Showing 1–50 of 397 results for author: Gao, Y

Tunable high-charge relativistic electron beams via direct laser acceleration in hohlraum-preheated foam targets

Authors: Ziyao Wang, Jieru Ren, Zhigang Deng, Wenqing Wei, Wei Qi, Olga N. Rosmej, Nikolay E. Andreev, Sergey Yu. Gus'kov, Rafael Yakhin, Yifang Gao, Bubo Ma, Mingzhe Yang, Shizheng Zhang, Xuyang Luo, Dieter H. H. Hoffmann, Peng Zhou, Ke Jiang, Taiwu Huang, Bo Cui, Weiwu Wang, Shaoyi Wang, Quanping Fan, Zhurong Cao, Sixin Wu, Yue Yang , et al. (6 additional authors not shown)

Abstract: Direct laser acceleration (DLA) in near-critical-density (NCD) plasmas can efficiently generate high-charge relativistic electron beams, yet beam parameters depend critically on precise plasma state manipulation. Solid-ablation NCD plasmas evolve rapidly, posing severe controllability challenges. We produce NCD plasma via indirectly heating foam targets with ns laser driven hohlraum soft X-ray. El… ▽ More Direct laser acceleration (DLA) in near-critical-density (NCD) plasmas can efficiently generate high-charge relativistic electron beams, yet beam parameters depend critically on precise plasma state manipulation. Solid-ablation NCD plasmas evolve rapidly, posing severe controllability challenges. We produce NCD plasma via indirectly heating foam targets with ns laser driven hohlraum soft X-ray. Electrons are generated through irradiating the plasma with another picosecond laser. Tuning the laser pulse delay $τ$ enables control of plasma profiles and beam parameters. Experiments show that when the foam is heated ($τ$ = 6 ns, 9 ns), the beam exhibits $T \sim 13$ MeV effective temperature, $E_k \sim 80$ MeV cutoff energy, and hundreds of nC/sr charge for $E_k > 7.5$ MeV. These values are significantly higher than those from solid-foil ($T$ $\sim$ 2.7 MeV, $E_k$ $\sim$ 20 MeV, $Q$ $\sim$ 9 nC/sr) and cold-foam ($T$ $\sim$ 12 MeV, $E_k$ $\sim$ 50 MeV, $Q$ $\sim$ 5 nC/sr) interactions. At a longer delay of $τ$ = 15 ns, the charge increases further while the temperature decreases, and at a shorter delay of $τ$ = 3 ns, both temperature and charge are lower. 3D PIC simulations link these observations to the interplay between the microstructure of the cold foam and the evolving plasma density profile at different delay times, which together determine the beam charge, effective temperature, and divergence. The finding provides a routine to generate and tailor the relativistic electron beams, which is essential for designing laser-driven electron sources for high energy density physics and photonuclear reaction applications. △ Less

Submitted 18 August, 2026; originally announced August 2026.

Roughness-controlled layer in oscillatory turbulent boundary layers over densely packed uniform roughness

Authors: Xuchen Liu, Yuan Gao, Yiyong Dong, Jing Yuan

Abstract: In coastal wave boundary layers over gravel-scale roughness, with near-bed orbital excursions ten to a hundred times the roughness height, the boundary layer is only a few roughness heights thick. A roughness-controlled layer (RCL) of the steady-flow extent two to five element heights would then leave no room for a logarithmic layer, yet experiments over densely packed marbles recover logarithmic… ▽ More In coastal wave boundary layers over gravel-scale roughness, with near-bed orbital excursions ten to a hundred times the roughness height, the boundary layer is only a few roughness heights thick. A roughness-controlled layer (RCL) of the steady-flow extent two to five element heights would then leave no room for a logarithmic layer, yet experiments over densely packed marbles recover logarithmic profiles within millimetres of the crests. We resolve this contradiction by re-analysing previous Particle Image Velocimetry (PIV) records with a triple decomposition that separates the marble-locked dispersive motion from the stochastic turbulence, across eleven wave, current and wave-current conditions. The boundary layer organises into an RCL, a transition region, and a logarithmic profile layer, with the RCL only one to two tenths of a marble diameter deep. This thinness is kinematic: above a periodic bed, the dispersive field decays over a length fixed by the element spacing, so close packing caps the layer at a fraction of a diameter. The layer is destroyed and rebuilt every half-cycle, tracking the near-bed velocity quasi-steadily, while the eddies within it stay locked to the inter-crest gap. Thinness does not imply weakness: within the layer, the dispersive kinetic energy rivals the turbulent kinetic energy, and the dispersive stress matches, near the crests exceeds, the Reynolds stress, showing that separated wakes carry organised momentum. The logarithmic layer survives because the decay length imposed by the packing is far smaller than the boundary-layer thickness, a margin set by the bed geometry rather than by the forcing. △ Less

Submitted 18 August, 2026; originally announced August 2026.

Erbium-implanted tellurite waveguides with low-temperature post-implantation activation and signal enhancement

Authors: Yuxuan Gao, Batoul Hashemi, Bruno L. Segat Frare, Niloofar Majidian Taleghani, Pooya Torab Ahmadi, Henry C. Frankis, Ponnambalam Ravi Selvaganapathy, Jonathan D. B. Bradley, Peter Mascher, Andrew P. Knights

Abstract: In this paper, we demonstrate erbium ion implantation and signal enhancement in tellurium oxide hybrid waveguides. Silicon nitride strips with a width of 2 $μ$m and a height of 100 nm were clad with a 110-nm-thick tellurium oxide layer to form hybrid waveguides, followed by erbium ion implantation at an energy of 200 keV and a dose of $1\times10^{15} ions/cm^{2}$, with a projected peak implantatio… ▽ More In this paper, we demonstrate erbium ion implantation and signal enhancement in tellurium oxide hybrid waveguides. Silicon nitride strips with a width of 2 $μ$m and a height of 100 nm were clad with a 110-nm-thick tellurium oxide layer to form hybrid waveguides, followed by erbium ion implantation at an energy of 200 keV and a dose of $1\times10^{15} ions/cm^{2}$, with a projected peak implantation depth of approximately 50 nm into the tellurium oxide layer. After low-temperature annealing at 150 °C for 30 minutes, the propagation loss decreased from 1.7 to 0.9 dB/cm, while the erbium lifetime increased from 40 $μ$m to over 800 $μ$m. We measure a small-signal enhancement of 9 dB in an 11-cm-long waveguide at a wavelength of 1550 nm. These results demonstrate progress towards a low-temperature post-ion implantation process for incorporating erbium and other rare earth ions into tellurium oxide films for integrated photonic applications. △ Less

Submitted 17 August, 2026; originally announced August 2026.

Inverse mask design for interference lithography using automatic differentiable wave propagation

Authors: Chuntian Cao, Jangwoon Sung, Jack Griffiths, Yuan Gao, Xi Yu, Paul Baity, Nikhil Tiwale, Zhitian Shi, Juhong Ahn, Shinjae Yoo, Yong S. Chu, Chang-Yong Nam

Abstract: Interference lithography (IL) is powerful for fabricating high-resolution periodic nanostructures, but designing masks to produce non-periodic patterns remains challenging. We introduce a gradient-based optimization framework for binary IL mask design using automatic differentiation. The forward model is implemented using the differentiable angular spectrum method (ASM). The inverse mask design is… ▽ More Interference lithography (IL) is powerful for fabricating high-resolution periodic nanostructures, but designing masks to produce non-periodic patterns remains challenging. We introduce a gradient-based optimization framework for binary IL mask design using automatic differentiation. The forward model is implemented using the differentiable angular spectrum method (ASM). The inverse mask design is formulated as an optimization problem, where the mask logits are updated through backpropagation of the loss between the simulated field amplitude and the target pattern. We optimize a mask that reproduces a target pattern with only 0.1% isolated pixel-level defects, resolving features at half the mask pixel pitch. To scale mask optimization, we employ the shifted ASM, which partitions the mask into patches that are propagated independently and summed at the image plane. For a 3.84 mm$\times$3.84 mm mask, shifted ASM with 16 patches reduces peak GPU memory by 3.8$\times$ at only 1.3$\times$ runtime cost relative to standard ASM. With gradient checkpointing, peak memory is reduced by 7.4$\times$ at 2$\times$ runtime. Distributing across multiple GPUs further accelerates the optimization. This work establishes a physics-informed, machine learning-driven approach for IL mask design, moving a step further towards complex, non-periodic patterns. The source code is available at https://github.com/chuntian236/holography-optimization.git . △ Less

Submitted 5 August, 2026; originally announced August 2026.

Comments: 11 pages, 4 figures. To be published in Proceedings of SPIE Optics + Photonics 2026, Optical Engineering + Applications

ScoreField: Neural Inverse Scattering with Score-Based Generative Priors

Authors: Wenhan Guo, Yuan Gao, Yu Sun

Abstract: Designing an effective electromagnetic inverse-scattering solver requires faithful enforcement of nonlinear full-wave physics together with an expressive prior on the unknown permittivity contrast. We propose ScoreField, a neural inverse scattering framework that integrates coupled implicit neural representations (INRs) with a pretrained score-based generative prior. ScoreField employs two INRs to… ▽ More Designing an effective electromagnetic inverse-scattering solver requires faithful enforcement of nonlinear full-wave physics together with an expressive prior on the unknown permittivity contrast. We propose ScoreField, a neural inverse scattering framework that integrates coupled implicit neural representations (INRs) with a pretrained score-based generative prior. ScoreField employs two INRs to parameterize the permittivity contrast and the induced current fields, and jointly optimize them under the Lippmann-Schwinger equations. In addition to the implicit regularization by the INR architecture, the score model provides a learned prior gradient on the contrast, which is propagated to the contrast INR through the chain rule. This formulation enables ScoreField to effectively handle strong multiple scattering, where nonlinear wave interactions require accurate modeling of the coupled full-wave physics. We evaluate ScoreField on simulated weak- and strong-scattering benchmarks, the canonical Austria phantom, and experimental Fresnel measurements. We note that ScoreField significantly improves reconstruction fidelity and suppresses artifacts relative to classical full-wave methods and deep learning baselines, achieving an average PSNR improvement of $1.8 \, \mathrm{dB}$ over the best competing method on real Fresnel data. △ Less

Submitted 3 August, 2026; originally announced August 2026.

Comments: 28 pages, 9 figures

High-Frequency Magnetohydrodynamic Waves with Substantial Energy in the Solar Polar Corona

Authors: Yuhang Gao, Hui Tian, Richard Morton, Tom Van Doorsselaere, Daye Lim, Mingzhe Guo, Jiansen He, Zhenyong Hou

Abstract: The acceleration and heating of the fast solar wind remain long-standing challenges in space physics. One type of leading theoretical models requires high-frequency magnetohydrodynamic (MHD) waves to transport and dissipate sufficient energy in the corona. However, such high-frequency waves with energetically significant amplitudes have never been unambiguously observed, leaving a key gap between… ▽ More The acceleration and heating of the fast solar wind remain long-standing challenges in space physics. One type of leading theoretical models requires high-frequency magnetohydrodynamic (MHD) waves to transport and dissipate sufficient energy in the corona. However, such high-frequency waves with energetically significant amplitudes have never been unambiguously observed, leaving a key gap between theories and observations. Using high-cadence, high-resolution extreme-ultraviolet imaging from Solar Orbiter's Extreme Ultraviolet Imager, we identify a previously hidden population of high-frequency MHD waves in coronal plumes of the solar polar region. An analysis of the detected propagating kink waves shows that over one-third have periods shorter than 100 s, a population largely undetected by earlier instruments. Power spectral analysis demonstrates that these high-frequency waves carry substantial energy flux, which are significantly underestimated in lower-cadence data. These results suggest that high-frequency MHD waves may contribute importantly to the energy budget of the solar polar corona and could play a role in solar wind acceleration, highlighting the value of high-resolution observations for probing energy transport in magnetized space and astrophysical plasmas. △ Less

Submitted 28 July, 2026; originally announced July 2026.

