Showing 1–50 of 1,410 results for author: Li, Z

Fast Nondestructive Readout for High-Clock-Rate Atom Array Quantum Processor

Authors: Xu-Zhao-Qiu Zeng, Chang You, Qing-Wei Wang, Zi-Feng Li, Yi Ji, Dong An, Chao Yu, Jia-Rui Liu, Zi-Mo He, Jia-Rui Gu, Yuhao Mei, Hao-Wen Cheng, Yu-Chen Zhang, Rui Lin, Zhan Wu, Jun Rui, Jun Zhang, Ming-Cheng Chen, Yu-Hao Deng, Chao-Yang Lu, Jian-Wei Pan

Abstract: Neutral-atom arrays have rapidly advanced to support thousands of qubits and execute high-fidelity logical operations. However, these processors remain severely throttled by their slowest fundamental operation: nondestructive qubit measurement, which requires milliseconds and fundamentally limits the system's clock rate. This bottleneck arises from both an inherent photon-budget dilemma---sufficie… ▽ More Neutral-atom arrays have rapidly advanced to support thousands of qubits and execute high-fidelity logical operations. However, these processors remain severely throttled by their slowest fundamental operation: nondestructive qubit measurement, which requires milliseconds and fundamentally limits the system's clock rate. This bottleneck arises from both an inherent photon-budget dilemma---sufficient fluorescence for reliable state discrimination must be collected without excessive heating or loss---and frame-based imaging, which imposes one common exposure and decision latency on intrinsically independent, site-local measurements. Here, we overcome these limitations with a fast, nondestructive readout architecture based on real-time, site-resolved adaptive protection. By integrating continuous photon counting with a dynamic feedforward framework, we decode qubit states with sub-microsecond latency and instantly shield atoms from redundant scattering. Demonstrated in parallel across a 100-qubit reconfigurable atom array, with adaptive protection on a 25-site subarray, this dynamic decision protocol reduces the average probe time to just $15\ μ\text{s}$. Model-free benchmarking yields a discrimination infidelity of $4.1 \times 10^{-5}$ and an atom loss of $2.1 \times 10^{-4}$, simultaneously setting new performance records for atom arrays. Exploiting this capability, we operate repeated quantum circuits at an unprecedented 1.7 kHz clock rate with atoms reused over 120 consecutive rounds---nearly sevenfold higher than the previous record---and enter the sub-millisecond cycle regime for the first time. By removing nondestructive readout as the dominant cycle-time bottleneck, this work unlocks high-clock-rate mid-circuit syndrome extraction, paving the way for high-throughput, fault-tolerant quantum computation. △ Less

Submitted 17 August, 2026; originally announced August 2026.

In-situ adjoint protocols for nonlinear PT-symmetric self-optimizing machines

Authors: Zheming Li, Lucas J. Fernández-Alcázar, Zin Lin, Tsampikos Kottos

Abstract: Adjoint methods provide a powerful route for gradient-based optimization, but their physical implementation is obstructed in generic nonlinear systems because the adjoint dynamics requires backward-time evolution, Jacobian transposition, and terminal-value constraints. Here we show that nonlinear parity-time ($\mathcal{PT}$)-symmetric systems overcome this obstruction. Using a class of nonlinear n… ▽ More Adjoint methods provide a powerful route for gradient-based optimization, but their physical implementation is obstructed in generic nonlinear systems because the adjoint dynamics requires backward-time evolution, Jacobian transposition, and terminal-value constraints. Here we show that nonlinear parity-time ($\mathcal{PT}$)-symmetric systems overcome this obstruction. Using a class of nonlinear non-Hermitian resonator networks, we establish symmetry relations that map the formal adjoint dynamics onto experimentally accessible forward-time evolutions supplemented by controlled injections. This construction enables exact in-situ evaluation of adjoint gradients without requiring explicit backward propagation or matrix transposition. We demonstrate the approach in nonlinear $\mathcal{PT}$-symmetric resonator chains, where the resulting optimization protocol autonomously discovers parameter configurations that realize prescribed spatio-temporal functionalities, including uniform energy redistribution and targeted wave transport at predefined time windows. Our results identify $\mathcal{PT}$ symmetry as a resource for implementing computational sensitivities within physical systems and establish a route toward self-optimizing nonlinear machines. △ Less

Submitted 16 August, 2026; originally announced August 2026.

Comments: 12 pages, 4 figures

Monolithic high density integrated photonics on bulk lithium niobate

Authors: Zizheng Li, Harmen Smedes, Bruno Lopez-Rodriguez, Thomas Scholte, Simon Groeblacher, Iman Esmaeil Zadeh

Abstract: Functional electro-optic (EO) tunable photonic integrated circuits are crucial to next-generation information processing and advanced computing, where ferroelectric materials such as lithium niobate (LiNbO3) provide outstanding optical properties and versatile tuning mechanisms. However, beyond achieving high-performance devices, practical deployment of LiNbO3 photonic integrated circuits requires… ▽ More Functional electro-optic (EO) tunable photonic integrated circuits are crucial to next-generation information processing and advanced computing, where ferroelectric materials such as lithium niobate (LiNbO3) provide outstanding optical properties and versatile tuning mechanisms. However, beyond achieving high-performance devices, practical deployment of LiNbO3 photonic integrated circuits requires rapid and cost-effective scale-up, which remains challenging because of the fabrication complexity and high costs of ion-sliced thin-film lithium niobate and wafer bonding processes. To address this gap, we demonstrate a fully monolithic photonic platform, amorphous silicon carbide (a-SiC) on bulk LiNbO3 crystal substrate, enabling scalable, CMOS-compatible, low-cost, and high-density photonic integrated circuits without relying on thin-film LiNbO3. This integration approach breaks the long-standing cost-performance-scalability tradeoff, and intrinsically eliminates the need for sophisticated fabrication processes, including ion slicing, wafer bonding, and LiNbO3 etching. It realizes high-density, low-loss, and record high EO tuning efficiency of VpiL = 2.87 Vcm on bulk LiNbO3 crystal substrate. Furthermore, leveraging slow-light effect near photonic crystal bandgap, an 8.5-fold EO tuning efficiency enhancement is achieved, highlighting the platform's capability in confinement control and dispersion engineering. △ Less

Submitted 16 August, 2026; originally announced August 2026.

Broadband phonon-velocity suppression and a finite anisotropic crossover in twisted bilayer SnSe

Authors: Peng Kang, Wei Yin, Da Wan, Shulin Bai, Sirui Fan, Qi Zou, Hongfeng Li, Xiao Xiang, Zhen Li, Yu Liu, Lei Zheng, Li-Dong Zhao

Abstract: Moiré superlattices reshape lattice dynamics without altering chemical composition, yet how crystal anisotropy modifies this control remains unclear. We combine density-functional-theory (DFT)-calibrated lattice-dynamical calculations with angle-matched untwisted controls to study puckered bilayer SnSe across seven commensurate twist angles ($3.18^\circ$--$8.77^\circ$). At 300 K, twisting suppress… ▽ More Moiré superlattices reshape lattice dynamics without altering chemical composition, yet how crystal anisotropy modifies this control remains unclear. We combine density-functional-theory (DFT)-calibrated lattice-dynamical calculations with angle-matched untwisted controls to study puckered bilayer SnSe across seven commensurate twist angles ($3.18^\circ$--$8.77^\circ$). At 300 K, twisting suppresses the band-path heat-capacity-weighted mean-square group velocity to 2.6--8.4\% of the control values; the suppression spans a broad frequency range rather than a few soft branches. The velocity response crosses over between $4.78^\circ$ and $3.82^\circ$ into a regime where the relaxed stacking textures and frequency-resolved velocity profiles become self-similar, with the normalized mean-square velocity ratio spanning only 11.1\% of its mean across the three smallest angles---a finite anisotropic crossover, not a singular-angle condition. Direct DFT--MACE force-constant agreement ($r=0.996$), uniform $4\times4\times1$ stability scans, and acoustic-sum-rule and path-density tests support the trend. The equilibrium trend is defined by six structures after excluding one relaxation-sensitive case. These results extend phonon twistronics to low-symmetry layered materials and identify crystal anisotropy as a key determinant of finite-angle phonon crossover behavior. △ Less

Submitted 15 August, 2026; originally announced August 2026.

