detector ladders. We present a detailed performance study of the flight-model ladders using a 350~GeV mixed hadron beam at the CERN SPS. The study focuses on the following aspects: (i) the performance of ladders with different numbers of SSDs, for which the intrinsic spatial resolution at normal incidence varies from $9.5~μ\mathrm{m}$ to $11.4~μ\mathrm{m}$ for ladders composed of 8 to 12 SSDs; (ii) the response consistency for particles impacting on the \emph{Head} and \emph{Tail} regions of the ladder; and (iii) the dependence of the detector performance on the particle incidence angle. △ Less

Submitted 26 March, 2026; originally announced March 2026.

A Telescope System for Charge and Position Measurement of High Energy Nuclei

Authors: Dexing Miao, Zhiyu Xiang, Giovanni Ambrosi, Mattia Barbanera, Baasansuren Batsukh, Mengke Cai, Xudong Cai, Yuan-Hann Chang, Shanzhen Chen, Hsin-Yi Chou, Xingzhu Cui, Mingyi Dong, Matteo Duranti, Ke Gong, Mingjie Feng, Valerio Formato, Daojin Hong, Maria Ionica, Xiaojie Jiang, Yaozu Jiang, Liangchenglong Jin, Shengjie Jin, Vladimir Koutsenko, Tiange Li, Zuhao Li , et al. (21 additional authors not shown)

Abstract: A high-granularity telescope system with a large sensitive area and low material budget has been developed for high-energy heavy ion beam tests. The telescope consists of nine layers of silicon microstrip detectors (SSDs), whose performance was validated through a heavy ion beam test at the CERN SPS. A hybrid machine learning algorithm is proposed to address the challenges of nuclear charge measur… ▽ More A high-granularity telescope system with a large sensitive area and low material budget has been developed for high-energy heavy ion beam tests. The telescope consists of nine layers of silicon microstrip detectors (SSDs), whose performance was validated through a heavy ion beam test at the CERN SPS. A hybrid machine learning algorithm is proposed to address the challenges of nuclear charge measurement with SSDs. The system achieves a spatial resolution of $\mathcal{O}(1) \,$\SI{}{\micro\metre} and a charge resolution better than 0.16 charge units for nuclei from $Z = 1$ to $Z = 29$, with a sensitive area of $8 \times 8 \, \mathrm{cm}^2$. To the best of our knowledge, this represents the most precise charge and spatial resolution simultaneously achieved by a silicon telescope to date. △ Less

Submitted 26 March, 2026; originally announced March 2026.

Two-dimensional IR-Raman spectroscopy of vibrational polaritons: Role of dipole surfaces

Authors: Xinwei Ji, Tomislav Begusic, Tao E. Li

Abstract: Nonlinear spectroscopy provides a unique perspective to understand time-resolved molecular dynamics under vibrational strong coupling (VSC). Herein, equilibrium-nonequilibrium cavity molecular dynamics simulations are performed to compute the two-dimensional (2D) infrared-infrared-Raman (IIR) spectroscopy of liquid water under VSC. In conventional computational chemistry practices, accurate molecu… ▽ More Nonlinear spectroscopy provides a unique perspective to understand time-resolved molecular dynamics under vibrational strong coupling (VSC). Herein, equilibrium-nonequilibrium cavity molecular dynamics simulations are performed to compute the two-dimensional (2D) infrared-infrared-Raman (IIR) spectroscopy of liquid water under VSC. In conventional computational chemistry practices, accurate molecular spectra are often constructed by using an advanced molecular dipole or polarizability model to post-process molecular dynamics trajectories evolved under a computationally efficient potential. By contrast, this work highlights the necessity of employing a consistent dipole surface model in both CavMD simulations and spectroscopic post-processing. While utilizing inconsistent dipole models only mildly influences the linear polariton spectrum, it severely distorts 2D spectra in wide frequency regions. With a consistent dipole-induced-dipole model, compared to the outside-cavity molecular 2D-IIR spectrum, the cavity 2D-IIR spectrum splits the OH stretch band to a pair of polariton branches along only the IR (not Raman) axis, while fading molecular signals at other frequency regions. This work provides the foundation for employing direct CavMD simulations to construct 2D spectra of realistic molecules under VSC. △ Less

Submitted 25 March, 2026; originally announced March 2026.