Comments: 14 pages, 4 figures, 1 table

Journal ref: National Science Review, Volume 13, Issue 14, July 2026, nwag370

EFT-Ramses: a code to simulate the effective field theory of dark energy

Authors: Nathaniel Ota Woodcock, Sownak Bose, Yunhao Gao, Baojiu Li

Abstract: While the standard $Λ$CDM paradigm is in excellent agreement with most current cosmological observations, theoretical challenges surrounding the cosmological constant ($Λ$) have strongly motivated the exploration of dynamical dark energy (DE) and modified gravity (MG) models. Investigating the physical nature of the cosmic acceleration requires N-body simulations to probe the non-linear growth of… ▽ More While the standard $Λ$CDM paradigm is in excellent agreement with most current cosmological observations, theoretical challenges surrounding the cosmological constant ($Λ$) have strongly motivated the exploration of dynamical dark energy (DE) and modified gravity (MG) models. Investigating the physical nature of the cosmic acceleration requires N-body simulations to probe the non-linear growth of cosmic structure and prepare for the high-precision data from Stage-IV surveys. In this paper, we present EFT-RAMSES, a comprehensive extension of the ECOSMOG cosmological simulation code designed to explore non-linear structure formation in DE and MG scenarios. We embed the effective field theory of dark energy (EFTofDE) framework into this new numerical pipeline, utilising the $α$-basis parameterisation to provide a versatile, model-agnostic, computational engine. By consolidating diverse scalar and vector-tensor theories---including the normal and self-accelerating Dvali-Gabadadze-Porrati (DGP) models, cubic Galileons (cubic scalar Galileon (csG), cubic vector Galileon (cvG) and generalised cubic covariant Galileon (GCCG)), and generic effective field theory (EFT) parameterisation---into a single "master" Vainshtein equation, this pipeline bypasses the need for model-specific solvers and easily specialises to any particular model. As validations, we perform high-resolution N-body simulations for the normal-branch DGP (nDGP), csG, GCCG, and EFT models, comparing the resulting matter power spectra against dependent and independent codes such as legacy ECOSMOG and HiCOLA, as well as linear theory, and find excellent agreement. EFT-RAMSES provides a robust and versatile computational tool for precision cosmological tests of DE and MG using upcoming cosmological surveys. The code is available for download from the GitHub EFT-RAMSES repository. △ Less

Submitted 27 July, 2026; originally announced July 2026.

Comments: 18 pages, 5 figures (2 more in an Appendix), submitted to MNRAS; comments welcome

Spin-Consistency Constraints in Noncollinear Tensor TDA

Authors: Wenxian Qin, Tai Wang, Yue Yu, Yuanyang Liu, Yiqin Gao, Yunlong Xiao

Abstract: TDDFT for open-shell systems, whether spin-conserving or spin-flip, has long suffered from spin contamination. This problem arises because the single-excitation space built upon a single Kohn-Sham determinant is not spin-complete. Adopting spin tensor reference states therefore offers an elegant and promising route to resolving this issue. In this work, we revisit the tensor TDDFT equations within… ▽ More TDDFT for open-shell systems, whether spin-conserving or spin-flip, has long suffered from spin contamination. This problem arises because the single-excitation space built upon a single Kohn-Sham determinant is not spin-complete. Adopting spin tensor reference states therefore offers an elegant and promising route to resolving this issue. In this work, we revisit the tensor TDDFT equations within the Tamm-Dancoff approximation (TDA) from a noncollinear perspective. We show that, for S = 1/2 reference states, the internal consistency of the spin tensor formulation can, with the aid of the zero-excitation-energy theorem, be recast as a set of constraints that the exchange-correlation kernel must satisfy. Standard noncollinear functionals, however, generally fail to meet these constraints. To address this, we propose a kernel reconstruction scheme that is independent of the specific functional form and free of empirical parameters. This scheme enforces the required constraints, restoring internal consistency in the full spin tensor structure, with spin adaptation following as a natural consequence. Furthermore, when extended to tensor reference states with other values of S, such as S = 1 for the oxygen molecule, the scheme eliminates the so-called artifact states, namely solutions with severely underestimated excitation energies. In addition, the scheme allows the target states that ROKS reference states aim to describe to be expressed and computed, at the TDA level, within the same unified framework as other states, a capability that spin-adapted spin-conserving TDDFT has so far lacked. △ Less

Submitted 22 July, 2026; originally announced July 2026.

Comments: 49 pages, 5 figures

One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy

Authors: Shaoyan Pan, Kirk Jon Luca, Yuan Gao, Shansong Wang, Mingzhe Hu, Ryan Sanford, Mojtaba Safari, Justin Roper, Zhen Tian, Tonghe Wang, Xiaofeng Yang

Abstract: In this work, we introduce the One-for-All Adaptive Radiotherapy Planning Agent, a unified foundation-model-based system that performs complete, treatment-specific online adaptive planning directly from daily cone-beam CT in under two minutes. The agent first autonomously predicts all essential planning components, including synthetic CT generation, multimodal alignment, and tumor/organ segmentati… ▽ More In this work, we introduce the One-for-All Adaptive Radiotherapy Planning Agent, a unified foundation-model-based system that performs complete, treatment-specific online adaptive planning directly from daily cone-beam CT in under two minutes. The agent first autonomously predicts all essential planning components, including synthetic CT generation, multimodal alignment, and tumor/organ segmentation. It then intelligently leverages these outputs to execute the final clinical plan design, providing a comprehensive, automated solution for daily treatment. We also demonstrate that the agent enables clinicians to define planning with intent and intervene at critical decision points, ensuring a "human-in-the-loop" framework that generates acceptable plans before final approval. Evaluated on multiple datasets spanning head-and-neck, lung, abdominal, and prostate cancers with both photon and proton therapy, the proposed framework achieves clinically acceptable accuracy and plan quality comparable to clinically generated treatment plans, with target dose errors (D98) generally within 2.0 Gy of the reference plan. The strong performance of the One-for-All agent highlights the promise of a unified foundation-model approach and opens opportunities for fast, scalable, and fully automated online adaptive radiotherapy across diverse clinical scenarios. △ Less

Submitted 16 July, 2026; originally announced July 2026.

Impact of Residual Angular Chirp in a Petawatt-class Laser System on Laser-driven Proton Acceleration

Authors: Qingfan Wu, Minjian Wu, Jiarui Zhao, Ying Gao, Haoran Chen, Tan Song, Zhongshuai Zhang, Zhangyi Wu, Tianhao Liang, Shirui Xu, Ziyang Peng, Hui Zhang, Tianqi Xu, Qihang Han, Chenghao Hua, Ke Chen, Pengcheng Fan, Yuntian Xie, Xianduo Li, Peiqiang Liu, Xiangyu Nong, Shengxuan Xu, Liyong Ma, Yixing Geng, Chen Lin , et al. (3 additional authors not shown)

Abstract: Laser-driven proton acceleration has attracted considerable interest owing to its appealing potential in versatile applications including cancer therapy. Proton energies depend critically on the on-target intensities, yet the detrimental impact of focal spot degradation induced by spatiotemporal couplings on the acceleration remains insufficiently elucidated. In this study, we demonstrate that res… ▽ More Laser-driven proton acceleration has attracted considerable interest owing to its appealing potential in versatile applications including cancer therapy. Proton energies depend critically on the on-target intensities, yet the detrimental impact of focal spot degradation induced by spatiotemporal couplings on the acceleration remains insufficiently elucidated. In this study, we demonstrate that residual angular chirp (AC), stemming from minor misalignments of the grating compressor in a Petawatt-class laser system, acts as a critical bottleneck for proton acceleration. Experimental results reveal that even around 100 microradians of grating misalignment induces substantial focal-spot elongation and a pronounced reduction in peak intensity. By implementing an in situ spectral-blocking diagnostic, we effectively eliminated the residual AC and restored a near-diffraction-limited focus. This optimization led to a significant recovery of the on-target intensity, resulting in a twofold increase in the proton cutoff energy. Our work presents a successful demonstration of diagnosing and eliminating residual AC. This provides a practical reference for generating high-energy proton beams and supporting their diverse applications in a PW-class laser. △ Less

Submitted 14 July, 2026; originally announced July 2026.

Comments: Accepted by Matter and Radiation at Extremes (MRE)

Full-Path Nonlinear Modeling of Microwave Power Transmission Through Ionospheric Plasma for Space Solar Power Station

Authors: Pengan Guo, Lei Chang, Yuhan Chen, Ya Gao, Longshuai Ye, Jikai Sun, Huaiqing Zhang, Jian Li

Abstract: Space Solar Power Station (SSPS) concepts rely on gigawatt-class microwave beams to carry orbital solar energy through the ionosphere, where the beam and the plasma form a coupled nonlinear system: the field heats electrons, the heating alters the collision frequency and plasma density, and the modified medium in turn reshapes the field. To our knowledge, this work is the first study to quantify t… ▽ More Space Solar Power Station (SSPS) concepts rely on gigawatt-class microwave beams to carry orbital solar energy through the ionosphere, where the beam and the plasma form a coupled nonlinear system: the field heats electrons, the heating alters the collision frequency and plasma density, and the modified medium in turn reshapes the field. To our knowledge, this work is the first study to quantify this two-way interaction between microwave power transmission and the ionospheric plasma environment through full-path nonlinear modeling. The 340 km path from 400 km to 60 km altitude is reconstructed by 34 cascaded two-dimensional axisymmetric finite-element full-wave segments with complex-field transfer, using International Reference Ionosphere (IRI) electron-density and NRLMSISE-00 neutral-atmosphere inputs. A Shallow Neural Network (SNN) surrogate replaces the implicit electron energy balance with an explicit closure that maps altitude and local field magnitude to electron temperature and effective collision frequency, enabling stable nonlinear iteration. For 1 GW beams at 2.45 GHz and 5.8 GHz, the volume-integrated Ohmic deposition is 29.4 kW and 5.11 kW, respectively -- fractional losses of order $10^{-5}$ -- and the ratio between the two bands follows the $ω^{-2}$ scaling of collisional absorption. The deposition concentrates near 95 km altitude, where the product of electron density and collision frequency peaks, whereas the electron-temperature perturbation (up to 3815 K) maximizes in the F region, where cooling is weakest; ponderomotive density depletion remains below 0.02\%. The ionosphere is therefore effectively transparent to the SSPS power budget but not to the beam phase: localized heating and refractive perturbation accumulate phase-front distortion relevant to phased-array beam control, rectenna phase compensation, and environmental assessment. △ Less

Submitted 13 July, 2026; originally announced July 2026.