Comments: 15 pages, 8 figures

Imaginary Gauge Fields for One-Way Transparency and Absorption in a Passive Metasurface

Authors: Qingdong Yang, Zhongfu Li, Xinhua Wen, Oubo You, Yi Wang, Shuang Zhang

Abstract: Electromagnetic nonreciprocity enables waves to respond differently when their propagation direction is reversed, forming the basis of isolation, directional routing, and asymmetric energy control. A central challenge is to achieve high transmission in one direction while inducing strong absorption in the opposite direction within a single passive element, as passive material dissipation typically… ▽ More Electromagnetic nonreciprocity enables waves to respond differently when their propagation direction is reversed, forming the basis of isolation, directional routing, and asymmetric energy control. A central challenge is to achieve high transmission in one direction while inducing strong absorption in the opposite direction within a single passive element, as passive material dissipation typically attenuates both propagation channels equally. Here we demonstrate that an imaginary artificial gauge field can redistribute net dissipation between opposite directions in a passive structure. By synthesizing a moving-type magnetoelectric response from gyromagnetic elements and subwavelength metallic resonators, we realize a polarization-independent metasurface in which the forward wave weakly excites the dissipative resonance through destructive current interference, whereas the backward wave strongly activates the same lossy mode through constructive interference. The fabricated metasurface transmits more than 80% of the incident power from one side while absorbing more than 80% from the opposite side, with low reflection from both directions. Near-field mapping of the surface electric field provides direct real-space evidence of this gauge-controlled, direction-dependent charge accumulation and dissipation. This work establishes imaginary gauge fields as a powerful route for engineering dissipative landscapes in open wave systems and opens a pathway toward compact, passive, reflectionless isolators and nonreciprocal absorbers. △ Less

Submitted 14 August, 2026; originally announced August 2026.

Comments: 19pages, 4 figures,

Transient Chirp Dynamics in Terahertz Quantum Cascade Lasers

Authors: Xianglong Bi, Xuhong Ma, Wenjian Wan, Binbin Liu, Guibin Liu, Ziping Li, Yanming Lu, Zhiwei Qin, Yunxiang Zhu, Ziyu Guo, J. C. Cao, Hua Li

Abstract: Laser frequency chirp is a ubiquitous dynamical process in semiconductor lasers, vital for frequency-modulated photonic systems. In the mid-infrared (MIR) and terahertz (THz) ranges, quantum cascade lasers (QCLs) are ideal sources with high power, narrow linewidth and compact size. While chirp dynamics in MIR QCLs have been studied, the transient chirp behavior of THz QCLs--particularly the therma… ▽ More Laser frequency chirp is a ubiquitous dynamical process in semiconductor lasers, vital for frequency-modulated photonic systems. In the mid-infrared (MIR) and terahertz (THz) ranges, quantum cascade lasers (QCLs) are ideal sources with high power, narrow linewidth and compact size. While chirp dynamics in MIR QCLs have been studied, the transient chirp behavior of THz QCLs--particularly the thermal chirp on microsecond to millisecond timescales--remains largely unexplored. Here, we experimentally investigate transient thermal chirp dynamics in single-mode THz QCLs via an on-chip heterodyne scheme. Twin monolithically integrated single-mode QCLs are used: one pulsed QCL as the device under test, and one continuous-wave (CW) QCL serving as both local oscillator (LO) and ultrafast THz detector. The frequency chirp is mapped to the radio-frequency (RF) domain by heterodyne down-conversion. By varying current and temperature, we observe three distinct chirp features: unidirectional down-chirp, V-shaped chirp, and unidirectional up-chirp. A two-node thermal model reproduces the dynamics with good agreement with experiments. Chirp dynamics in the multi-mode regime are also identified, showing the potential for sensitive dynamic spectral characterization. These findings deepen the understanding of THz QCL thermal chirp mechanisms and support applications in THz frequency combs, frequency-modulated continuous-wave (FMCW) radar, and high-speed coherent communications. △ Less

Submitted 14 August, 2026; originally announced August 2026.

High-dimensional Supermode Photonics Enabled by Hierarchical Supersymmetric Transformation

Authors: Yuan Zhong, Kaile Chen, Qi Lu, Chunxue Wang, Jingchi Li, Yuru Li, Zhaohui Li, Chao Lu, Xinchen Ji, Yikai Su, Lu Sun

Abstract: Modes provide a fundamental degree of freedom for photonic information processing, yet conventional multimode waveguides exhibit non-equidistant effective-index distributions, making closely spaced modes vulnerable to intermodal crosstalk. Supermode photonics can overcome this limitation by geometrically engineering coupled waveguide arrays to realize large and equidistant effective-index spacing,… ▽ More Modes provide a fundamental degree of freedom for photonic information processing, yet conventional multimode waveguides exhibit non-equidistant effective-index distributions, making closely spaced modes vulnerable to intermodal crosstalk. Supermode photonics can overcome this limitation by geometrically engineering coupled waveguide arrays to realize large and equidistant effective-index spacing, but precise supermode excitation and detection remain challenging at the subwavelength scale. Here, we report a hierarchical second-order discrete supersymmetric (DSUSY) transformation method that enables high-purity excitation and extraction of arbitrary target supermodes in a compact and scalable architecture. We experimentally demonstrate six-supermode multiplexing systems on silicon-on-insulator and silicon nitride platforms. Benefiting from the large supermode index spacing and the isospectrality of DSUSY transformations, the fabricated devices exhibit low insertion losses (<2.6 dB) and intermodal crosstalk (<-11.1 dB) for all channels over a 100-nm wavelength range. A high-speed transmission experiment on the silicon device achieves an aggregate data rate of 1.2 Tbit/s, with all channel bit error rates below the 7% hard-decision forward-error-correction threshold. The method can further support polarization-insensitive architectures, enabling compact polarization-supermode hybrid multiplexing. This work provides a scalable route toward high-dimensional supermode photonics for high-capacity optical interconnects, highly parallel AI optical computing, and high-dimensional quantum information processing. △ Less

Submitted 11 August, 2026; originally announced August 2026.

Comments: 23 pages, 6 figures

Memristive Behavior and Mechanism in Solid-State Nanopores

Authors: Zhiwei Li, Ngan Hoang Pham, Shi-Li Zhang, Chenyu Wen

Abstract: Nanofluidic memristors whose conductance evolves through history-dependent ionic transport and dynamic interfacial processes are promising building blocks for ionic neuromorphic applications. However, most existing designs rely on biological nanopores, polymers, and two-dimensional materials, which limit scalable fabrication and poses challenges to integration of ionic computing circuits and syste… ▽ More Nanofluidic memristors whose conductance evolves through history-dependent ionic transport and dynamic interfacial processes are promising building blocks for ionic neuromorphic applications. However, most existing designs rely on biological nanopores, polymers, and two-dimensional materials, which limit scalable fabrication and poses challenges to integration of ionic computing circuits and systems. Here, we report memristive behaviors of silicon-based solid-state nanopores (SSNPs) fabricated based on wafer-scale semiconductor processes. The SSNPs exhibit hysteretic current-voltage characteristics with a dependence on voltage sweeping frequency, electrolyte concentration, and nanopore geometry. To investigate the physical origin of their memory feature, the measured current of the SSNPs is decomposed into resistive, capacitive, and memristive components. An ion adsorption-desorption kinetics is developed to explain and predict the memristive behavior. A dynamical system analysis further reveals that the memristive behavior arises from delayed relaxation, thereby linking the measured hysteresis to the observed adaptive ionic response. Together, these findings establish native SSNPs as scalable ionic memristive elements and provide a generalized electrokinetic mechanism for memristive behavior under nanoconfinement. The resulting analytical framework connects device characterization with the underlying dynamics, deepens mechanism understanding, and guides the design of ionic neuromorphic devices. △ Less

Submitted 5 August, 2026; originally announced August 2026.