Advanced Virgo Plus for O5 -- Design Report Overview

Authors: F. Acernese, A. Agapito, D. Agarwal, I. -L. Ahrend, L. Aiello, A. Ain, S. Albanesi, W. Ali, C. Alléné, A. Allocca, W. Amar, A. Amato, F. Amicucci, C. Amra, M. Andia, T. Andrić, S. Ansoldi, S. Antier, E. Z. Appavuravther, M. Arca Sedda, F. Arciprete, F. Armato, N. Arnaud, L. Asprea, M. Assiduo , et al. (556 additional authors not shown)

Abstract: This document presents an overview of the design, implementation, and expected performance of the Advanced Virgo Plus (AdV+) upgrades in view of the O5 observing run. Following the experience gained during the O4 commissioning and operations, the Virgo Collaboration has revised the upgrade strategy to address limitations associated with marginally stable recycling cavities. The O5 upgrade program… ▽ More This document presents an overview of the design, implementation, and expected performance of the Advanced Virgo Plus (AdV+) upgrades in view of the O5 observing run. Following the experience gained during the O4 commissioning and operations, the Virgo Collaboration has revised the upgrade strategy to address limitations associated with marginally stable recycling cavities. The O5 upgrade program combines elements from the original AdV+ Phase II project with new design solutions, including the implementation of stable recycling cavities, a major modification to the central interferometer layout, and a comprehensive renewal of critical subsystems. The planned upgrades are organized in two steps, targeting progressive improvements in operational stability, noise reduction, and detector sensitivity. Key developments include new vacuum infrastructures, suspensions, mirrors, optical configurations, quantum noise reduction systems, and high-power laser technologies. The resulting configuration is expected to significantly enhance the interferometer performance, enabling a substantial increase in astrophysical reach and scientific return during O5. △ Less

Submitted 31 March, 2026; v1 submitted 20 March, 2026; originally announced March 2026.

Physics-Constrained Diffusion Model for Synthesis of 3D Turbulent Data

Authors: Tianyi Li, Michele Buzzicotti, Fabio Bonaccorso, Luca Biferale

Abstract: Synthesizing fully developed three-dimensional turbulent velocity fields remains a long-standing problem in fluid mechanics and an open challenge for generative modeling. The difficulty arises from the coexistence of extreme dimensionality, multiscale rough fluctuations and strong intermittency, together with exact physical constraints such as incompressibility and zero-mean momentum. We propose a… ▽ More Synthesizing fully developed three-dimensional turbulent velocity fields remains a long-standing problem in fluid mechanics and an open challenge for generative modeling. The difficulty arises from the coexistence of extreme dimensionality, multiscale rough fluctuations and strong intermittency, together with exact physical constraints such as incompressibility and zero-mean momentum. We propose a physics-constrained diffusion model (PCDM) in which these \emph{a priori} constraints are incorporated directly into the generative dynamics. Using rotating turbulence as a stringent benchmark, we show that the proposed framework enables stable and statistically faithful synthesis of inertial-range three-dimensional turbulent velocity fields at medium resolution, accurately reproducing anisotropic energy spectra, intermittency statistics, and physical constraints. By contrast, standard denoising diffusion probabilistic models without such constraints exhibit multiscale statistical deviations, violations of physical consistency, and substantially slower training convergence. These findings point to broader implications for generative modeling of high-dimensional complex systems under physical constraints. △ Less

Submitted 13 March, 2026; originally announced March 2026.

Comments: 14 pages, 9 figures. Submitted to a journal

Nuclear-Electronic Quantum Dynamics in a Plasmonic Nanocavity

Authors: Jonathan H. Fetherolf, Tim Duong, Tao E. Li, Sharon Hammes-Schiffer

Abstract: Plasmonic nanocavities are a promising platform for strong light-matter coupling and enhanced spectroscopies at the single-molecule level. These nanoscale environments are challenging to model due to their strongly multimodal character and short cavity lifetimes. Herein, we study the effects of these environments using real-time nuclear-electronic orbital time-dependent density functional theory (… ▽ More Plasmonic nanocavities are a promising platform for strong light-matter coupling and enhanced spectroscopies at the single-molecule level. These nanoscale environments are challenging to model due to their strongly multimodal character and short cavity lifetimes. Herein, we study the effects of these environments using real-time nuclear-electronic orbital time-dependent density functional theory (RT-NEO-TDDFT) coupled to multiple classical cavity modes in a manner that includes cavity loss. In RT-NEO-TDDFT, the quantum mechanical densities of all electrons and specified nuclei, typically protons, are propagated in real time. We show that a cavity with many modes at different frequencies can be used to probe and modify the nuclear-electronic quantum dynamics of chemical systems. Ultrafast excited-state proton transfer reactions can be probed through the time- and energy-resolved cavity emission of a multimode cavity. Under strong coupling conditions, the cavity can modify the dynamics, in some cases suppressing proton transfer and exhibiting Rabi-like oscillations of the cavity emission due to polariton formation. Utilizing the spectral density for an experimentally relevant nanoparticle-on-mirror single-molecule cavity, we show that an excited-state proton transfer system can evolve into resonance with the cavity even when initially out of resonance with the dominant cavity peak. In this case, tuning the dominant cavity peak to be resonant with the electronic transition leads to polariton formation for a small collection of molecules. The RT-NEO framework with multimode cavities enables the efficient simulation of chemical reactions in physically realistic electromagnetic environments, providing fundamental insights into the dynamics and associated spectroscopic signatures. △ Less

Submitted 3 July, 2026; v1 submitted 12 March, 2026; originally announced March 2026.