Comments: 27 pages, 13 figures

Electron-beam Writing of Spectrally Uniform Green Single-photon Emitters in Hexagonal Boron Nitride

Authors: Qingsong Tao, Fuyi Zhou, Zhijie Li, Yihao Yan, Shuangyue Li, Yuelan Gao, Zijing Wu, Yizhou Liu, Tao Liang, Shuai Yuan, Dakun Wu, Hongzhi Zhou, Qi Zhang, Zhenyi Ni, Chunlei Yu, Pan Wang, Fei Yu, Lili Hu, Ning Zhou

Abstract: Scalable quantum photonic technologies require single-photon emitters whose positions and emission energies can be engineered simultaneously. Hexagonal boron nitride (hBN) is an attractive room-temperature host, but deterministic creation of spectrally reproducible emitters remains challenging. Here, we use a standard scanning electron microscope as a direct-writing tool to activate bright green s… ▽ More Scalable quantum photonic technologies require single-photon emitters whose positions and emission energies can be engineered simultaneously. Hexagonal boron nitride (hBN) is an attractive room-temperature host, but deterministic creation of spectrally reproducible emitters remains challenging. Here, we use a standard scanning electron microscope as a direct-writing tool to activate bright green single-photon emitters in hBN at predefined sites, without ion implantation or post-fabrication thermal annealing. The written emitters exhibit reproducible zero-phonon-line emission centered near 536 nm, room-temperature antibunching with g(2)(0) as low as 0.08, high brightness, strong linear polarization, and stable emission. Thickness-dependent activation, stacking experiments, cathodoluminescence spectroscopy, and first-principles calculations support a carbon-related defect complex as the most plausible origin of the emission. As a proof of nanophotonic compatibility, we further activate emitters in a nanoparticle-on-mirror plasmonic nanocavity and observe photoluminescence enhancement accompanied by shortened emission lifetimes. These results establish electron-beam direct writing as a practical route to site-selective, spectrally uniform green quantum emitters in hBN, offering a promising basis for integrated room-temperature quantum photonic architectures. △ Less

Submitted 2 July, 2026; originally announced July 2026.

Observation of stopping power reduction at strong ion-plasma coupling

Authors: Yun Liu, Jieru Ren, Zhigang Deng, Wei Qi, Bubo Ma, Wenqing Wei, Shizheng Zhang, Xuyang Luo, Ziqian Zhao, Mingzhe Yang, Yifang Gao, Xueguang Ren, Jianxing Li, Dieter H. H. Hoffmann, Xing Wang, Zhongfeng Xu, Shaoyi Wang, Quanping Fan, Bo Cui, Weiwu Wang, Sixin Wu, Yue Yang, Zhurong Cao, Zongqing Zhao, Yuqiu Gu , et al. (8 additional authors not shown)

Abstract: Ion stopping in dense plasma is crucial for stellar evolution and fusion ignition. However, its behavior in the strong ion-plasma coupling regime beyond the linear limit has long remained elusive, due to formidable experimental challenges. Here we report the first experimental investigation of ion stopping at an unprecedented coupling parameter exceeding unity, achieved by sending laser-accelerate… ▽ More Ion stopping in dense plasma is crucial for stellar evolution and fusion ignition. However, its behavior in the strong ion-plasma coupling regime beyond the linear limit has long remained elusive, due to formidable experimental challenges. Here we report the first experimental investigation of ion stopping at an unprecedented coupling parameter exceeding unity, achieved by sending laser-accelerated short-pulse and intense quasi-monoenergetic carbon ions ($\sim$583 keV/u, C$^{5+}$) into a uniform, long-lived, well-characterized dense plasma target ($T_e$ $\approx$ 17 eV, $n_e$ $\approx$ 4$\times$10$^{20}$ cm$^{-3}$). By simultaneously measuring ion energy loss and charge-state evolution, we eliminated key experimental ambiguities arising from charge-state determination. Our results clearly show a reduction in stopping power compared with predictions from standard linear dielectric response or binary collision models, and they agree well with the hybrid calculation of molecular dynamics with quantum corrections. The importance of nonlinear screening effects arising from many-body interactions and quantum effects due to the wave nature of electrons was demonstrated at strong coupling. This work establishes a definitive high-fidelity experimental benchmark for collisional dynamics in the strong-coupling regime. It offers critical insight for accurate modeling of energy transport in inertial confinement fusion and astrophysical plasmas. △ Less

Submitted 15 July, 2026; v1 submitted 22 June, 2026; originally announced June 2026.

Reaction-Network-Level Discovery of Ammonia Synthesis Catalysts via Ten-Million-Scale Generative Exploration

Authors: Ruili Li, Rui Qi, Shuoqi Zhang, Qingli Tang, Qingqing Mao, Ritankar Das, Beien Zhu, Yi Gao

Abstract: Catalyst discovery for ammonia synthesis is inherently a reaction-network challenge because catalytic performance is governed not by a single adsorbed intermediate, but by a surface's orchestrated compatibility with multiple distinct intermediates across competing dissociative and associative pathways. However, navigating ultra-large chemical spaces under such multi-intermediate constraints remain… ▽ More Catalyst discovery for ammonia synthesis is inherently a reaction-network challenge because catalytic performance is governed not by a single adsorbed intermediate, but by a surface's orchestrated compatibility with multiple distinct intermediates across competing dissociative and associative pathways. However, navigating ultra-large chemical spaces under such multi-intermediate constraints remains a formidable bottleneck for conventional screening workflows. Here, we report a reaction-network-level catalyst discovery framework driven by ten-million-scale generative exploration. By coupling adsorbate-specific generative Transformers with high-throughput machine learning potentials, we systematically map the structure-property landscapes of four critical intermediates (N*, NH*, NNH*, and HNNH*). Scale-dependent overlap analysis shows that the full four-intermediate compatibility space remains strongly under-sampled at conventional 105-106 generative scales, emerging exclusively under ten-million-scale exploration. By generating approximately 15 million configurations per adsorbate, followed by structural compression and machine-learning-potential predictions, we identified 279 highly potential target materials. This sparse compatibility space successfully recovers traditional Fe- and Ru-based motifs while uncovering previously unexplored catalyst families. Representative DFT calculations validate pathway-dependent mechanisms: Fe-V emerges as a dissociative-pathway lead by significantly lowering the initial N2 dissociation barrier, whereas Al-Pd-Zr efficiently stabilizes associative intermediates as an associative-pathway lead. These findings establish multi-intermediate reaction-network compatibility as a robust criterion for discovering advanced catalysts from multi-million generative chemical spaces. △ Less

Submitted 7 July, 2026; v1 submitted 22 June, 2026; originally announced June 2026.

Hybrid NARX-LLM for Greenland Iceberg Discharge: Prompt-Driven Residual Correction

Authors: Yiquan Gao, Duohui Xu

Abstract: Greenland iceberg discharge exhibits complex nonlinear dynamics with limited observability, challenging traditional predictive models. We present a Hybrid NARX-LLM framework that combines a nonlinear autoregressive model with exogenous inputs (NARX) and a large language model (LLM) for residual correction. We further propose a Physics-Informed Prompt (PIP) method that transforms unstructured physi… ▽ More Greenland iceberg discharge exhibits complex nonlinear dynamics with limited observability, challenging traditional predictive models. We present a Hybrid NARX-LLM framework that combines a nonlinear autoregressive model with exogenous inputs (NARX) and a large language model (LLM) for residual correction. We further propose a Physics-Informed Prompt (PIP) method that transforms unstructured physical knowledge into structured prompts for zero-shot in-context reasoning. The primary objective is to explore the corrective potential of this framework for modeling Greenland iceberg discharge, rather than merely optimizing predictive accuracy. The NARX component captures intrinsic temporal dependencies, while the LLM, guided by PIP, encodes glacier dynamics and environmental drivers and perceives key trend patterns to correct systematic prediction errors. This integration allows the model to reason about unmodeled factors and produce interpretable residuals, enhancing overall predictive accuracy. Applied to Greenland iceberg discharge time series, our approach addresses extreme events that are difficult to predict due to rare variations and nonstationary trends, a limitation often overlooked by traditional methods. By fusing structured time-series modeling with knowledge-driven foundation AI, the framework offers a scalable and interpretable pathway to bridge data-limited climate forecasting with physics-informed LLM reasoning. The code is available. △ Less

Submitted 13 June, 2026; originally announced June 2026.

ChatMOSP: A Chemistry-Grounded Mobile Agent for Working-State Catalyst Simulations

Authors: Sanyang Ye, Rui Qi, Beien Zhu, Yi Gao

Abstract: Catalytic nanoparticles restructure dynamically under reaction conditions, so their working morphology and activity are governed by temperature, pressure, and gas composition. However, converting experimentally specified environments into physically meaningful morphology-performance simulations remains difficult because the translation of reaction conditions into model-specific energetic, kinetic,… ▽ More Catalytic nanoparticles restructure dynamically under reaction conditions, so their working morphology and activity are governed by temperature, pressure, and gas composition. However, converting experimentally specified environments into physically meaningful morphology-performance simulations remains difficult because the translation of reaction conditions into model-specific energetic, kinetic, and execution parameters requires the specialized knowledge in computational catalysis. Here we report ChatMOSP, a chemistry-grounded mobile scientific agent that translates natural-language and voice-expressed catalytic requests into parameter-validated simulations using the Multi-scale Operando Simulation Package. ChatMOSP maps catalyst identity, temperature, pressure, gas composition, and target observables onto multiscale structure reconstruction and kinetic Monte Carlo tasks, retrieves database parameters or constructs missing inputs from an online literature-retrieval workflow, and executes validated MOSP workflows. Using CO oxidation on Pd nanoparticles as an example, we verify the ChatMOSP simulations capture the temperature-induced transition from faceted to rounded morphologies observed by in-situ TEM experiments either by built-in database or from web-retrieved literature information when the parameters are absent. Moreover, we demonstrate the capability of ChatMOSP to perform end-to-end study at mobile devices to simulate a pressure-coverage-morphology-activity feedback cycle for Pt CO oxidation to interpret the oscillatory CO conversion. These results establish ChatMOSP as a physically constrained mobile agent for accessible and interpretable catalyst working-state simulations. △ Less

Submitted 22 May, 2026; originally announced May 2026.

Comments: 26 pages, 5 figures

Physics-informed neural networks for quantitative assessment of cancellous bone microstructure from photoacoustic signals

Authors: Shoukun Lyu, Haohan Sun, Shibo Nie, Weiya Xie, Ying Gu, Shiying Wu, Ya Gao, Qian Cheng

Abstract: Artificial intelligence (AI) empowers innovative diagnostic tools for common diseases, yet its clinical application in skeletal health evaluation is constrained by unsatisfactory accuracy, owing to the inherent porous and poroelastic biophysical features of bone. To address such bottlenecks amid global population aging, this study targets skeletal health and develops a reliable AI framework for pr… ▽ More Artificial intelligence (AI) empowers innovative diagnostic tools for common diseases, yet its clinical application in skeletal health evaluation is constrained by unsatisfactory accuracy, owing to the inherent porous and poroelastic biophysical features of bone. To address such bottlenecks amid global population aging, this study targets skeletal health and develops a reliable AI framework for precise bone microstructural characterization. We proposed Biot-PINN, a physics-informed neural network embedded with Biot's poroelasticity theory to characterize mechanical responses and wave propagation in poroelastic bone tissues. By decoding photoacoustic signals encoding bone mineral and microstructural features, the framework enables automatic bone microstructural grading. Experimental results reveal that Biot-PINN reaches an accuracy of 97%, markedly surpassing traditional data-driven approaches and providing a robust solution for early skeletal health diagnosis. △ Less

Submitted 20 May, 2026; originally announced May 2026.