Generation of dense relativistic electron beams via vortex laser-driven self-generated magnetic pinching

Authors: Mingxuan Wei, Fengyu Sun, Zhongpeng Li, Xichen Hu, Huiting Ma, Guangwei Lu, Zhuofan Zhang, Lijie Cui, Qijin Zhang, Mengjiao Wang, Weijun Zhou, Qian Zhao, Wenqing Wei, Yi Xu, Zongxin Zhang, Jiayi Qian, Jiacheng Zhu, Xiaoyan Liang, Min Chen, Wenpeng Wang, Jian-Xing Li, Wenchao Yan, Yuxin Leng, Jie Zhang

Abstract: In multi-petawatt laser plasma accelerators, achieving high-density relativistic electron beams is typically accompanied by large transverse divergence, limiting the attainable effective electron density needed for high-flux interaction regimes relevant to laboratory astrophysics. Here we report experimental demonstration of self-generated magnetic pinching (SMP), a collective mechanism that activ… ▽ More In multi-petawatt laser plasma accelerators, achieving high-density relativistic electron beams is typically accompanied by large transverse divergence, limiting the attainable effective electron density needed for high-flux interaction regimes relevant to laboratory astrophysics. Here we report experimental demonstration of self-generated magnetic pinching (SMP), a collective mechanism that actively regulates transverse beam dynamics using a Laguerre-Gaussian laser at strong relativistic intensity (~8 x 10^19 W/cm^2) interacting with an underdense plasma. The electron beam evolves from a two-lobe high-charge injection structure into a compressed, high-density profile, yielding a threefold reduction in divergence and nearly an order-of-magnitude enhancement in effective beam density compared with a Gaussian driver. Particle-in-cell simulations agree with the experimental observations and reveal that a self-generated azimuthal magnetic field governs the electron dynamics within the SMP regime, which is defined by the forming condition S = 0.717 l a0 [ne(10^18 cm^-3)]^-3/4 = 1, where l, a0, and ne are topological charge, laser amplitude, and plasma density, respectively. A transient kick from a dense inner sheath electron population drives collective magnetic pinching, transforming an initially separated electron distribution into a compressed and well-collimated beam. For higher-power laser systems, the forming condition can be extended to higher plasma densities and larger orbital angular momentum modes, potentially enabling electron beams with charges exceeding several nC and effective densities above 10^19 cm^-3. This mechanism provides a route to overcoming transverse expansion and enhancing rare interaction processes relevant to high-flux particle sources. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Nonlinear asymptotic bubble growth in single-mode spherical Rayleigh-Taylor instability

Authors: De-Hua Zhang, Shi-Heng Wang, Ke-Jian Qian, Zhu-Jun Li, Rui Yan, Hang Ding

Abstract: We present an analytical model for the nonlinear growth of a single-mode Rayleigh-Taylor instability (RTI) bubble in spherical geometry. The model captures the bubble growth along the polar axis, spanning the linear to nonlinear regimes, for arbitrary Atwood numbers and under both converging- and diverging-gravity configurations. The model predicts that the bubble acceleration approaches an asympt… ▽ More We present an analytical model for the nonlinear growth of a single-mode Rayleigh-Taylor instability (RTI) bubble in spherical geometry. The model captures the bubble growth along the polar axis, spanning the linear to nonlinear regimes, for arbitrary Atwood numbers and under both converging- and diverging-gravity configurations. The model predicts that the bubble acceleration approaches an asymptotic value in the nonlinear stage. The spherical geometry is found to enhance the RTI bubble growth relative to planar and cylindrical configurations with the same effective perturbation wavenumber in the converging-gravity cases, whereas it mitigates the bubble growth in the diverging-gravity cases. The model predictions show favorable agreement with direct numerical simulations. △ Less

Submitted 31 July, 2026; originally announced July 2026.

Comments: 14 pages, 5 figures

Quantum Computing Enabled ab initio Molecular Dynamics Simulations

Authors: Susanta Das, Subhamoy Bhowmik, Zhen Li, Milana Bazayeva, Danil Kaliakin, Akhil Shajan, Kenneth M. Merz Jr

Abstract: We demonstrate a quantum-classical workflow for ab initio molecular dynamics (AIMD) in which quantum measurements from a chemistry-inspired LUCJ ansatz are post-processed using Sample-based Quantum Diagonalization (SQD) to recover determinant subspaces and deliver energies and analytical nuclear gradients for dynamics. As an exact benchmark, we use full configuration interaction (FCI) in the STO-3… ▽ More We demonstrate a quantum-classical workflow for ab initio molecular dynamics (AIMD) in which quantum measurements from a chemistry-inspired LUCJ ansatz are post-processed using Sample-based Quantum Diagonalization (SQD) to recover determinant subspaces and deliver energies and analytical nuclear gradients for dynamics. As an exact benchmark, we use full configuration interaction (FCI) in the STO-3G basis, enabling a direct assessment of the accuracy of SQD. In gas-phase benchmarks, SQD reproduces FCI energies and gradients to within 1 kcal mol$^{-1}$ of the FCI reference and yields stable AIMD trajectories. In explicit-solvent QM/MM simulations, SQD retains this agreement, matching FCI energy fluctuations and RMS gradient profiles and reproducing solute-solvent structure as quantified by radial distribution functions. Overall, these benchmarks establish LUCJ+SQD as a practical route for integrating current quantum hardware into QM/MM molecular dynamics and provide an early demonstration of condensed-phase QM/MM dynamics driven by a quantum electronic-structure engine. △ Less

Submitted 30 July, 2026; originally announced July 2026.

Comments: 45 pages, 7 figures. Supporting Information included as an ancillary file

Embedded quantum computing for many-body surface reaction

Authors: Dedong Wan, Xiaopeng Li, Yi Fan, Jie Liu, Xiongzhi Zeng, Zhenyu Li

Abstract: Predictive simulations of catalytic interfaces require correlated electronic-structure treatments that describe localized chemical transformations while retaining the influence of the extended metallic environment. We introduce QC-DFET, a quantum-computing density-functional embedding framework that maps surface-reaction active spaces to compact, environment-aware qubit Hamiltonians. A reaction-co… ▽ More Predictive simulations of catalytic interfaces require correlated electronic-structure treatments that describe localized chemical transformations while retaining the influence of the extended metallic environment. We introduce QC-DFET, a quantum-computing density-functional embedding framework that maps surface-reaction active spaces to compact, environment-aware qubit Hamiltonians. A reaction-consistent active-space protocol preserves orbital continuity along reaction coordinates, while quantum-selected configuration interaction based on measurements from the Zuchongzhi superconducting quantum processor and strongly contracted perturbation theory capture static and dynamic correlation. On Cu(111), QC-DFET treats active spaces up to 28 qubits and is validated through a hierarchy of experimentally constrained surface-chemistry challenges. H2 dissociation/desorption tests balanced bond breaking and recombination barriers, CO adsorption tests site selectivity and metal-adsorbate bonding, and formate hydrogenation tests competing hydrogenation branches with different kinetic and thermodynamic signatures. Across these cases, QC-DFET reproduces bidirectional H2 barriers, recovers the observed top-site preference and adsorption strength of CO, and reconciles the experimentally benchmarked H2COO* reverse barrier with the lower forward barrier to HCOOH*. These results establish embedded quantum computing as a practical route to correlated surface-reaction energetics. △ Less

Submitted 29 July, 2026; originally announced July 2026.

Twin reflections from a moving space-time boundary

Authors: Yukun Yang, Hao Hu, Youxiu Yu, Linyang Zou, Liangliang Liu, Jiang Xiong, Baile Zhang, Francisco J. Garcia-Vidal, Zhuo Li, Yu Luo

Abstract: An interluminal interface, a space-time boundary propagating at a velocity between the group velocities of the surrounding media, enables extraordinary wave phenomena such as nonreciprocal amplification and analogues of Hawking radiation. A particularly intriguing prediction is that such an interface splits an incident wave into three outgoing waves: one transmitted and two reflected. Yet, despite… ▽ More An interluminal interface, a space-time boundary propagating at a velocity between the group velocities of the surrounding media, enables extraordinary wave phenomena such as nonreciprocal amplification and analogues of Hawking radiation. A particularly intriguing prediction is that such an interface splits an incident wave into three outgoing waves: one transmitted and two reflected. Yet, despite decades of theoretical study, this triple-wave scattering has remained experimentally unobserved, owing to stringent requirements on interface velocity and modulation speed. Here, we introduce a programmable spatiotemporal microstrip transmission-line platform that realizes step-modulated interluminal interfaces with controlled velocity. Using this system, we report the direct observation of bi-reflection from an interluminal interface, confirming the emergence of two distinct reflected waves alongside a transmitted one. Measured frequencies and scattering coefficients show excellent agreement with longstanding theoretical predictions. Furthermore, we reveal that the two reflections possess fundamentally different causal symmetries: one is spatially inverted, while the other is time-reversed. These findings resolve a half-century-old puzzle in moving-boundary electrodynamics and establish a versatile experimental platform for studying wave interaction with dynamic interfaces. Our work opens pathways to velocity-independent scattering devices, broadband frequency conversion, and advanced spatiotemporal wave engineering. △ Less

Submitted 29 July, 2026; originally announced July 2026.