For molecular polaritons, disorder and phonon timescales control the activation of dark states in the thermodynamic limit

Authors: Tianchu Li, Pranay Venkatesh, Qiang Shi, Andrés Montoya-Castillo

Abstract: Collective light-matter systems host an extensive manifold of dark states whose role in the emergence of thermodynamic behavior remains poorly understood, especially in the presence of disorder and structured environments. Here, we develop a hybrid matrix product state-hierarchical equations of motion (MPS-HEOM) approach that enables numerically exact simulations of polariton dynamics from a few e… ▽ More Collective light-matter systems host an extensive manifold of dark states whose role in the emergence of thermodynamic behavior remains poorly understood, especially in the presence of disorder and structured environments. Here, we develop a hybrid matrix product state-hierarchical equations of motion (MPS-HEOM) approach that enables numerically exact simulations of polariton dynamics from a few emitters to the thermodynamic limit under both static and dynamic disorder. This allows us, for the first time, to provide a quantitative and operational answer to the long-standing question of what is the minimum system size required to reach the thermodynamic limit in collective polaritonic systems. By introducing a convergence scale, $N_{T}$, i.e., the number of molecules required for the photonic dynamics to reach the thermodynamic limit, we show that dynamic disorder generally poses a greater computational challenge than static disorder. We attribute this behavior to the suppression of collective light-matter dynamics by disorder, which dynamically activates non-collective degrees of freedom. We further find that $N_{T}$ exhibits a turnover behavior as the bath becomes more Markovian, as the bath timescales regulate bright-to-dark energy transfer and the involvement of dark and gray states. Hence, phonon timescales control both the breakdown of collective behavior and the growth of $N_{T}$. Our results establish the suppression of collective behavior as the key mechanism governing thermodynamic convergence in disordered light-matter systems. △ Less

Submitted 9 March, 2026; v1 submitted 6 March, 2026; originally announced March 2026.

Automated Detection and Climatological Analysis of Ripple-Scale Gravity Wave Instabilities Using a Squeeze-and-Excitation Convolutional Neural Network

Authors: Jiahui Hu, Alan Liu, Adriana Feener, Jing Li, Tao Li, Wenjun Dong

Abstract: All-sky OH airglow imaging provides two-dimensional observations of mesospheric gravity wave structure near ~87 km altitude. Ripple-scale instability signatures, characterized by 5-15 km horizontal wavelengths and short lifetimes, are particularly difficult to identify consistently using manual inspection. In this study, we develop a reproducible, automated detection framework based on a squeeze-a… ▽ More All-sky OH airglow imaging provides two-dimensional observations of mesospheric gravity wave structure near ~87 km altitude. Ripple-scale instability signatures, characterized by 5-15 km horizontal wavelengths and short lifetimes, are particularly difficult to identify consistently using manual inspection. In this study, we develop a reproducible, automated detection framework based on a squeeze-and-excitation convolutional neural network (SE-CNN) trained on 41 x 41 pixel image patches, to identify ripple-scale structures in 512 x 512 pixel all-sky airglow images acquired at Yucca Ridge Field Station (40.7o N, 104.9o W). The time-differenced images are normalized using a robust median-absolute-deviation (MAD) scaling procedure to mitigate star contamination and background variability. The model is trained and validated on manually annotated ripple and non-ripple patches, then evaluated using independent test subsets. The automated detection is performed using a sliding-window approach with spatial and temporal clustering criteria for event definition. At the patch level, the classifier achieves 92\% F1-score with high precision and recall. At the event level, automated detections recover approximately 90\% of manually identified ripple events while identifying additional low-amplitude occurrences. Validated against previous manual identification study, the automated detection catalog enables objective quantification of ripple occurrence frequency, seasonal modulation, and lifetime distributions. By emphasizing methodological transparency, calibration considerations, and validation metrics, this framework establishes a scalable measurement technique for systematic detection of mesospheric instability signatures in long-term airglow image archives. △ Less

Submitted 11 May, 2026; v1 submitted 3 March, 2026; originally announced March 2026.