Comments: 20 pages, 4 figures, 1 table

HotLoop Optimization of Petawatt Laser Focal Spot via a Twin-Focus Scheme

Authors: Qingfan Wu, Ying Gao, Minjian Wu, Jiarui Zhao, Shiyou Chen, Tianhao Liang, Haoran Chen, Tan Song, Zhongshuai Zhang, Zhangyi Wu, Shirui Xu, Ziyang Peng, Tianqi Xu, Zhuo Pan, Yujia Zhang, Qihang Han, Ke Chen, Chenghao Hua, Pengcheng Fan, Yuntian Xie, Yifei Shen, Shengxuan Xu, Liyong Ma, Yixing Geng, Chen Lin , et al. (3 additional authors not shown)

Abstract: Achieving diffraction-limited focusing of high-power laser pulses to generate ultra-high intensities is crucial for developing compact laser-driven particle accelerators and exploring strong-field quantum electrodynamics. However, accurately diagnosing and optimizing the focal spots of petawatt (PW) laser pulses remains a significant challenge. In this work, we present an experimental methodology… ▽ More Achieving diffraction-limited focusing of high-power laser pulses to generate ultra-high intensities is crucial for developing compact laser-driven particle accelerators and exploring strong-field quantum electrodynamics. However, accurately diagnosing and optimizing the focal spots of petawatt (PW) laser pulses remains a significant challenge. In this work, we present an experimental methodology utilizing a twin-focus scheme to precisely characterize the intensity distribution and wavefront of focused PW femtosecond laser pulses, and employ it to elucidate their power-dependent evolution. Furthermore, we optimized the focal spots at full power via our in-situ wavefront correction method termed ``HotLoop', achieving a Strehl ratio of 0.80 for 1 PW laser pulses. Consequently, the cutoff proton energies in laser proton acceleration experiments were significantly enhanced. The success of this approach underscores the necessity of in-situ high-energy wavefront correction for ultra-high intensity laser-matter interactions. △ Less

Submitted 19 May, 2026; originally announced May 2026.

Comments: Accepted by Matter and Radiation at Extremes (MRE)

State-resolved electron capture in low-energy Ar2+-Ar/N2 collisions

Authors: Shucheng Cui, Dadi Xing, Xiaolong Zhu, Dongmei Zhao, Dalong Guo, Yong Gao, Shaofeng Zhang, Chenzhong Dong, Xinwen Ma

Abstract: As a fundamental process in atomic physics, charge exchange relies on quantum state-resolved data that is crucial for various fields such as astrophysics and plasma physics. However, there remains a g in the research on multi-electron target systems. This study aims to investigate the dynamic mechanisms of single/double electron capture in collisions between Ar2+ ions and Ar atoms or N2 molecules… ▽ More As a fundamental process in atomic physics, charge exchange relies on quantum state-resolved data that is crucial for various fields such as astrophysics and plasma physics. However, there remains a g in the research on multi-electron target systems. This study aims to investigate the dynamic mechanisms of single/double electron capture in collisions between Ar2+ ions and Ar atoms or N2 molecules at an energy of 40 keV, thereby supplementing high-precision experimental data in this field. The experiment is conducted on the electron beam ion source (EBIS) platform at the Institute of Modern Physics, Chinese Academy of Sciences, using the cold target recoil ion momentum spectroscopy (COLTRIMS) technique. An ion beam containing ground-state Ar2+ (3s^2 3p^(4 3) P) and metastable Ar2+ (3s^2 3p^(4 1) D,(_^1)S) is used as the projectile, colliding with a supersonic Ar/ N2 mixed gas target. Three-dimensional momentum of recoil ions is reconstructed through coincidence measurements of recoil ions and scattered ions, and the Q-value and scattering angle distribution are calculated. Theoretical comparisons are performed using the molecular Coulombic over barrier model (MCBM). △ Less

Submitted 7 May, 2026; originally announced May 2026.

Journal ref: Acta Phys. Sin. 2025, 74(24):240702

TDDFT Gradients and Nonadiabatic Couplings with Minimal Auxiliary Basis Set Approximation for Fewest-Switches Surface Hopping Dynamics

Authors: Cheng Fan, Zhichen Pu, Zehao Zhou, Yuanheng Wang, Yi Qin Gao, Qiming Sun

Abstract: The electronic structure calculations remain a major bottleneck in ab initio nonadiabatic molecular dynamics. We develop an efficient TDDFT-based FSSH implementation in the GPU4PySCF package for medium-sized molecular systems. Our approach combines density fitting, TDDFT with minimal auxiliary basis sets (TDDFT-ris), and an approximate Z-vector solver to reduce the computational cost of TDDFT exci… ▽ More The electronic structure calculations remain a major bottleneck in ab initio nonadiabatic molecular dynamics. We develop an efficient TDDFT-based FSSH implementation in the GPU4PySCF package for medium-sized molecular systems. Our approach combines density fitting, TDDFT with minimal auxiliary basis sets (TDDFT-ris), and an approximate Z-vector solver to reduce the computational cost of TDDFT excited states and derivative coupling calculations. These approximations introduce negligible errors in realistic FSSH workloads while maintaining high computational efficiency. Benchmark results show that, for 73-atom systems with a triple-$ζ$ basis set, individual electronic structure calculations are completed within one minute on a single NVIDIA A100 GPU. △ Less

Submitted 7 May, 2026; originally announced May 2026.

A Physics-Constrained Learning Framework for Wave Propagation in Complex Poroelastic Multilayered Media

Authors: Ya Gao, Yifan Wang, Yiming Chen, Haohan Sun, Shoukun Lyu, Junmei Cao, Weijiang Xu, Qian Cheng

Abstract: Wave propagation through complex poroelastic multilayered media is difficult to model and invert because pronounced heterogeneity, scattering, mode conversion and fluid-solid coupling jointly distort acoustic signals during propagation. Here we present Physics-Constrained Learning for Complex Multilayered Media (PCL-CMM), a general framework that integrates Biot's poroelastic theory with the elast… ▽ More Wave propagation through complex poroelastic multilayered media is difficult to model and invert because pronounced heterogeneity, scattering, mode conversion and fluid-solid coupling jointly distort acoustic signals during propagation. Here we present Physics-Constrained Learning for Complex Multilayered Media (PCL-CMM), a general framework that integrates Biot's poroelastic theory with the elastic wave equation to bridge the gap between physically rigorous wave modelling and data-driven learning. PCL-CMM constructs a high-fidelity digital twin that dynamically computes an effective acoustic stiffness tensor for forward wave modelling and incorporates the resulting physical constraint as a loss term to regularize the training of deep neural networks. We demonstrate PCL-CMM on transcranial photoacoustic imaging, where skull-induced acoustic distortions severely degrade image formation. Across simulations and ex vivo experiments, PCL-CMM effectively compensates for these distortions and improves SSIM by more than 0.06 compared with purely data-driven neural networks. This work establishes a physics-constrained learning framework for acoustic wave modelling in complex poroelastic multilayered media. △ Less

Submitted 6 May, 2026; originally announced May 2026.

Comments: 30 pages,8 figures,2 tables

A Spatial-Resolved Proton Energy Spectrometer Based on a Scintillation-Fiber Cube

Authors: Tan Song, Ying Gao, Di Wang, Yujia Zhang, Jiarui Zhao, Qingfan Wu, Zhuo Pan, Shirui Xu, Ziyang Peng, Yulan Liang, Tianqi Xu, Zihao Zhang, Haoran Chen, Qihang Han, Xuan Liu, Ye Yang, Maocheng Wang, Siguang Wang, Yihua Yan, Zhongming Wang, Wenjun Ma

Abstract: Advanced particle acceleration methods have produced high-peak-current ion beams with broad energy spread and complex spatial distribution. There is an urgent need to develop online spatial-resolved energy spectrometers for high-energy pulsed ions. This paper introduces a novel spectrometer based on a scintillation-fiber cube for online diagnosis of proton beams with broadband energy spread and co… ▽ More Advanced particle acceleration methods have produced high-peak-current ion beams with broad energy spread and complex spatial distribution. There is an urgent need to develop online spatial-resolved energy spectrometers for high-energy pulsed ions. This paper introduces a novel spectrometer based on a scintillation-fiber cube for online diagnosis of proton beams with broadband energy spread and complex spatial distribution. We present its working principles, experimental setup, and comprehensive calibration using monoenergetic and spatially uniform proton beams generated by a synchrotron accelerator. Calibration results confirm an energy measurement range of 6-93 MeV, a relative energy uncertainty of 0.6% at 80 MeV, and a pixel size of 0.5 mm for beam profile reconstruction. By exploiting a custom-designed energy degrader, we generated a complex proton beam and measured it with the scintillation-fiber cube spectrometer (SFICS). The results demonstrate the spectrometer's potential for online measurement of the energy spectrum and spatial distribution of complex proton beams. △ Less

Submitted 22 April, 2026; originally announced April 2026.

Comments: 21 pages, 12 figures

PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research

Authors: Tingjia Miao, Wenkai Jin, Muhua Zhang, Jinxin Tan, Yuelin Hu, Tu Guo, Jiejun Zhang, Yuhan Wang, Wenbo Li, Yinuo Gao, Shuo Chen, Weiqi Jiang, Yayun Hu, Zixing Lei, Xianghe Pang, Zexi Liu, Yuzhi Zhang, Linfeng Zhang, Kun Chen, Wei Wang, Weinan E, Siheng Chen

Abstract: The paradigm of agentic science requires AI systems to conduct robust reasoning and engage in long-horizon, autonomous exploration. However, current scientific benchmarks remain confined to domain knowledge comprehension and complex reasoning, failing to evaluate the exploratory nature and procedural complexity of real-world research. In this work, we present research-oriented evaluations in theor… ▽ More The paradigm of agentic science requires AI systems to conduct robust reasoning and engage in long-horizon, autonomous exploration. However, current scientific benchmarks remain confined to domain knowledge comprehension and complex reasoning, failing to evaluate the exploratory nature and procedural complexity of real-world research. In this work, we present research-oriented evaluations in theoretical and computational physics, a natural testbed with comprehensive domain knowledge, complex reasoning, and verifiable end-to-end workflows without reliance on experiments. Here we introduce PRL-Bench (Physics Research by LLMs), a benchmark designed to systematically map the capability boundaries of LLMs in executing end-to-end physics research. Constructed from 100 curated papers from the latest issues of Physical Review Letters since August 2025 and validated by domain experts, PRL-Bench covers five major theory- and computation-intensive subfields of modern physics: astrophysics, condensed matter physics, high-energy physics, quantum information, and statistical physics. Each task in the benchmark is designed to replicate the core properties of authentic scientific research, including exploration-oriented formulation, long-horizon workflows, and objective verifiability, thereby reconstructing the essential reasoning processes and research workflows of real physics research. Evaluation across frontier models shows that performance remains limited, with the best overall score below 50, revealing a pronounced gap between current LLM capabilities and the demands of real scientific research. PRL-Bench serves a reliable testbed for accessing next generation AI scientists advancing AI systems toward autonomous scientific discovery. △ Less

Submitted 16 April, 2026; originally announced April 2026.

Comments: 15 pages, 5 figures

Symmetry-protected coexistence of a nodal surface and multiple types of Weyl fermions in $P6_3$-$\text{B}_{30}$

Authors: Xiao-Jing Gao, Yanfeng Ge, Yan Gao

Abstract: The coexistence of topological states with different dimensionalities in a single crystalline system offers a unique platform to study the interplay of distinct fermionic excitations. Here, integrating first-principles calculations with symmetry analysis, we propose the three-dimensional boron allotrope $P6_3$-$\text{B}_{30}$ as an ideal, structurally stable candidate for exploring multidimensiona… ▽ More The coexistence of topological states with different dimensionalities in a single crystalline system offers a unique platform to study the interplay of distinct fermionic excitations. Here, integrating first-principles calculations with symmetry analysis, we propose the three-dimensional boron allotrope $P6_3$-$\text{B}_{30}$ as an ideal, structurally stable candidate for exploring multidimensional topological physics. Benefiting from the practically negligible spin-orbit coupling of the light-element framework, $P6_3$-$\text{B}_{30}$ operates as a pristine spinless topological semimetal. We show that the combined time-reversal and twofold screw symmetry ($\mathcal{T}S_{2z}$) enforces a robust two-dimensional nodal surface on the $k_z = π$ plane via a Kramers-like degeneracy. Concurrently, the system hosts a diverse set of zero-dimensional Weyl fermions -- including an unconventional double-Weyl point ($\mathcal{C} = -2$), conventional Type-I WPs ($\mathcal{C} = -1$), and completely tilted Type-II WPs ($\mathcal{C} = +1$) -- emerging at the high-symmetry points $Γ$ and K, as well as along the H-K path, protected by $C_6$ and $C_3$ crystalline rotational symmetries. Crucially, the substantial momentum-space separation between the nodal surface and Weyl points allows for their unambiguous independent resolution. Calculations of the (100) surface states reveal distinct, nontrivial Fermi arcs connecting Weyl nodes of opposite chirality. This work establishes $P6_3$-$\text{B}_{30}$ as a compelling material platform for investigating the physics of multidimensional hybrid topological fermions and their interplay. △ Less

Submitted 15 April, 2026; originally announced April 2026.