Phase singularity enabled polarization switchable analog spatial differentiation in an atomic MoS$_2$ planar Fabry-Pérot cavity

Authors: Zhonglin Li, Yingying Wang, Jiawei Kang, Yangyang Zhang, Wenjun Liu, Zexiang Shen

Abstract: Reconfigurable analog optical computing requires rapid and efficient switching between core mathematical operations, such as first- and second-order spatial differentiation. Here, we demonstrate a monolayer MoS$_2$ integrated a planar Fabry-Pérot (F-P) cavity that performs polarization switchable analog spatial differentiation under oblique incidence. By exploiting polarization dependent phase sin… ▽ More Reconfigurable analog optical computing requires rapid and efficient switching between core mathematical operations, such as first- and second-order spatial differentiation. Here, we demonstrate a monolayer MoS$_2$ integrated a planar Fabry-Pérot (F-P) cavity that performs polarization switchable analog spatial differentiation under oblique incidence. By exploiting polarization dependent phase singularities, the device satisfies distinct optical transfer functions for different-order differentiation at the same operating condition. As a result, first-order and second-order derivatives of input images are experimentally realized by simply switching the incident polarization. These results establish the planar cavity as a compact reconfigurable spatial differentiator, whose computational order is controlled solely by light polarization. This approach provides a fast, convenient, and integration friendly strategy for tunable optical computing, and enables polarization switchable edge detection for image processing, with potential applications in real-time object recognition, feature extraction, and optical data compression. △ Less

Submitted 23 July, 2026; originally announced July 2026.

Comments: 25 pages, 5 figures

The Influence of Interior Noise on Just-Noticeable Speed Differences in Conventional and Electric Vehicles

Authors: Zhenxian Li, Etienne Parizet, Claudio Colangeli

Abstract: Electric vehicles (EVs) and internal-combustion-engine vehicles (ICEVs) differ fundamentally in their in-cabin acoustics, notably the attenuation or absence of engine-order content. Prior work reports associations between reduced engine sound, speed underestimation, and poorer speed maintenance; however, research on how EVs' new sound affects speed perception and control is scarce, and most newer… ▽ More Electric vehicles (EVs) and internal-combustion-engine vehicles (ICEVs) differ fundamentally in their in-cabin acoustics, notably the attenuation or absence of engine-order content. Prior work reports associations between reduced engine sound, speed underestimation, and poorer speed maintenance; however, research on how EVs' new sound affects speed perception and control is scarce, and most newer studies focus on comfort and subjective pleasantness rather than speed perception. Addressing this gap, the present study uses a two-interval, two-alternative forced-choice (2AFC) paradigm to directly measure just-noticeable differences (JNDs) in speed under ICEV, EV, and silent conditions. Thirty participants performed a 2AFC task in which, on each trial, they viewed two first-person highway clips (reference vs. comparison) and indicated which appeared faster. Results from ANOVA and post-hoc tests indicate that at the 40 km/h reference speed participants showed no clear differences across sound conditions, whereas at 100 km/h there were marked differences in JND: mean values were 1.93 km/h (ICEV), 3.48 km/h (EV), and 5.15 km/h (silence). A psychoacoustic parameter analysis suggests that this effect is not explained by speed-dependent changes in loudness or sharpness; we interpret that RPM-related, clearly audible frequency shifts in ICEV provide the primary contributory cue. For EV NVH or artificial sound design, enhancing speed-contingent, trackable spectral cues while respecting comfort may help maintain drivers' ability to discriminate speed differences. △ Less

Submitted 21 July, 2026; originally announced July 2026.

Journal ref: 14th International Styrian Noise, Vibration \& Harshness Congress: The European Automotive Noise Conference, Jun 2026, Graz, Austria

Final assessment of radioactive impurities in the JUNO detector

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

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

Submitted 19 July, 2026; originally announced July 2026.

Broadband suspended lithium tantalate Mach-Zehnder modulator achieving a 460 Gbit/s net data rate

Authors: Zihan Li, Alexander Kotz, Adrian Schwarzenberger, Christian Koos, Tobias J. Kippenberg

Abstract: Thin-film lithium tantalate photonic integrated circuits have recently been demonstrated as a promising next-generation electro-optic platform, offering favorable properties including reduced DC drift, higher optical power handling, and lower birefringence compared to lithium niobate. However, high-speed LiTaO3 modulators reported to date have predominantly relied on silicon substrates, whose larg… ▽ More Thin-film lithium tantalate photonic integrated circuits have recently been demonstrated as a promising next-generation electro-optic platform, offering favorable properties including reduced DC drift, higher optical power handling, and lower birefringence compared to lithium niobate. However, high-speed LiTaO3 modulators reported to date have predominantly relied on silicon substrates, whose large dielectric constant compromises microwave velocity matching and imposes RF conductor losses that limit the achievable electro-optic bandwidth. Here, we implement a silicon substrate undercut technique to suspend the electrode region of lithium-tantalate-on-insulator (LTOI) Mach-Zehnder modulators (MZMs), effectively decoupling the traveling-wave electrodes from the high-permittivity silicon handle wafer, thereby reducing microwave losses. In addition, the undercut removes any susceptibility to parasitic surface conductance (PSC) induced losses of the oxide-silicon interface. The fabricated MZM achieves a 3 dB electro-optic bandwidth of 110 GHz, with a half-wave voltage of 5.1 V for an 8 mm-long device. Exploiting the extended bandwidth, we demonstrate a high single-lane intensity-modulation and direct-detection (IMDD) net data rate of 460 Gbit/s using PAM8 signaling. These results establish silicon substrate undercut as an effective and process-compatible pathway to unlock the full electro-optic potential of lithium tantalate on its native silicon-based wafer platform. △ Less

Submitted 19 July, 2026; originally announced July 2026.

Quantum-Centric Geometry Optimization with Wave-Function-Based Embedding

Authors: Danil Kaliakin, Akhil Shajan, Fangchun Liang, Zhen Li, Kenneth M. Merz Jr

Abstract: The EWF-(FCI,SQD) method, a wave-function-based embedding approach combining full configuration interaction (FCI) and sample-based quantum diagonalization (SQD), is a promising new tool for the simulation of molecular systems. However, applications of EWF-(FCI,SQD) have so far been limited to single-point calculations, whereas the study of complex chemical processes requires the ability to explore… ▽ More The EWF-(FCI,SQD) method, a wave-function-based embedding approach combining full configuration interaction (FCI) and sample-based quantum diagonalization (SQD), is a promising new tool for the simulation of molecular systems. However, applications of EWF-(FCI,SQD) have so far been limited to single-point calculations, whereas the study of complex chemical processes requires the ability to explore potential energy surfaces. In this work, we demonstrate geometry optimization with EWF-(FCI,SQD), scaling our simulations to molecules as large as menthone and benzidine within the STO-3G basis set. Without fragmentation, these systems comprise 73 and 82 molecular orbitals respectively, presenting an intractable Hilbert space for conventional exact or high-level subspace solvers and establishing a clear necessity for fragmentation-based methodologies. The underlying fragment SQD simulations in the EWF-(FCI,SQD) geometry optimizations use up to 70 qubits. The resulting geometries show exceptional accuracy relative to the classical reference, with deviations below 4 picometers. △ Less

Submitted 5 August, 2026; v1 submitted 17 July, 2026; originally announced July 2026.

Linear Gyrokinetic Simulations of Micro-tearing Mode: Local versus Global

Authors: Yifei Liu, Haotian Chen, Yao Yao, Zhengji Li, Jiquan Li, Wei Chen

Abstract: A systematic comparison of local and global linear gyrokinetic simulations of micro-tearing modes (MTMs) is performed using the GENE code. The analysis spans diverse plasma parameters, including the core regions with normal and weak magnetic shear, as well as the pedestal region with the strong plasma non-uniformity. The global simulations reveal a distinct MTM type characterized by a `parity mixi… ▽ More A systematic comparison of local and global linear gyrokinetic simulations of micro-tearing modes (MTMs) is performed using the GENE code. The analysis spans diverse plasma parameters, including the core regions with normal and weak magnetic shear, as well as the pedestal region with the strong plasma non-uniformity. The global simulations reveal a distinct MTM type characterized by a `parity mixing' mode structure, which can be significantly destabilized by trapped electrons. Moreover, in contrast to electrostatic drift wave instabilities, the current layer width ($ Δ_c $) is identified as the crucial factor determining the importance of global effects. The MTM in the core region exhibits the slab-like feature with narrow $ Δ_c $, leading to high consistency between local and global results. However, in the pedestal region, the steep pressure gradient broadens $Δ_c$, driving quantitative deviations when $Δ_c$ becomes comparable to the plasma pressure gradient scale length. For high-$n$ MTMs, $ Δ_c $ can exceed the distance between adjacent mode rational surfaces. The resulted overlapping of current layers enhances the toroidal mode coupling effect, accounting for the substantial discrepancies observed between local and global simulations. △ Less

Submitted 17 July, 2026; originally announced July 2026.