Engineering Photoluminescence with Mie Voids

Authors: Yuchao Fu, Ilia Lykov, Sergejs Boroviks, Nai-Quan Zhu, Tianyue Li, Siarhei Zavatski, Makhlad Chahid, Olivier J. F. Martin

Abstract: Spontaneous emission, as a fundamental radiative process and a versatile information carrier, plays a vital role in light-emitting devices, optical information modulation and encryption, super-resolution fluorescence imaging. Engineering the photonic environment surrounding photon emitters enables control over their emission properties. However, simultaneously achieving precise engineering of both… ▽ More Spontaneous emission, as a fundamental radiative process and a versatile information carrier, plays a vital role in light-emitting devices, optical information modulation and encryption, super-resolution fluorescence imaging. Engineering the photonic environment surrounding photon emitters enables control over their emission properties. However, simultaneously achieving precise engineering of both excitation enhancement and quantum-yield modulation at the nanoscale remains elusive, highlighting substantial room for advancing the precise orchestrating of photoluminescence. Here, we introduce silicon Mie voids - air-defined cavities that invert the conventional solid-particle geometry - to achieve independent tuning of photoluminescence within a single subwavelength unit, while minimizing optical losses. Full-wave simulations and experiments on both gradient and uniform Mie-void arrays jointly validate this quantitative framework for spontaneous emission tuning, which disentangles excitation enhancement arising from local field confinement in air and quantum-yield enhancement resulting from strengthened emitter-resonator coupling, while confirming the accelerated radiative decay enabled by the modified optical LDOS. Leveraging this flexible mechanism, we realize a multimodal nanophotonic pattern with near-diffraction-limited pixels that encode the EPFL logo in the bright field and the SJTU logo in both dark field and photoluminescence maps. These results establish Mie voids as a powerful platform for high-density multimodal encrypted displays and open new avenues for advancing state-of-the-art nanophotonic devices. △ Less

Submitted 12 June, 2026; v1 submitted 27 January, 2026; originally announced January 2026.

Comments: 29 pages, 6 figures

Broadband Heterodyne Microwave Detection using Rydberg Atoms with High Sensitivity

Authors: Hsuan-Jui Su, Shao-Cheng Fang, Ting-An Li, Chen-Hao Chang, Yu-Chi Chen, Yi-Hsin Chen

Abstract: We present a Rydberg atom-based microwave electric field sensor that achieves extended dynamic range and enhanced sensitivity across a broad bandwidth. By characterizing the Autler-Townes (AT) splitting induced by a single-tone microwave field, we demonstrate a spectroscopic method that simultaneously extracts both the microwave frequency and electric field strength directly from the splitting pat… ▽ More We present a Rydberg atom-based microwave electric field sensor that achieves extended dynamic range and enhanced sensitivity across a broad bandwidth. By characterizing the Autler-Townes (AT) splitting induced by a single-tone microwave field, we demonstrate a spectroscopic method that simultaneously extracts both the microwave frequency and electric field strength directly from the splitting pattern. We implement dual-tone heterodyne detection, achieving a minimum detectable field strength on the order of uV/cm and a sensitivity in the sub-uV/cm/Hz^1/2 regime, while extending the operational bandwidth up to 3 GHz. Through systematic characterization of frequency and power dependencies, we identify optimal operating conditions to minimize power broadening in the resonant AT regime and maximize sensitivity in the far-off-resonance AC Stark regime. The resulting platform combines high sensitivity, broad bandwidth, and a dynamic range of approximately 90 dB, establishing Rydberg atoms as practical sensors for precision electric field metrology. △ Less

Submitted 27 January, 2026; originally announced January 2026.

Comments: 8 pages, 5 figures

New energy conversion system based on charge-exchange and inner-shell electron transitions

Authors: Tianrui Li, Yi Jiang, Chen Zhao, Bingsheng Tu, Peining Chen, Jiajun Qin, Huisheng Peng

Abstract: The rapidly growing demand for compact, high-energy power sources has outpaced the capabilities of conventional electrochemical systems that rely on outer-shell redox reactions. In this work, we present a new energy platform that utilizes inner-shell electron transitions that are previously inaccessible due to their high energy thresholds. By leveraging charge exchange processes between bare argon… ▽ More The rapidly growing demand for compact, high-energy power sources has outpaced the capabilities of conventional electrochemical systems that rely on outer-shell redox reactions. In this work, we present a new energy platform that utilizes inner-shell electron transitions that are previously inaccessible due to their high energy thresholds. By leveraging charge exchange processes between bare argon ions (Ar^18+) and neutral helium atoms, we provide clear evidence for the emission of soft X-ray and extreme-ultraviolet photons across a broad spectra range, resulting from inner-shell electron capture and cascade de-excitation. This strategy overcomes the limitations of radiative recombination by enhancing photon energy utilization through broader emission profiles more compatible with practical energy converters. Our design of a helium-filled chamber design enables precise control of output via pressure tuning, achieving a remarkable radiation power density of 6.29*10^8 W L^-1 and an unprecedented energy density of 2.64*10^6 Wh kg^-1. These results may provide a new and effective paradigm for energy conversion systems with ultra-high power and energy densities based on inner-shell electrons. △ Less

Submitted 14 January, 2026; originally announced January 2026.

Comments: 17 pages, 4 figures