Comments: 4 figures

GPU Accelerated Minimal Auxiliary Basis Approach TDDFT for Large Organic Molecules

Authors: Zehao Zhou, Xiaojie Wu, Yanheng Li, Xinran Wei, Cheng Fan, Fusong Ju, Qiming Sun, Yi Qin Gao

Abstract: We introduce a GPU-accelerated implementation of time-dependent density functional theory with the minimal auxiliary basis approach (TDDFT-risp) in GPU4PySCF, together with large system demonstrations carried out using the Tamm--Dancoff approximation (TDA-risp). The method combines GPU-accelerated three-center integral evaluation, tensor contractions, exchange-space truncation, omission of hydroge… ▽ More We introduce a GPU-accelerated implementation of time-dependent density functional theory with the minimal auxiliary basis approach (TDDFT-risp) in GPU4PySCF, together with large system demonstrations carried out using the Tamm--Dancoff approximation (TDA-risp). The method combines GPU-accelerated three-center integral evaluation, tensor contractions, exchange-space truncation, omission of hydrogen atoms from the auxiliary basis, and a host memory assisted Davidson solver. On the EXTEST42 benchmark set, a conservative 40 eV exchange cutoff yields excitation-energy errors relative to standard TDA of about 0.03--0.05 eV for low-lying states. For systems of 300 to 3000 atoms, we demonstrate that TDA-risp calculations of 15 low-lying excited states with $ω$B97XD/def2-SVP complete on a single A100 GPU with wall times ranging from minutes to hours. These results position GPU-TDDFT-risp as a practical route toward excited-state calculations for large organic and biomolecular systems with thousands of atoms. △ Less

Submitted 31 March, 2026; originally announced March 2026.

Comments: 21 pages, 12 figures

Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials

Authors: Rajni Chahal-Crockett, Michael D. Toomey, Logan T. Kearney, Yawei Gao, Joshua T. Damron, Amit K. Naskar, Santanu Roy

Abstract: Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li+, by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energie… ▽ More Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li+, by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH- dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li+-coupled electron transfer along the PAN backbone, causing ~10,000 times faster sequential ring-formation of the remaining nitriles. PAN's extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications. △ Less

Submitted 25 March, 2026; originally announced March 2026.

Comments: 8 pages, 4 figures

Cylindrical Metasurface for Efficient Traveling-wave MRI at 7 T

Authors: Kristina I. Popova, Georgiy A. Solomakha, Zicheng Wen, Mikhail M. Popov, Xiatong Zhang, Stanislav B. Glybovski, Yang Gao

Abstract: This research focuses on the design and evaluation of an ultrathin cylindrical metasurface for improving the transmit efficiency of traveling-wave magnetic resonance imaging (MRI) of the human brain. To improve efficiency, we matched a travelling waveguide mode to an electrically large, lossy dielectric load using a thin cylindrical metasurface, which occurs to be a task closely related to impedan… ▽ More This research focuses on the design and evaluation of an ultrathin cylindrical metasurface for improving the transmit efficiency of traveling-wave magnetic resonance imaging (MRI) of the human brain. To improve efficiency, we matched a travelling waveguide mode to an electrically large, lossy dielectric load using a thin cylindrical metasurface, which occurs to be a task closely related to impedance matching in waveguide circuits in the microwave. This metasurface was designed as a compact and lightweight replacement for a high-permittivity dielectric waveguide previously proposed for the same purpose. The dispersion analysis showed that both structures (waveguide and metasurface) support a similar type of slow-wave propagation, characterized by a uniform magnetic field profile close to the cylinder axis. At the Larmor frequency, the longitudinal wavenumbers showed close agreement. Based on the optimized unit cell geometry of the periodic copper strip grid loaded with PCB capacitors, full numerical model of the cylindrical metasurface in the presence of a voxel human body model was constructed. We also compared the proposed metasurface with the dielectric waveguide in the traveling-wave setup experimentally, including in vivo measurements performed on a healthy volunteer. The proposed metasurface showed improved B1 + homogeneity (by 17.3%), transmit efficiency (by 27.4%), and SAR-efficiency (by 23%) compared to the dielectric waveguide. The proposed cylindrical metasurface, optimized for field enhancement in the human brain at 7 T in the traveling-wave excitation regime, can further improve the transmit efficiency and homogeneity in the region of interest compared to state-of-the-art structures for traveling-wave MRI, at the same time, granting the advantages of light weight and compactness. △ Less

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

UniMatSim: A High-Throughput Materials Simulation Automation Framework Based on Universal Machine Learning Potentials

Authors: Yanjin Xiang, Yihan Nie, Yunzhi Gao, Haidi Wang, Wei Hu

Abstract: Universal machine learning interatomic potentials (UMLIPs) offer accuracy close to first-principles calculations at a fraction of the cost, showing significant potential for large-scale material simulations. However, the fragmented UMLIPs ecosystem lacks unified interface standards and integration frameworks, hindering their automated deployment in high-throughput workflows. To address this, we pr… ▽ More Universal machine learning interatomic potentials (UMLIPs) offer accuracy close to first-principles calculations at a fraction of the cost, showing significant potential for large-scale material simulations. However, the fragmented UMLIPs ecosystem lacks unified interface standards and integration frameworks, hindering their automated deployment in high-throughput workflows. To address this, we present UniMatSim, a modular Python framework. It systematically integrates various UMLIPs (e.g., CHGNet, M3GNet, MACE) and automates workflows from structural optimization to stability verification. The framework enables seamless model switching via abstracted interfaces, incorporates task orchestration, and provides standardized modules for key properties (elasticity, phonons, molecular dynamics), including automated handling for low-dimensional materials. As a test case, using the 2D Lieb lattice system, we constructed a multi-stage high-throughput screening workflow covering structural optimization, elastic stability, and phonon spectrum calculations. Starting from 1,176 candidate compositions, a four-model consensus pipeline yields 393 stable structures. These are refined by magnetic-state screening and DFT band-structure calculations to 59 Lieb-lattice candidates with staggered-magnetic-band characteristics. Results show UniMatSim significantly improves computational efficiency and reproducibility, providing a reliable infrastructure for data-driven materials discovery and design. △ Less

Submitted 11 March, 2026; originally announced March 2026.

Comments: 14 pages main text, 17 pages Supporting Information, 4 figures

The Python Simulations of Chemistry Framework: 10 years of an open-source quantum chemistry project

Authors: Qiming Sun, Matthew R Hermes, Xiaojie Wu, Huanchen Zhai, Xing Zhang, Abdelrahman M. Ahmed, Juan José Aucar, Oliver J. Backhouse, Samragni Banerjee, Peng Bao, Nikolay A. Bogdanov, Kyle Bystrom, Frédéric Chapoton, Ning-Yuan Chen, Ivan Yu. Chernyshov, Helen S. Clifford, Sander Cohen-Janes, Zhi-Hao Cui, Yann D. Damour, Nike Dattani, Linus Bjarne Dittmer, Sebastian Ehlert, Janus Juul Eriksen, Francesco A. Evangelista, Simon A. Ewing , et al. (78 additional authors not shown)

Abstract: Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum chemical method development. This article reviews the major advances since the previous overview in 2020, covering new modules and methodology, infrastructure changes, and performance benchmarks. Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum chemical method development. This article reviews the major advances since the previous overview in 2020, covering new modules and methodology, infrastructure changes, and performance benchmarks. △ Less

Submitted 7 April, 2026; v1 submitted 14 March, 2026; originally announced March 2026.

Towards Universal Computational Aberration Correction in Photographic Cameras: A Comprehensive Benchmark Analysis

Authors: Xiaolong Qian, Qi Jiang, Yao Gao, Lei Sun, Zhonghua Yi, Kailun Yang, Luc Van Gool, Kaiwei Wang

Abstract: Prevalent Computational Aberration Correction (CAC) methods are typically tailored to specific optical systems, leading to poor generalization and labor-intensive re-training for new lenses. Developing CAC paradigms capable of generalizing across diverse photographic lenses offers a promising solution to these challenges. However, efforts to achieve such cross-lens universality within consumer pho… ▽ More Prevalent Computational Aberration Correction (CAC) methods are typically tailored to specific optical systems, leading to poor generalization and labor-intensive re-training for new lenses. Developing CAC paradigms capable of generalizing across diverse photographic lenses offers a promising solution to these challenges. However, efforts to achieve such cross-lens universality within consumer photography are still in their early stages due to the lack of a comprehensive benchmark that encompasses a sufficiently wide range of optical aberrations. Furthermore, it remains unclear which specific factors influence existing CAC methods and how these factors affect their performance. In this paper, we present comprehensive experiments and evaluations involving 24 image restoration and CAC algorithms, utilizing our newly proposed UniCAC, a large-scale benchmark for photographic cameras constructed via automatic optical design. The Optical Degradation Evaluator (ODE) is introduced as a novel framework to objectively assess the difficulty of CAC tasks, offering credible quantification of optical aberrations and enabling reliable evaluation. Drawing on our comparative analysis, we identify three key factors -- prior utilization, network architecture, and training strategy -- that most significantly influence CAC performance, and further investigate their respective effects. We believe that our benchmark, dataset, and observations contribute foundational insights to related areas and lay the groundwork for future investigations. Benchmarks, codes, and Zemax files will be available at https://github.com/XiaolongQian/UniCAC. △ Less

Submitted 12 March, 2026; originally announced March 2026.

Comments: Accepted to CVPR 2026. Benchmarks, codes, and Zemax files will be available at https://github.com/XiaolongQian/UniCAC

High-Performance Quantum Frequency Conversion from Ultraviolet to Telecom Band

Authors: Yi Yang, Bin Wang, Ji-Chao Lin, Yang Gao, Xin Li, Jiu-Peng Chen, Lei Hou, Ye Wang, Yong Wan, Xiu-Ping Xie, Ming-Yang Zheng, Qiang Zhang, Jian-Wei Pan

Abstract: Quantum frequency conversion (QFC) is essential for bridging the spectral gap between stationary qubits and low-loss optical communication channels. In this work, we demonstrate a short-wavelength-pumping QFC with the first-order quasi-phase matching period of 3.07 um on thin-film lithium niobate, converting ultraviolet photons to the telecom C-band. By constructing a theoretical model that correl… ▽ More Quantum frequency conversion (QFC) is essential for bridging the spectral gap between stationary qubits and low-loss optical communication channels. In this work, we demonstrate a short-wavelength-pumping QFC with the first-order quasi-phase matching period of 3.07 um on thin-film lithium niobate, converting ultraviolet photons to the telecom C-band. By constructing a theoretical model that correlates the normalized conversion efficiency with domain defects in the short-period phase-matched waveguide, we found the critical tolerance of domain defects along the waveguide should be $\le 2$ (excluding the ends). Based on this, we achieved a theoretical limit normalized conversion efficiency of 839%/(W*cm^2) for the fundamental guided mode through fabrication optimization. Furthermore, we propose a robust noise suppression strategy for short-wavelength pumping by utilizing the counter-tuning behaviors of difference-frequency generation and spontaneous parametric down-conversion. By combining these advances with ultra-narrowband filtering, we achieve a record-high external efficiency of 28.8% and an ultra-low noise of 35 counts per second. This high-performance QFC connecting ultraviolet and telecom bands satisfies the stringent requirements for long-lived remote ion-ion entanglement in scalable quantum networks [W.-Z. Liu et al., Nature (2026)]. △ Less

Submitted 2 March, 2026; originally announced March 2026.