Inunda: A GPU-Native, Agent-enabled, Differentiable Solver for High-Resolution Flood Inundation Modeling

Authors: Zhi Li

Abstract: Predicting where floodwater goes and how deep it gets, at high resolution and across large domains, remains computationally expensive with conventional hydraulic solvers, while purely data-driven surrogates are fast but lack physical guarantees and generalize poorly beyond their training events. We present Inunda, a GPU-native flood inundation model that solves the two-dimensional shallow water eq… ▽ More Predicting where floodwater goes and how deep it gets, at high resolution and across large domains, remains computationally expensive with conventional hydraulic solvers, while purely data-driven surrogates are fast but lack physical guarantees and generalize poorly beyond their training events. We present Inunda, a GPU-native flood inundation model that solves the two-dimensional shallow water equations. Inunda uses a mass-conservative local-inertial scheme and runs multi-day events over millions of cells in minutes on a single GPU. Because every operator is autograd-compatible, the solver is differentiable by construction: model parameters can be estimated by gradient descent against gage observations through reverse-mode automatic differentiation of the full simulation. We demonstrate Inunda on three case studies. For a hindcast of Hurricane Harvey (2017) in Harris County, Texas, Inunda matches surveyed high-water marks to a mean absolute error of 0.67 m, competitive with or better than a suite of established flood models, and reaches a median gage water-level Nash Sutcliffe efficiency of +0.72, more than double the +0.31 of the operational National Water Model v3.0. For the July 2025 Central Texas flash flood, Inunda is driven by an 18-member 1-km convection-allowing precipitation ensemble to produce probabilistic flood forecasts whose skill improves systematically as lead time to the crest shortens. For a post-fire flash-flood application in the Rio Ruidoso burn scar, differentiable calibration recovers the saturated hydraulic conductivity as a spatially explicit field at the model's own resolution and traces its multi-year post-fire recovery. Inunda provides an open, end-to-end pipeline for real-event flood modeling that couples the accuracy of physics-based hydraulics with the calibration and coupling advantages of modern differentiable programming. △ Less

Submitted 10 July, 2026; originally announced July 2026.

Implicit discretization schemes for full-kinetic ion and drift-kinetic electron simulations

Authors: Zilong Li, Yang Chen, Haotian Chen, Lei Ye, Zhe Gao, Wei Chen

Abstract: We present a new electromagnetic plasma simulation model with full-kinetic ions and drift-kinetic electrons. This model (termed as FIDES) solves the electric field using the implicit perpendicular Ohm's law and a novel implicit parallel Ampere's law, where the latter requires an implicit scheme for the parallel electric field in advancing the electron weights. To suppress unphysical high-frequency… ▽ More We present a new electromagnetic plasma simulation model with full-kinetic ions and drift-kinetic electrons. This model (termed as FIDES) solves the electric field using the implicit perpendicular Ohm's law and a novel implicit parallel Ampere's law, where the latter requires an implicit scheme for the parallel electric field in advancing the electron weights. To suppress unphysical high-frequency instabilities, ion weights are advanced using an implicit scheme for perpendicular electric fields. Simulations of perpendicular and parallel waves validate the model's capability in handling high-frequency physics. Low-frequency wave simulations demonstrate that the implicit parallel Ampere's law can mitigate the cancellation problem more effectively than the conventional schemes using the parallel Ohm's law. To reduce the numerical damping from implicit time-stepping, we develop a second-order scheme for particle pushing. Meanwhile, an integrated strategy combining the first- and second-order schemes is employed to suppress odd-even decoupling while maintaining the accuracy of the second-order formulation. △ Less

Submitted 9 July, 2026; originally announced July 2026.

Joint Discrete-Continuous Flow Matching for Open-Vocabulary Inverse Design of Multilayer Optical Coatings

Authors: Zhiyi Li, Yuheng Jin, Yidan Huang, Nan Chen, Hongyan Fu, Yikun Bu

Abstract: Amortized neural inverse design typically remains closed-world: component choices are fixed vocabulary tokens, coordinate grids are frozen at training time, and continuous variables are discretized into sequence tokens. Multilayer optical coatings are an industrially important instance, coupling material sequence, layer thickness and wavelength-dependent response. We present IrisFlow, a query-base… ▽ More Amortized neural inverse design typically remains closed-world: component choices are fixed vocabulary tokens, coordinate grids are frozen at training time, and continuous variables are discretized into sequence tokens. Multilayer optical coatings are an industrially important instance, coupling material sequence, layer thickness and wavelength-dependent response. We present IrisFlow, a query-based, open-vocabulary flow-matching framework instantiated in coatings: the target reflectance/transmittance spectrum, wavelength grid, candidate-material optical constants and layer count are supplied at query time. Candidate materials enter as wavelength-aware optical tokens rather than learned identities; material sequences are sampled by discrete flow matching over the query's candidate bank, thicknesses by continuous flow matching without discretization. A single 136M-parameter model designs 2-100-layer stacks. Across a 224-task benchmark it reconstructs in-distribution targets faithfully and retains same-order accuracy on a 15-material held-out bank without retraining; it reconstructs bands up to 1100 nm beyond its training envelope, designs against analytic application specifications and outperforms an autoregressive baseline on that baseline's material library. With optical constants calibrated to our deposition process, IrisFlow designs four color-displaying coolers, fabricated by ion-assisted evaporation: the three chromatic devices reach a CIEDE2000 color error of 3.1-5.2 while retaining 93-95% solar near-infrared reflectance, demonstrating open-vocabulary design carried through to fabricated coatings. △ Less

Submitted 9 July, 2026; originally announced July 2026.

Non-Hermitian Dirac Vortex: Minimal Theory for Topological-Cavity Surface-Emitting Laser

Authors: Zong-Liang Li, Guang-Rui Li, Le-Chen Yang, Zhong Wang, Ling Lu

Abstract: We construct a non-Hermitian Dirac-vortex model that combines a complex-mass winding with an infinite-imaginary-potential boundary, extending the Jackiw-Rossi and neutrino-billiard models to the dissipative regime. Moreover, this model serves as a minimal theory for the recently proposed topological-cavity surfaceemitting laser (TCSEL): the imaginary mass encodes vertical radiation loss and the ab… ▽ More We construct a non-Hermitian Dirac-vortex model that combines a complex-mass winding with an infinite-imaginary-potential boundary, extending the Jackiw-Rossi and neutrino-billiard models to the dissipative regime. Moreover, this model serves as a minimal theory for the recently proposed topological-cavity surfaceemitting laser (TCSEL): the imaginary mass encodes vertical radiation loss and the absorbing boundary defines the active region. We derive closed-form expressions for the modal frequencies, thresholds, and tunable vectorbeam polarizations, which are validated experimentally. Our work provides a rare example in which an analytical non-Hermitian topological theory captures the essential physics for engineering practical optoelectronic devices. △ Less

Submitted 6 July, 2026; originally announced July 2026.

Comments: 8 pages, 7 figures

Differentiable OPLS Force Field Parameterization for Ionic Electrolytes and High-Throughput Application to Lithium-ion Batteries

Authors: Haichao Huang, Zilin Chen, Qi Liu, Tianqi Zhao, Yunpei Liu, Guotao Qiu, Jianhui Chen, Zhen Li, Wenshuo Liang, Minsung Cho, Manxue Zhang, Feiyu Kang, Xiaolong Zou, Yidan Cao, Xushan Zhao, Ziqi Cheng, Ye Mei

Abstract: The rational design of ionic electrolytes for lithium-ion batteries (LIBs) is severely constrained by the vast solvent-salt combinatorial space and low efficiency of empirical trial-and-error. While molecular dynamics (MD) bridges microscopic solvation structures and macroscopic physicochemical properties, classical force fields often lack sufficient accuracy for multicomponent systems. To address… ▽ More The rational design of ionic electrolytes for lithium-ion batteries (LIBs) is severely constrained by the vast solvent-salt combinatorial space and low efficiency of empirical trial-and-error. While molecular dynamics (MD) bridges microscopic solvation structures and macroscopic physicochemical properties, classical force fields often lack sufficient accuracy for multicomponent systems. To address these challenges, we develop an automated differentiable OPLS-AA force field parameterization workflow tailored for general ionic electrolytes. It employs topology-guided atom typification to reduce parameter redundancy and optimizes Lennard-Jones parameters via the DMFF framework, with experimental density as the fitting target and ionic conductivity as an independent validation metric. Rigorous convergence tests yield a standardized simulation protocol with $\sim$100,000-atom systems and 35-40 ns NVT runs to ensure reliable transport property quantification. High-throughput MD simulations of over 10,000 formulations spanning 67 solvents and 15 lithium salts are conducted on the Tianqiong platform, generating a comprehensive dataset covering five core properties: density, dielectric constant, viscosity, diffusion coefficient, and ionic conductivity. t-SNE visualization reveals partial clustering of distinct salt chemistries, continuous property gradients with concentration and temperature, and internal physical self-consistency, with solvent composition identified as another key performance regulator. Together, the accurate transferable force field and large-scale dataset provide a solid foundation for data-driven rational design of ionic electrolytes. △ Less

Submitted 7 July, 2026; v1 submitted 5 July, 2026; originally announced July 2026.