Comments: 13 pages, 10 figures

Switchable high-Q light absorbers based on phase-change resonant metasurfaces

Authors: Kai Qi, Guoxiang Wang, Xiang Shen, Yixiao Gao

Abstract: In this paper, we propose a switchable high-Q light absorber based on a reconfigurable metasurface enabled by a lowloss phase-change material (PCM). By leveraging the coupling between guided-mode resonance and Fabry-Perot modes, mediated by the phase-transition dynamics of the embedded PCM, the resonance Q factor can be actively tuned. This allows the system to switch from a perfect dark state, go… ▽ More In this paper, we propose a switchable high-Q light absorber based on a reconfigurable metasurface enabled by a lowloss phase-change material (PCM). By leveraging the coupling between guided-mode resonance and Fabry-Perot modes, mediated by the phase-transition dynamics of the embedded PCM, the resonance Q factor can be actively tuned. This allows the system to switch from a perfect dark state, governed by the physics of bound states in the continuum, to a critically coupled resonance with a finite Q factor. Consequently, the metasurface exhibits perfect absorption in the amorphous state and a reflection-dominated response in the crystalline state. The proposed metasurface holds significant potential for diverse nanophotonic applications, including photodetection and thermal emission control. △ Less

Submitted 11 February, 2026; originally announced February 2026.

Large language models for spreading dynamics in complex systems

Authors: Shuyu Jiang, Hao Ren, Yichang Gao, Yi-Cheng Zhang, Li Qi, Dayong Xiao, Jie Fan, Rui Tang, Wei Wang

Abstract: Spreading dynamics is a central topic in the physics of complex systems and network science, providing a unified framework for understanding how information, behaviors, and diseases propagate through interactions among system units. In many propagation contexts, spreading processes are influenced by multiple interacting factors, such as information expression patterns, cultural contexts, living en… ▽ More Spreading dynamics is a central topic in the physics of complex systems and network science, providing a unified framework for understanding how information, behaviors, and diseases propagate through interactions among system units. In many propagation contexts, spreading processes are influenced by multiple interacting factors, such as information expression patterns, cultural contexts, living environments, cognitive preferences, and public policies, which are difficult to incorporate directly into classical modeling frameworks. Recently, large language models (LLMs) have exhibited strong capabilities in natural language understanding, reasoning, and generation, enabling explicit perception of semantic content and contextual cues in spreading processes, thereby supporting the analysis of the different influencing factors. Beyond serving as external analytical tools, LLMs can also act as interactive agents embedded in propagation systems, potentially influencing spreading pathways and feedback structures. Consequently, the roles and impacts of LLMs on spreading dynamics have become an active and rapidly growing research area across multiple research disciplines. This review provides a comprehensive overview of recent advances in applying LLMs to the study of spreading dynamics across two representative domains: digital epidemics, such as misinformation and rumors, and biological epidemics, including infectious disease outbreaks. We first examine the foundations of epidemic modeling from a complex-systems perspective and discuss how LLM-based approaches relate to traditional frameworks. We then systematically review recent studies from three key perspectives, which are epidemic modeling, epidemic detection and surveillance, and epidemic prediction and management, to clarify how LLMs enhance these areas. Finally, open challenges and potential research directions are discussed. △ Less

Submitted 8 February, 2026; originally announced February 2026.

Multi-objective fluorescent molecule design with a data-physics dual-driven generative framework

Authors: Yanheng Li, Zhichen Pu, Lijiang Yang, Zehao Zhou, Yi Qin Gao

Abstract: Designing fluorescent small molecules with tailored optical and physicochemical properties requires navigating vast, underexplored chemical space while satisfying multiple objectives and constraints. Conventional generate-score-screen approaches become impractical under such realistic design specifications, owing to their low search efficiency, unreliable generalizability of machine-learning predi… ▽ More Designing fluorescent small molecules with tailored optical and physicochemical properties requires navigating vast, underexplored chemical space while satisfying multiple objectives and constraints. Conventional generate-score-screen approaches become impractical under such realistic design specifications, owing to their low search efficiency, unreliable generalizability of machine-learning prediction, and the prohibitive cost of quantum chemical calculation. Here we present LUMOS, a data-and-physics driven framework for inverse design of fluorescent molecules. LUMOS couples generator and predictor within a shared latent representation, enabling direct specification-to-molecule design and efficient exploration. Moreover, LUMOS combines neural networks with a fast time-dependent density functional theory (TD-DFT) calculation workflow to build a suite of complementary predictors spanning different trade-offs in speed, accuracy, and generalizability, enabling reliable property prediction across diverse scenarios. Finally, LUMOS employs a property-guided diffusion model integrated with multi-objective evolutionary algorithms, enabling de novo design and molecular optimization under multiple objectives and constraints. Across comprehensive benchmarks, LUMOS consistently outperforms baseline models in terms of accuracy, generalizability and physical plausibility for fluorescence property prediction, and demonstrates superior performance in multi-objective scaffold- and fragment-level molecular optimization. Further validation using TD-DFT and molecular dynamics (MD) simulations demonstrates that LUMOS can generate valid fluorophores that meet various target specifications. Overall, these results establish LUMOS as a data-physics dual-driven framework for general fluorophore inverse design. △ Less

Submitted 19 January, 2026; originally announced January 2026.

Comments: Total 43 pages: 32 pages Main Text + 11 pages SI

Electric field switching of altermagnetic spin-splitting in multiferroic skyrmions

Authors: Gui Wang, Yuhang Li, Bin Li, Xianzhe Chen, Jianting Dong, Weizhao Chen, Xiaobing Chen, Naifu Zheng, Maosen Guo, Aomei Tong, Hua Bai, Hongrui Zhang, Yifan Gao, Kaiwen Shen, Jiangyuan Zhu, Jiahao Han, Yingfen Wei, Hao Jiang, Xumeng Zhang, Ming Wang, Kebiao Xu, Wu Shi, Pengfei Wang, Jia Zhang, Qihang Liu , et al. (4 additional authors not shown)

Abstract: Magnetic skyrmions are localized magnetic structures that retain their shape and stability over time, thanks to their topological nature. Recent theoretical and experimental progress has laid the groundwork for understanding magnetic skyrmions characterized by negligible net magnetization and ultrafast dynamics. Notably, skyrmions emerging in materials with altermagnetism, a novel magnetic phase f… ▽ More Magnetic skyrmions are localized magnetic structures that retain their shape and stability over time, thanks to their topological nature. Recent theoretical and experimental progress has laid the groundwork for understanding magnetic skyrmions characterized by negligible net magnetization and ultrafast dynamics. Notably, skyrmions emerging in materials with altermagnetism, a novel magnetic phase featuring lifted Kramers degeneracy-have remained unreported until now. In this study, we demonstrate that BiFeO3, a multiferroic renowned for its strong coupling between ferroelectricity and magnetism, can transit from a spin cycloid to a Neel-type skyrmion under antidamping spin-orbit torque at room temperature. Strikingly, the altermagnetic spin splitting within BiFeO3 skyrmion can be reversed through the application of an electric field, revealed via the Circular photogalvanic effect. This quasiparticle, which possesses a neutral topological charge, holds substantial promise for diverse applications-most notably, enabling the development of unconventional computing systems with low power consumption and magnetoelectric controllability. △ Less

Submitted 10 January, 2026; originally announced January 2026.

Human-like AI-based Auto-Field-in-Field Whole-Brain Radiotherapy Treatment Planning With Conversation Large Language Model Feedback

Authors: Adnan Jafar, An Qin, Gavin Atkins, Xiaoyu Hu, Yin Gao, Xun Jia

Abstract: Whole-brain radiotherapy (WBRT) is a common treatment due to its simplicity and effectiveness. While automated Field-in-Field (Auto-FiF) functions assist WBRT planning in modern treatment planning systems, it still requires manual approaches for optimal plan generation including patient-specific hyperparameters definition and plan refinement based on quality feedback. This study introduces an auto… ▽ More Whole-brain radiotherapy (WBRT) is a common treatment due to its simplicity and effectiveness. While automated Field-in-Field (Auto-FiF) functions assist WBRT planning in modern treatment planning systems, it still requires manual approaches for optimal plan generation including patient-specific hyperparameters definition and plan refinement based on quality feedback. This study introduces an automated WBRT planning pipeline that integrates a deep learning (DL) Hyperparameter Prediction model for patient-specific parameter generation and a large-language model (LLM)-based conversational interface for interactive plan refinement. The Hyperparameter Prediction module was trained on 55 WBRT cases using geometric features of clinical target volume (CTV) and organs at risk (OARs) to determine optimal Auto-FiF settings in RayStation treatment planning system. Plans were generated under predicted hyperparameters. For cases in which the generated plan was suboptimal, quality feedback via voice input was captured by a Conversation module, transcribed using Whisper, and interpreted by GPT-4o to adjust planning settings. Plan quality was evaluated in 15 independent cases using clinical metrics and expert review, and model explainability was supported through analysis of feature importance. Fourteen of 15 DL-generated plans were clinically acceptable. Normalized to identical CTV D95% as the clinical plans, the DL-generated and clinical plans showed no statistically significant differences in doses to the eyes, lenses, or CTV dose metrics D1% and D99%. The DL-based planning required under 1 minute of computation and achieved total workflow execution in approximately 7 minutes with a single mouse click, compared to 15 minutes for manual planning. In cases requiring adjustment, the Conversational module successfully improved dose conformity and hotspot reduction. △ Less

Submitted 2 January, 2026; originally announced January 2026.

Comments: 22 pages, 7 figures

Two-stage Respiratory Motion-resolved Radial MR Image Reconstruction Using an Interpretable Deep Unrolled Network

Authors: Shanshan Shan, Hongli Chen, Yuhan Wei, Peng Wu, Yang Gao, Tess Reynolds, Paul Liu, Jialiang Zhang, Qidi Luo, Chunyi Liu, Paul Keall, Feng Liu, Yaqin Zhang, David E. J. Waddington, Mingyuan Gao

Abstract: Due to the prolonged MRI encoding process, respiratory motion can cause undesired artifacts and image blurring, degrading image quality and limiting clinical applications in abdominal and pulmonary imaging. In this work, we develop a two-stage respiratory motion-resolved radial MR image reconstruction pipeline using an interpretable deep unrolled network (MoraNet), enabling high-quality imaging un… ▽ More Due to the prolonged MRI encoding process, respiratory motion can cause undesired artifacts and image blurring, degrading image quality and limiting clinical applications in abdominal and pulmonary imaging. In this work, we develop a two-stage respiratory motion-resolved radial MR image reconstruction pipeline using an interpretable deep unrolled network (MoraNet), enabling high-quality imaging under free-breathing conditions. Firstly, low-resolution images are reconstructed from the central region of successive golden-angle radial k-space to extract respiratory motion signals. The binned k-space data based on the respiratory signal are then used to reconstruct the motion-resolved high-resolution image for each motion state. The MoraNet applies nonuniform fast Fourier transform (NUFFT) to operate radial encoding and convolutional neural network (CNN) modules to conduct image regularizations. The MoraNet was trained on retrospectively acquired lung MRI images for both fully sampled and undersampled acquisitions. The performance of the proposed method was evaluated on digital CT/MRI breathing XCAT (CoMBAT) phantom data, QUASAR motion phantom data acquired from a 1.0T MRI scanner and volunteer chest data acquired from a 1.5T MRI scanner. The MoraNet pipeline was compared with motion-averaged reconstruction and a conventional compressed sensing (CS)-based method in terms of SSIM, RMSE and computation time. Simulation and experimental results demonstrated that the proposed network could provide accurate respiratory signal estimation and enable effective motion correction. Compared with the CS method, the MoraNet preserved better structural details with lower RMSE and higher SSIM values at acceleration factor of 4, and meanwhile took ten-fold faster inference time. △ Less

Submitted 28 December, 2025; originally announced December 2025.

The interactions between two drops floating on a partially miscible liquid pool

Authors: Yuan Gao, Yanshen Li

Abstract: The interaction of drops floating on liquid surfaces is important for many natural processes and industrial applications. In many of the cases, the system is multicomponent, leading to Marangoni flows on the surface. Here we investigate the competing effect of the attractive ``Cheerios effect'' and the repulsive solutal Marangoni flow by observing the behaviors of two identical oil drops floating… ▽ More The interaction of drops floating on liquid surfaces is important for many natural processes and industrial applications. In many of the cases, the system is multicomponent, leading to Marangoni flows on the surface. Here we investigate the competing effect of the attractive ``Cheerios effect'' and the repulsive solutal Marangoni flow by observing the behaviors of two identical oil drops floating on partially miscible pool made of ethanol-water mixtures. Three typical behaviors are found: Repel, Coalesce and Rebound, in which the drops repel each other, attract each other and then coalesce, and attract and rebound upon contact. A scaling theory based on the two competing forces is developed to distinguish the repulsive and attractive behaviors of the drops. For the transition from Coalesce to Rebound, a lubrication layer is found to form when the immersed lower halves of the drops are more than half a sphere, which prevents the drops from coalescing. △ Less

Submitted 14 December, 2025; originally announced December 2025.

Comments: 11 pages, 7 figures

Broadband Reflective Elastic Mode Conversion Enabled by a Single Row of Inclined Long-Slits

Authors: Kaifei Feng, Weidong Wang, Yucheng Gao, Fengming Liu, Qiujiao Du, Wenshuai Zhang, Pai Peng

Abstract: Broadband longitudinal-to-transverse mode conversion under normal incidence remains difficult to achieve, especially with structurally simple designs. Numerical simulations show that a single periodic row of inclined long-slits near a free surface enables high-efficiency broadband conversion, where the conversion rate exceeds 0.8 across a normalized-frequency range of 0.27 to 0.74, corresponding t… ▽ More Broadband longitudinal-to-transverse mode conversion under normal incidence remains difficult to achieve, especially with structurally simple designs. Numerical simulations show that a single periodic row of inclined long-slits near a free surface enables high-efficiency broadband conversion, where the conversion rate exceeds 0.8 across a normalized-frequency range of 0.27 to 0.74, corresponding to a 93.1% relative bandwidth. The broadband response originates from two intrinsic deformation modes of the mass blocks between adjacent inclined slits: a rotational mode and a quadrupole mode. The spectral overlap of these two modes sustains a continuous high-efficiency band. The effect is robust against geometric variations, demonstrating that geometric asymmetry alone offers a minimal yet effective route to broadband elastic-wave mode conversion. △ Less

Submitted 24 November, 2025; originally announced November 2025.

Analytical Excited-State Gradients and Derivative Couplings in TDDFT with Minimal Auxiliary Basis Set Approximation and GPU Acceleration

Authors: Zhichen Pu, Xiaojie Wu, Yuanheng Wang, Cheng Fan, Wen Yan, Zehao Zhou, Yi Qin Gao, Qiming Sun

Abstract: Calculating excited-state gradients and derivative couplings using time-dependent density functional theory (TDDFT) remains a computationally demanding task. An efficient variant, TDDFT with resolution of the identity and a minimal auxiliary basis (TDDFT-ris), has been developed to accelerate excitation energy calculations. However, the formulation and implementation of analytical derivatives for… ▽ More Calculating excited-state gradients and derivative couplings using time-dependent density functional theory (TDDFT) remains a computationally demanding task. An efficient variant, TDDFT with resolution of the identity and a minimal auxiliary basis (TDDFT-ris), has been developed to accelerate excitation energy calculations. However, the formulation and implementation of analytical derivatives for this method have not yet been reported. In this work, we present an implementation of analytical excited-state gradients and derivative couplings within the TDDFT-ris framework. Benchmark calculations on medium-sized organic molecules demonstrate a two- to three-fold speedup for both gradients and derivative couplings compared to standard TDDFT. The accuracy of the TDDFT-ris approach is assessed for gradient-dependent applications, including geometry optimizations, emission energy calculations, and the localization of minimum-energy crossing points. Overall, the TDDFT-ris method provides reliable approximations for most cases, with noticeable errors mainly occurring in derivative couplings between nearly degenerate states. △ Less

Submitted 25 November, 2025; v1 submitted 22 November, 2025; originally announced November 2025.

Journal ref: JCTC 2026

Learning Latent Transmission and Glare Maps for Lens Veiling Glare Removal

Authors: Xiaolong Qian, Qi Jiang, Lei Sun, Zongxi Yu, Kailun Yang, Peixuan Wu, Jiacheng Zhou, Yao Gao, Yaoguang Ma, Ming-Hsuan Yang, Kaiwei Wang

Abstract: Beyond the commonly recognized optical aberrations, the imaging performance of simplified optical systems--including single-lens and metalens designs--is often further degraded by veiling glare caused by stray-light scattering from non-ideal optical surfaces and coatings, particularly in complex real-world environments. This compound degradation undermines traditional lens aberration correction ye… ▽ More Beyond the commonly recognized optical aberrations, the imaging performance of simplified optical systems--including single-lens and metalens designs--is often further degraded by veiling glare caused by stray-light scattering from non-ideal optical surfaces and coatings, particularly in complex real-world environments. This compound degradation undermines traditional lens aberration correction yet remains underexplored. A major challenge is that conventional scattering models (e.g., for dehazing) fail to fit veiling glare due to its spatial-varying and depth-independent nature. Consequently, paired high-quality data are difficult to prepare via simulation, hindering application of data-driven veiling glare removal models. To this end, we propose VeilGen, a generative model that learns to simulate veiling glare by estimating its underlying optical transmission and glare maps in an unsupervised manner from target images, regularized by Stable Diffusion (SD)-based priors. VeilGen enables paired dataset generation with realistic compound degradation of optical aberrations and veiling glare, while also providing the estimated latent optical transmission and glare maps to guide the veiling glare removal process. We further introduce DeVeiler, a restoration network trained with a reversibility constraint, which utilizes the predicted latent maps to guide an inverse process of the learned scattering model. Extensive experiments on challenging simplified optical systems demonstrate that our approach delivers superior restoration quality and physical fidelity compared with existing methods. These suggest that VeilGen reliably synthesizes realistic veiling glare, and its learned latent maps effectively guide the restoration process in DeVeiler. All code and datasets will be publicly released at https://github.com/XiaolongQian/DeVeiler. △ Less

Submitted 5 March, 2026; v1 submitted 21 November, 2025; originally announced November 2025.

Comments: Accepted to CVPR 2026. All code and datasets will be publicly released at https://github.com/XiaolongQian/DeVeiler

Towards Blind Lens Aberration Correction via Large LensLib Pre-training and Discrete Degradation Priors

Authors: Xiaolong Qian, Qi Jiang, Yao Gao, Lei Sun, Kailun Yang, Xian Wang, Zhonghua Yi, Wenyong Li, Ming-Hsuan Yang, Luc Van Gool, Kaiwei Wang

Abstract: Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations. This work proposes FoundCAC, a universal foundational framework that resolves two challenges hindering the generalization of existing pipelines:… ▽ More Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations. This work proposes FoundCAC, a universal foundational framework that resolves two challenges hindering the generalization of existing pipelines: the difficulty of scaling training data and the absence of prior guidance characterizing optical degradation. To improve data scalability, we expand the design specifications to increase degradation diversity and construct AODLibpro, a large-scale lens library using stratified sampling over spatial-variation patterns and degradation severity. In terms of model design, to leverage Point Spread Functions (PSFs) as guidance while maintaining the blind paradigm, we propose a multi-stage vector-quantized representation learning scheme. This paradigm is specifically designed to construct a Latent PSF Representation (LPR), explicitly encoding complex continuous PSFs into a discrete degradation prior to regularize the highly ill-posed restoration process. Through a simple yet effective codebook-freezing strategy, our framework leverages the discrete prior to elevate full-shot restoration performance and unlock highly efficient few-shot adaptation for unseen lenses. Experiments on synthetic LensLib, real-design simulations, and real-captured lenses show that our framework achieves state-of-the-art zero-shot performance under complementary evaluation protocols, while enabling highly efficient few-shot adaptation for specific lenses. The source code and datasets will be made publicly available at https://github.com/zju-jiangqi/FoundCAC. △ Less

Submitted 10 July, 2026; v1 submitted 21 November, 2025; originally announced November 2025.

Comments: Accepted to 2026 IEEE International Conference on Computational Photography (ICCP). The source code and datasets will be made publicly available at https://github.com/zju-jiangqi/FoundCAC

Broadband telecom single-photon emissions from InAs/InP quantum dots grown by MOVPE droplet epitaxy

Authors: Shichen Zhang, Li Liu, Kai Guo, Xingli Mu, Yuanfei Gao, Junqi Liu, Fengqi Liu, Quanyong Lu, Zhiliang Yuan

Abstract: The development of quantum materials for single-photon emission is crucial for the advancement of quantum information technology. Although significant advancement has been witnessed in recent years for single photon sources in near infrared band (λ~700-1000 nm), several challenges have yet to be addressed for ideal single photon emission at the telecommunication band. In this study, we present a d… ▽ More The development of quantum materials for single-photon emission is crucial for the advancement of quantum information technology. Although significant advancement has been witnessed in recent years for single photon sources in near infrared band (λ~700-1000 nm), several challenges have yet to be addressed for ideal single photon emission at the telecommunication band. In this study, we present a droplet-epitaxy strategy for O-band to C-band single-photon source based semiconductor quantum dots (QDs) using metal-organic vapor-phase epitaxy (MOVPE). Via investigating the growth conditions of the epitaxial process, we have successfully synthesized InAs/InP QDs with narrow emission lines spanning a broad spectral range of λ~1200-1600 nm. The morphological and optical properties of the samples were characterized using atomic force microscopy and micro photoluminescence spectroscopy. The recorded single-photon purity of a plain QD structure reaches (g(2)(0) = 0.16), with a radiative recombination lifetime as short as 1.5 ns. This work provides a crucial platform for future research on integrated microcavity enhancement techniques and coupled QDs with other quantum photonics in the telecom bands, offering significant prospects for quantum network applications. △ Less

Submitted 20 November, 2025; originally announced November 2025.