An orthogonal-to-non-orthogonal multiplexing format converter

Authors: Zijian Li, Chen Ding, Zixian Wei, Qiarong Xiao, Ka-Suen Lee, Chaoran Huang, Changyuan Yu, Chester Shu

Abstract: Time-frequency orthogonality has been a foundational principle in the historical development of optical communications, whether in dense wavelength division multiplexing (WDM) within long-reach high-capacity coherent optical transmission or in time-frequency division multiple access within short-reach dense passive optical networks. Towards next-generation agile optical networks, jointly programma… ▽ More Time-frequency orthogonality has been a foundational principle in the historical development of optical communications, whether in dense wavelength division multiplexing (WDM) within long-reach high-capacity coherent optical transmission or in time-frequency division multiple access within short-reach dense passive optical networks. Towards next-generation agile optical networks, jointly programmable orthogonal and non-orthogonal regulation offers flexible spectral allocation, ultra-dense packet distribution, and increased capacity. For bridging the fundamental differences of physical implementation, we propose and demonstrate a versatile orthogonal to non-orthogonal multiplexing format converter, with application to high-speed coherent optical transmission network enabled by a Talbot-based processor. The programmable Talbot-processed pumps coherently transfer and superpose optical signals of distinct wavelength channels onto a single channel through cross-phase modulation. We first demonstrate flexible conversion of two 80-Gbps WDM QPSK channels separated by 200-250 GHz into a non-orthogonal power-division multiplexing channel, while maintaining the high-quality encoded information in the digital domain. We then validate a digital-subcarrier-multiplexing dense access scenario in which eight 20-Gbps sub-channels are combined, converted, transmitted, and successfully decoded over a field-deployed fiber. The multiplexing format converter promises potential for applications in next-generation optical systems and networks with complex topologies and dense populations. △ Less

Submitted 5 July, 2026; originally announced July 2026.

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.

Numerical Study of Compressibility and Velocity Parameter Effects on Spatially Evolving Supersonic Turbulent Shear Layers

Authors: M. R. B. Shahadat, Z. Li, F. A. Jaberi, D. Livescu

Abstract: Direct Numerical Simulations (DNS) of a spatially developing supersonic turbulent shear layer are conducted for a range of convective Mach numbers ($M_c$) and velocity parameters ($λ$) to examine the effects of compressibility and advection on the growth rate, self-similarity, flow statistics, asymmetry, and entrainment of the layer. At distant downstream locations, self-similarity is attained for… ▽ More Direct Numerical Simulations (DNS) of a spatially developing supersonic turbulent shear layer are conducted for a range of convective Mach numbers ($M_c$) and velocity parameters ($λ$) to examine the effects of compressibility and advection on the growth rate, self-similarity, flow statistics, asymmetry, and entrainment of the layer. At distant downstream locations, self-similarity is attained for all cases. The self-similar region is identified by the collapse of normalized mean streamwise velocity, the constant peak of normalized Reynolds stresses, and the linear growth rate of the shear layer thickness and momentum thickness. Despite significant variations in lower-order and higher-order statistics across different $M_c$ and $λ$ values, profiles of all turbulence quantities examined collapse within the self-similar region using our proposed self-similar scalings. The self-similar forms of continuity, momentum, and energy equations have been formulated, incorporating compressibility and centerline shifts. The self-similar normalized density distribution inside the layer is used to explain the effects of compressibility on various flow statistics, including the far-field cross-stream velocity. The density variation is linked to dissipation effects as revealed by our analysis of the self-similar energy equation. An approximate equation for the cross-stream velocity is developed, and the profiles of cross-stream velocity obtained from this equation show good agreement with the DNS results. A geometric interpretation of the entrainment ratio is presented, and the approximate equation for the cross-stream velocity is used to provide a general closed-form expression of the entrainment ratio. The entrainment ratio increases with $M_c$ and $λ$, favoring excess entrainment on the high-speed side. △ Less

Submitted 1 July, 2026; originally announced July 2026.

From Materials Database to Materials Bank: Assetizing Data for AI Driven Materials Innovation

Authors: Chenyao Ma, Di Zhang, Weibo Gong, Wei Du, Rui Su, Yuhang Chen, Kan Xu, Huan Gu, Limin Li, Piao Ma, Zhenghao Li, Hao Li

Abstract: Driven by high-throughput experimentation, computational modeling, and artificial intelligence (AI), materials data has expanded at an unprecedented rate. Conventional materials databases function only as passive repositories, archiving raw experimental records indiscriminately including both successful and failed data, without systematic value filtering or asset management. This creates a critica… ▽ More Driven by high-throughput experimentation, computational modeling, and artificial intelligence (AI), materials data has expanded at an unprecedented rate. Conventional materials databases function only as passive repositories, archiving raw experimental records indiscriminately including both successful and failed data, without systematic value filtering or asset management. This creates a critical gap between massive data accumulation and actionable innovation, hindering the identification of high-potential materials and industrial translation. To address this bottleneck, we propose an industrialization-oriented Materials Bank, a dedicated valuefiltering and assetization layer that operates beyond traditional databases. It does not merely curate high-quality data but systematically elevates qualified candidates into standardized, upgradable materials assets via a multi-dimensional BankCard framework covering scientific validity, synthesis feasibility, application readiness, and industrial value. By unifying databases, AI models, automated experimentation, and multi-criteria assessment into a cohesive closed-loop ecosystem, the Materials Bank establishes a clear trajectory from data to knowledge, candidate, asset, and product. It serves not as an enhanced database or screening tool, but as a decision infrastructure bridging academic discovery and industrial demand, offering a scalable paradigm to accelerate AI-driven materials innovation and deliver tangible real-world impact. △ Less

Submitted 25 July, 2026; v1 submitted 30 June, 2026; originally announced June 2026.

Journal ref: AI for Materials 2026, 1(1), 9

Scenario-conditioned flow matching for probabilistic generation of three-component ground-motion waveforms

Authors: Yi Ding, Jinjun Hu, Su Chen, Xianwei Liu, Zhongxiang Zhang, Zongchao Li, Xiaojun Li, Lili Xie

Abstract: Performance-based seismic risk assessment requires three-component acceleration histories compatible with specified source, path, and site conditions. Conventional ground-motion prediction equations provide scalar intensity measures, while many generative waveform models learn amplitude and waveform shape within a single high-dimensional target. We present WaveFlowGMM, a two-stage probabilistic gr… ▽ More Performance-based seismic risk assessment requires three-component acceleration histories compatible with specified source, path, and site conditions. Conventional ground-motion prediction equations provide scalar intensity measures, while many generative waveform models learn amplitude and waveform shape within a single high-dimensional target. We present WaveFlowGMM, a two-stage probabilistic ground-motion model that uses peak ground acceleration (PGA) as an amplitude interface between scenario conditioning and waveform generation. The amplitude stage uses physics-informed symbolic learning to estimate component-wise PGA medians and a full cross-component covariance. The waveform stage uses few-step AlphaFlow in an invertible wavelet-packet coefficient space to generate normalised three-component histories that are rescaled by sampled PGA. Tests on an event-level NGA-West2 holdout set show that the generated motions recover the main magnitude, distance, and site scaling, keep peak and spectral residuals close to zero, preserve three-component amplitude dependence, and yield velocity and displacement histories without systematic drift after integration of the generated three-component acceleration histories. The framework provides an interpretable and computationally efficient candidate component for waveform-level seismic hazard and risk analysis. △ Less

Submitted 30 June, 2026; originally announced June 2026.

Quantum Computations on Fusion Blanket Molten Salts

Authors: Susanta Das, Thiago J. Pinheiro Dos Santos, Subhamoy Bhowmik, Milana Bazayeva, Zhen Li, Akhil Shajan, Danil Kaliakin, Fangchun Liang, Vyacheslav S. Bryantsev, Al Geist, Abigail McClain Gomez, Thaddeus Pellegrini, Robert Walkup, Seetharami R. Seelam, Mario Motta, Kenneth M. Merz, Jr., Thomas Beck

Abstract: Molten salts such as FLiBe (2LiF--BeF$_2$) are leading blanket materials for breeding and recovering tritium in fusion reactors. Predicting tritium speciation requires accurate electronic ground-state energies for representative molten-salt clusters, a demanding task for correlated electronic-structure methods. Here we report the first application of heterogeneous quantum--classical computing to t… ▽ More Molten salts such as FLiBe (2LiF--BeF$_2$) are leading blanket materials for breeding and recovering tritium in fusion reactors. Predicting tritium speciation requires accurate electronic ground-state energies for representative molten-salt clusters, a demanding task for correlated electronic-structure methods. Here we report the first application of heterogeneous quantum--classical computing to tritium binding in FLiBe. Clusters drawn from ab initio molecular dynamics are partitioned by an embedded-wavefunction (EWF) method into atom-centered fragments, and the largest fragments are solved on IBM quantum hardware using extended sample-based quantum diagonalization (ext-SQD). Across nine clusters, the heterogeneous quantum--classical workflow reproduces fragment ground-state energies with agreement to full configuration interaction within 0.7~kcal/mol and a mean absolute deviation of 0.3~kcal/mol. In contrast, fragmented and unfragmented conformational energy differences and tritium binding energies differ by 12~kcal/mol and 110~kcal/mol on average, respectively, identifying fragment construction rather than fragment solution as the dominant source of algorithmic bias. To the best of our knowledge, this is the first such demonstration for a charged ionic system and in particular an inorganic molten salt, where electrostatic and polarization effects make the accurate treatment of electronic correlation particularly challenging. These results also identify areas of future research towards an accurate and scalable quantum--classical workflow to compute free-energy estimates of tritium speciation in fusion blankets. △ Less

Submitted 29 June, 2026; originally announced June 2026.

NGSE-Corr: A technique for objective clinical evaluation of quantitative-imaging methods without a gold standard

Authors: Yan Liu, Ziping Liu, Zekun Li, Jingqin Luo, Daniel L. J. Thorek, Barry A. Siegel, Abhinav K. Jha

Abstract: Objective evaluation of quantitative-imaging (QI) methods based on how reliably they measure true values is important for clinical translation. Performing such evaluation with patient data is highly desirable but hindered by the lack of gold standards. To address this challenge, advancing on previous studies, we propose a no-gold-standard evaluation technique, NGSE-Corr, that objectively evaluates… ▽ More Objective evaluation of quantitative-imaging (QI) methods based on how reliably they measure true values is important for clinical translation. Performing such evaluation with patient data is highly desirable but hindered by the lack of gold standards. To address this challenge, advancing on previous studies, we propose a no-gold-standard evaluation technique, NGSE-Corr, that objectively evaluates QI methods without true values. The technique assumes a linear stochastic relationship between true and measured values, characterized by a slope, bias, and multivariate Gaussian-distributed noise term that models correlated noise across QI methods. We derive a maximum-likelihood approach to estimate these parameters using only measured values. From the estimates, we compute noise-to-slope ratio (NSR) to rank QI methods based on precision. Numerical experiments showed that NGSE-Corr reliably estimated the NSR, accurately ranked methods, and maintained performance even when assumptions made by the technique were partially violated. We also validated NGSE-Corr in an in silico imaging trial to rank three quantitative SPECT methods for measuring regional activity uptake in patients with bone metastatic castrate-resistant prostate cancer treated with radium-223. NGSE-Corr correctly identified the most precise QI method and ranked the methods for 95% (95% CI, 89%-98%) and 91% (95% CI, 84%-95%) of trials, respectively, with data from 50 patients. Performance further improved with larger cohorts. With 200 patients, NGSE-Corr yielded same rankings as those obtained with true values across all trial instances. These findings demonstrate the ability of NGSE-Corr to accurately rank QI methods without gold standards and motivate clinical validation and broader applications. △ Less

Submitted 26 June, 2026; originally announced June 2026.

Comments: 11 pages, 6 figures

Scaling patch analysis of turbulent kinetic energy budget equation in wall-bounded flows

Authors: Tie Wei, Zhaorui Li, Sergio Pirozzoli

Abstract: The scaling patch approach is applied to analyze the turbulent kinetic energy (TKE) budget equation in wall-bounded turbulent flows. The balance of the TKE equation is divided into several distinct regions, or scaling patches, each characterized by a dominant balance among the governing terms and its own appropriate scaling parameters. In the near-wall viscous sublayer, the TKE balance is primaril… ▽ More The scaling patch approach is applied to analyze the turbulent kinetic energy (TKE) budget equation in wall-bounded turbulent flows. The balance of the TKE equation is divided into several distinct regions, or scaling patches, each characterized by a dominant balance among the governing terms and its own appropriate scaling parameters. In the near-wall viscous sublayer, the TKE balance is primarily between viscous diffusion and dissipation, and the characteristic scales are set by the kinematic viscosity and the wall dissipation rate. The thickness of this sublayer is on the order of the Kolmogorov length scale. Moving away from the wall, the peak TKE production provides a natural reference scale for the inner layer, yielding the traditional inner scaling. Grouping the viscous diffusion and dissipation terms in the inner layer enhances the collapse across different Reynolds numbers. In the outer region, Prandtl's mixing-length model is used to derive a characteristic scale for TKE production. A new meso-scaling is further introduced to describe the intermediate region, ensuring a smooth transition between the inner and outer layers. The scaling patch framework offers a unified interpretation of the structure and scaling behavior of the TKE budget across all regions of wall-bounded turbulence. △ Less

Submitted 22 June, 2026; originally announced June 2026.

Comments: 23 pages, 11 figures

Impurity-Preserved Density Matrix Embedding Theory for Local Electronic Excitations

Authors: Teng Zhang, Ze-Wei Li, Zhe-Bin Guan, Hong Jiang

Abstract: Density matrix embedding theory (DMET), which is usually based on a Schmidt decomposition of Slater determinants by partitioning the full system into impurity and environment in terms of local orthogonal orbitals (LOs), has demonstrated considerable promise in electronic structure studies because it enables the extraction of local properties using a high-level solver within an embedded impurity su… ▽ More Density matrix embedding theory (DMET), which is usually based on a Schmidt decomposition of Slater determinants by partitioning the full system into impurity and environment in terms of local orthogonal orbitals (LOs), has demonstrated considerable promise in electronic structure studies because it enables the extraction of local properties using a high-level solver within an embedded impurity subsystem with greatly reduced degrees of freedom, thereby achieving a balance between accuracy and computational cost. However, its application to excited states of strongly correlated systems, such as lanthanide complexes, remains challenging because the errors relative to all-electron results can still be significant. Motivated by the success of the previously developed atomic orbitals (AOs) based DMET framework (Ai, Li, and Jiang, Phys. Rev. Lett. 2025, 135, 026502.), termed AO-DMET, which attains improved accuracy by constructing the embedded subspace based on a non-orthogonal decomposition of the Slater determinant in terms of AOs, we propose a new LO-based partitioning scheme that fully preserves the impurity space spanned by corresponding AOs and can achieve accuracy closely matching that of AO-DMET while retaining the orthogonal partition and its associated computational efficiency. The performance of the proposed method is demonstrated through excitation energy calculations for several representative lanthanide complexes. These results establish an efficient and accurate partitioning scheme for describing excited states in strongly correlated systems within the DMET framework. △ Less

Submitted 22 June, 2026; originally announced June 2026.

Comments: 32 pages, 1 figure, submitted to Journal of Chemical Theory and Computation

Empowering Polymeric Materials Discovery by Artificial Intelligence

Authors: Chenyao Ma, Linda Zhang, Yuheng Chen, Wei Du, Shangwen Fang, Zihao Jiang, Chuanyu Liu, Xinyu Ma, Rui Su, Gang Wang, Muyao Yu, Dong Zhong, Jie Zhu, Weibo Gong, Huan Gu, Limin Li, Chen Shen, Rui Wu, Zhenghao Wu, Kan Xu, Min Zhou, Donglin He, Xiayun Huang, Shan Jiang, Pengfei Ou , et al. (7 additional authors not shown)

Abstract: Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing. Yet their rational design remains exceptionally challenging because material performance emerges from complex interactions among molecular composition, chain architecture, processing history and hierarchical structural evolution across multiple length and time scale… ▽ More Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing. Yet their rational design remains exceptionally challenging because material performance emerges from complex interactions among molecular composition, chain architecture, processing history and hierarchical structural evolution across multiple length and time scales. Consequently, polymer research has long relied on labor-intensive experimentation and fragmented modeling approaches, limiting both mechanistic understanding and innovation efficiency. Recent advances in data infrastructure, machine learning, large artificial intelligence (AI) models and laboratory automation are beginning to reshape this landscape. Rather than functioning as isolated tools, polymer databases, predictive models, AI agents and automated laboratories are increasingly converging into interconnected discovery ecosystems. As a result, the central challenge is shifting from improving predictive accuracy alone to enabling reliable decision-making, adaptive learning and seamless integration across computation, experimentation and scientific reasoning. We argue that polymer science is entering an era of autonomous discovery, in which data, simulation, reasoning and experimentation operate within self-improving feedback loops that continuously generate hypotheses, design materials, execute experiments and refine predictive models. By unifying molecular design, process optimization, experimental validation and industrial translation, such autonomous ecosystems establish a more predictive, reproducible and scalable paradigm for polymer innovation, fundamentally transforming how polymer research is conducted. △ Less

Submitted 16 August, 2026; v1 submitted 18 June, 2026; originally announced June 2026.