Experimental Realization of All-Optical Terahertz Attoclock

Authors: Yanjun Gao, Yizhu Zhang, Yulin Chen, Jingjing Zhao, Meng Li, Xiaokun Liu, Yange Chen, Jiahui Guo, Kiyoshi Ueda, Ahai Chen, Yuhai Jiang

Abstract: The attoclock is a powerful tool for probing ultrafast electron dynamics with attosecond precision.Here, we demonstrate an all-optical terahertz (THz) attoclock that reconstructs photoionization dynamics by detecting the THz radiation emitted from Ar atoms ionized by two-color (800 nm/400 nm) laser fields. In this approach, the polarization direction of the emitted THz field reflects the direction… ▽ More The attoclock is a powerful tool for probing ultrafast electron dynamics with attosecond precision.Here, we demonstrate an all-optical terahertz (THz) attoclock that reconstructs photoionization dynamics by detecting the THz radiation emitted from Ar atoms ionized by two-color (800 nm/400 nm) laser fields. In this approach, the polarization direction of the emitted THz field reflects the direction of the photoelectron drift velocity and thus serves as a direct observable that encodes the effective ionization delay, analogous to the angular deflection of photoelectrons in conventional attoclocks. By precisely tailoring the relative phase and ellipticity of the driving fields, we observe intensity-dependent rotations of the THz polarization. These rotations, which reveal changes of the effective delay, are consistent with both conventional attoclock measurements and time-dependent Schrödinger equation simulations. Our experiment establishes the feasibility of the THz attoclock as a vacuum-free and contactless probe of tunneling dynamics, offering a transformative alternative for investigating condensed-matter systems where photoelectron detection is challenging. △ Less

Submitted 14 January, 2026; v1 submitted 20 November, 2025; originally announced November 2025.

Recent advances in stimulus-assisted nanoprecipitation for nanoparticle synthesis

Authors: Mingbo Li, Junhao Cai, Yawen Gao

Abstract: Nanoprecipitation, the rapid solvent-displacement route to nanoscale phase separation, has matured from a simple batch operation into a versatile platform for nanomaterial synthesis. This review synthesizes recent progress in stimulus-assisted nanoprecipitation, wherein externally applied triggers (ultrasonic, electrical, supergravity, thermal, chemical, and photonic/other stimuli) are integrated… ▽ More Nanoprecipitation, the rapid solvent-displacement route to nanoscale phase separation, has matured from a simple batch operation into a versatile platform for nanomaterial synthesis. This review synthesizes recent progress in stimulus-assisted nanoprecipitation, wherein externally applied triggers (ultrasonic, electrical, supergravity, thermal, chemical, and photonic/other stimuli) are integrated with contemporary mixing technologies (batch, flash, microfluidic, membrane and high-shear reactors) to decouple and selectively control over nucleation, growth kinetics, and assembly processes. These methods allow for the precise tuning of the size, morphology, stability and functionality of nanoparticles (NPs), thereby broadening their applications in drug delivery, catalysis and materials science. We distill mechanistic principles by which each stimulus alters local supersaturation, chain mobility, interfacial instabilities, or droplet/film microreactor dynamics, and compare advantages and limitations by surveying research works from recent years. We also explore the potential development trends of multiscale coupling models, design rules for stimulus-compatible continuous reactors, and adoption of data-driven optimization frameworks to expand the capabilities of nanoprecipitation for advanced nanomaterial design. △ Less

Submitted 18 November, 2025; originally announced November 2025.

Diamond-based sensing of stray fields from the bulk of thin-film magnets via nano-indentation

Authors: Ming-Zhong Ai, Kang-Yuan Liu, Biao Zhang, Weng-Hang Leong, Yao Gao, Yue Cui, Guoli Zhu, Licong Peng, Yanglong Hou, Quan Li, Ren-Bao Liu

Abstract: Measurement of the magnetization in the bulk of thin-film or two-dimensional materials is important for understanding their intrinsic properties without the complications from edges or domain walls. However, the stray fields from the bulk vanish or are very weak, which limits the application of direct measurement methods. Here, we develop a non-destructive approach to directly measuring the stray… ▽ More Measurement of the magnetization in the bulk of thin-film or two-dimensional materials is important for understanding their intrinsic properties without the complications from edges or domain walls. However, the stray fields from the bulk vanish or are very weak, which limits the application of direct measurement methods. Here, we develop a non-destructive approach to directly measuring the stray fields from the bulk of thin-film magnets at arbitrarily designatable locations, with nanoscale spatial resolution. We employ nano-indentation to induce the leakage of stray fields from the materials and use nano-diamond magnetometers to measure them. We apply the method to iron thin films and determine the intrinsic magnetization in the bulk of the materials. This work provides direct access to the intrinsic magnetic properties of thin-film and low-dimensional materials, as well as a method to study the mechanical effects on magnetization in nanomaterials. △ Less

Submitted 13 November, 2025; originally announced November 2025.

Comments: Comments are welcome

Geometric phase metasurfaces for linearly polarized light

Authors: Yubin Gao, Qikai Chen, Yaoguang Ma

Abstract: The geometric phase is a universal concept in modern physics and has enabled the development of metasurfaces for versatile wavefront shaping. However, its realization in metasurfaces has been restricted to circularly polarized light, confining geometric phase metasurfaces to helicity-dependent operation and excluding them from the linear-polarization domain that dominates modern optics. In this wo… ▽ More The geometric phase is a universal concept in modern physics and has enabled the development of metasurfaces for versatile wavefront shaping. However, its realization in metasurfaces has been restricted to circularly polarized light, confining geometric phase metasurfaces to helicity-dependent operation and excluding them from the linear-polarization domain that dominates modern optics. In this work, we overcome this limitation by harnessing exceptional points of non-Hermitian physics. We introduce and experimentally realize quasi-exceptional-point metasurfaces that exploit engineered singularities to directly impart a geometric phase onto linearly polarized light. Proof-of-principle demonstrations with gratings and holograms confirm broadband and high-fidelity wavefront shaping across arbitrary linear polarizations, which has not been achieved with previous phase modulation approaches. By revealing an intrinsic connection between geometric phase and non-Hermitian photonics, our work resolves a long-standing theoretical impasse and establishes a new framework for high-dimensional light control, opening opportunities for scalable polarization optics, advanced imaging, holography, optical communications, and integrated photonics. △ Less

Submitted 10 November, 2025; originally announced November 2025.

Stochastic simulation of partial discharge inception

Authors: Jannis Teunissen, Yuting Gao

Abstract: We present a Monte Carlo method for simulating the inception of electric discharges in gases. The input consists of an unstructured grid containing the electrostatic field. The output of the model is the estimated probability of discharge inception per initial electron position, as well as the estimated time lag between the appearance of the initial electron and discharge inception. To obtain thes… ▽ More We present a Monte Carlo method for simulating the inception of electric discharges in gases. The input consists of an unstructured grid containing the electrostatic field. The output of the model is the estimated probability of discharge inception per initial electron position, as well as the estimated time lag between the appearance of the initial electron and discharge inception. To obtain these quantities electron avalanches are simulated for initial electron positions throughout the whole domain, also including regions below the critical electric field. Avalanches are assumed to propagate along field lines, and they can produce additional avalanches due to photon and ion feedback. If the number of avalanches keeps increasing over time we assume that an electric discharge will eventually form. A statistical distribution for the electron avalanche size is used, which is also valid for gases with strong electron attachment. We compare this distribution against the results of particle simulations. Furthermore, we demonstrate examples of inception simulations in 2D Cartesian, 2D axisymmetric and 3D electrode geometries. △ Less

Submitted 22 May, 2026; v1 submitted 6 November, 2025; originally announced November 2025.

Seeing Clearly and Deeply: An RGBD Imaging Approach with a Bio-inspired Monocentric Design

Authors: Zongxi Yu, Xiaolong Qian, Shaohua Gao, Qi Jiang, Yao Gao, Kailun Yang, Kaiwei Wang

Abstract: Achieving high-fidelity, compact RGBD imaging presents a dual challenge: conventional compact optics struggle with RGB sharpness across the entire depth-of-field, while software-only Monocular Depth Estimation (MDE) is an ill-posed problem reliant on unreliable semantic priors. While deep optics with elements like DOEs can encode depth, they introduce trade-offs in fabrication complexity and chrom… ▽ More Achieving high-fidelity, compact RGBD imaging presents a dual challenge: conventional compact optics struggle with RGB sharpness across the entire depth-of-field, while software-only Monocular Depth Estimation (MDE) is an ill-posed problem reliant on unreliable semantic priors. While deep optics with elements like DOEs can encode depth, they introduce trade-offs in fabrication complexity and chromatic aberrations, compromising simplicity. To address this, we first introduce a novel bio-inspired all-spherical monocentric lens, around which we build the Bionic Monocentric Imaging (BMI) framework, a holistic co-design. This optical design naturally encodes depth into its depth-varying Point Spread Functions (PSFs) without requiring complex diffractive or freeform elements. We establish a rigorous physically-based forward model to generate a synthetic dataset by precisely simulating the optical degradation process. This simulation pipeline is co-designed with a dual-head, multi-scale reconstruction network that employs a shared encoder to jointly recover a high-fidelity All-in-Focus (AiF) image and a precise depth map from a single coded capture. Extensive experiments validate the state-of-the-art performance of the proposed framework. In depth estimation, the method attains an Abs Rel of 0.026 and an RMSE of 0.130, markedly outperforming leading software-only approaches and other deep optics systems. For image restoration, the system achieves an SSIM of 0.960 and a perceptual LPIPS score of 0.082, thereby confirming a superior balance between image fidelity and depth accuracy. This study illustrates that the integration of bio-inspired, fully spherical optics with a joint reconstruction algorithm constitutes an effective strategy for addressing the intrinsic challenges in high-performance compact RGBD imaging. Source code will be publicly available at https://github.com/ZongxiYu-ZJU/BMI. △ Less

Submitted 29 October, 2025; originally announced October 2025.

Comments: The source code will be publicly available at https://github.com/ZongxiYu-ZJU/BMI

TRIDS: AI-native molecular docking framework for accelerating high-throughput virtual screening with physically valid binding poses

Authors: Xuhan Liu, Baohua Zhang, Yue Xue, Yize Hao, Tiejun Bing, Lili Chai, Tanfeng Zhao, Lijiang Yang, Hong Zhang, Yi Qin Gao

Abstract: Molecular docking is a cornerstone of drug discovery for unveiling the mechanism of ligand-receptor interactions. With the recent advances of deep learning (DL), AI-powered molecular docking methods have achieved higher accuracy for binding pose prediction and virtual screening compared with classical physics-based methods. However, there is still a scarcity of approaches to strike a balance among… ▽ More Molecular docking is a cornerstone of drug discovery for unveiling the mechanism of ligand-receptor interactions. With the recent advances of deep learning (DL), AI-powered molecular docking methods have achieved higher accuracy for binding pose prediction and virtual screening compared with classical physics-based methods. However, there is still a scarcity of approaches to strike a balance among accuracy, computational efficiency, and rigorous physical validity of the output conformations. In the previous two versions of DSDP, we demonstrated the effectiveness of guiding conformation sampling with the gradient of an analytic scoring function. As the third version, TRIDS was devised as an AI-native docking framework that expand the similar strategy to unify conformation sampling and docking processes with DL-based model for improving accuracy of docking and screening. Furthermore, it is tailored for seamless cooperation of AI and physics to guarantee the physical validity of predicted binding poses. Being user-friendly, TRIDS predicts the binding site, parses multiple file formats, and supports Python programming and PyMOL graphical interaction. It improves docking accuracy and passes physical validation with high computational efficiency, i.e. a single docking task is done in a fraction of second while maintaining a highly lightweight GPU memory footprint of merely hundreds of megabytes, facilitating high-throughput virtual screening in reality. As a proof of concept, TRIDS allowed us to obtain hit compounds with novel scaffolds for tumor necrosis factor-alpha (TNFα) inhibitor through a large-scale virtual screening. △ Less

Submitted 1 July, 2026; v1 submitted 28 October, 2025; originally announced October 2025.