The Moving Target of Urban Equity: Spatiotemporal Demand and Double Disadvantage in Hefei, China

Authors: Shirui Zhou, Matteo Bruno, Mattia Mazzoli, Junfang Tian, Rui Jiang, Enwan Zhang, Zheng Li, Vittorio Loreto

Abstract: Equitable access to essential urban services is a pillar of modern planning, yet most accessibility models rely strictly on static residential locations, ignoring how demand shifts throughout the daily loop. This study introduces a population-based, temporally differentiated framework to examine the resulting "moving target" of urban equity, focusing on medical facilities and green spaces in Hefei… ▽ More Equitable access to essential urban services is a pillar of modern planning, yet most accessibility models rely strictly on static residential locations, ignoring how demand shifts throughout the daily loop. This study introduces a population-based, temporally differentiated framework to examine the resulting "moving target" of urban equity, focusing on medical facilities and green spaces in Hefei, China. Utilising large-scale mobile phone GPS data, we construct dynamic residential and workplace population exposure surfaces to capture shifting hourly demand. We then evaluate accessibility via network-based travel times paired with a novel per-capita provision metric that accounts for real-time demand competition. We define \textit{double disadvantage} as the co-occurrence of poor spatial accessibility and insufficient per-capita service availability. Counterintuitively, the results reveal that double-disadvantaged areas cluster primarily along the inner suburban belt rather than the remote periphery, where per-capita service provision remains relatively sufficient. Furthermore, temporal shifts drastically alter equity landscapes: daytime workplace concentrations intensely exacerbate demand competition in urban job centres. These findings demonstrate that urban inequality depends heavily on spatiotemporal population flows rather than just the fixed location of services. Ultimately, achieving true urban equity requires dynamic planning interventions that address time-varying demand rather than focusing solely on static, home-based metrics. △ Less

Submitted 18 June, 2026; originally announced June 2026.

Radiology-Report Semantic Modelling and Host-Response Laboratory Biomarkers for Multimodal Survival Prediction in Lung Cancer

Authors: Jingxiang Shi, Yiming Wang, Zhengda Li, Yan Zhang, Weihua Meng, Yuqi Ma, Xiaoyan Li, Feng-Ming, Kong, Gen Yang

Abstract: TNM staging is essential for lung cancer management, but patients within the same anatomic stage often show heterogeneous survival outcomes. We developed a multimodal adaptive risk score (AMRS) that integrates radiology-report semantics with routinely available clinical laboratory biomarkers. In a retrospective two-center cohort, 1129 patients diagnosed between December 2017 and February 2026 were… ▽ More TNM staging is essential for lung cancer management, but patients within the same anatomic stage often show heterogeneous survival outcomes. We developed a multimodal adaptive risk score (AMRS) that integrates radiology-report semantics with routinely available clinical laboratory biomarkers. In a retrospective two-center cohort, 1129 patients diagnosed between December 2017 and February 2026 were screened; 574 patients were included after exclusion for short follow-up or missing imaging reports and were split into training (n = 459) and test (n = 115) cohorts. Radiology reports were encoded with a domain-adapted MC-BERT branch to capture imaging-derived semantic information, while clinical and laboratory variables were modeled after Mahalanobis-distance-based imputation using random survival forests. Weighted risk fusion generated the final patient-level score. AMRS achieved C-index values of 0.920 in training and 0.849 in testing, and separated survival trajectories across clinical subgroups and TNM-related strata. SHAP analysis identified hematologic, inflammatory, coagulation, nutritional, tumor-marker, organ-function, and age-related contributors. AMRS may complement TNM staging in imaging-centered oncology workflows, but prospective validation, calibration, ablation testing, and clinical-utility assessment are required before deployment. △ Less

Submitted 11 June, 2026; originally announced June 2026.

Comments: 14 pages,7 figures

Effects of microstructural heterogeneity on the macroscopic spectrum of elastically accommodated grain-boundary sliding

Authors: Zhengxuan Li, John F. Rudge

Abstract: Elastically accommodated grain-boundary sliding (EAGBS) is a plausible source of upper-mantle seismic attenuation and dispersion, yet classical theory predicts a localized Debye-like peak that is absent or only weakly expressed in dry olivine experiments. Here we test whether microstructural heterogeneity can explain this discrepancy using 2-D finite-element simulations on periodic Voronoi tessell… ▽ More Elastically accommodated grain-boundary sliding (EAGBS) is a plausible source of upper-mantle seismic attenuation and dispersion, yet classical theory predicts a localized Debye-like peak that is absent or only weakly expressed in dry olivine experiments. Here we test whether microstructural heterogeneity can explain this discrepancy using 2-D finite-element simulations on periodic Voronoi tessellations. We find that irregular grain geometry changes the baseline EAGBS response relative to the regular hexagonal benchmark, but increasing grain-size variance alone produces only modest changes in modulus and peak height, with little spectral broadening. In contrast, a broad distribution of grain-boundary viscosities progressively suppresses and broadens the Debye-like loss peak into a weak background spanning a wide frequency interval. This broadening arises from the superposition of many localized relaxation processes with distinct characteristic timescales and motivates a reduced-order 0-D description of the aggregate response. These results suggest that the absence of a pronounced EAGBS peak in dry olivine does not necessarily imply the absence of EAGBS mechanism itself. If grain boundaries sample a sufficiently broad viscosity distribution, the macroscopic EAGBS contribution may appear experimentally only as part of a broad attenuation background, while still remaining relevant for upper-mantle seismic attenuation and velocity dispersion. △ Less

Submitted 10 June, 2026; originally announced June 2026.

Comments: Submitted to Journal of Geophysical Research: Solid Earth

Towards stable and accurate electron dynamics via neural network based time-dependent variational Monte Carlo

Authors: Weizhong Fu, Zhe Li, Yubing Qian, Ruichen Li, Weiluo Ren, Ji Chen

Abstract: Real-time dynamics of interacting electrons lies at the interface between quantum mechanics and non-equilibrium physics, governing the microscopic origin of ultrafast phenomena of molecules and nano-materials. Though neural network variational Monte Carlo has achieved unprecedented accuracy for stationary state calculations, its extension to real-time evolution remains challenging. In this work, w… ▽ More Real-time dynamics of interacting electrons lies at the interface between quantum mechanics and non-equilibrium physics, governing the microscopic origin of ultrafast phenomena of molecules and nano-materials. Though neural network variational Monte Carlo has achieved unprecedented accuracy for stationary state calculations, its extension to real-time evolution remains challenging. In this work, we introduce the neural basis time-dependent variational Monte Carlo framework, which achieves stable and highly accurate simulations of electron dynamics. By constraining the time evolution to a compact, customized manifold spanned by the neural basis, we effectively bypass instability issues and achieve long-term stable evolution. Moreover, we demonstrate that this framework yields benchmark-quality accuracy in simulating the laser-driven dipole responses of the hydrogen atom and a stretched hydrogen molecule, and accurately extracts the dynamic polarizabilities of helium and beryllium atoms. Our work reveals the vast potential of neural network wavefunctions for accurately describing real-time electron dynamics and establishes a promising new route for first-principles simulations of complex, time-dependent electronic phenomena. △ Less

Submitted 11 June, 2026; v1 submitted 4 June, 2026; originally announced June 2026.

Reinforcement Learning-Enabled Agent for Transmitter Optimization in Digital-Analog Radio-over-Fiber Fronthaul

Authors: Junhao Zhao, Huayuan Qin, Ouhan Huang, Zhongya Li, Chengxi Wang, Boyu Dong, Liangtao Chen, Xuyu Deng, An Yan, Penghao Luo, Renle Zheng, Yongzhu Hu, Aolong Sun, Yinjun Liu, Sizhe Xing, Nan Chi, Junwen Zhang

Abstract: Digital-analog radio-over-fiber (DA-RoF) has emerged as a promising fronthaul solution that combines the high spectral efficiency of analog transmission with the robustness of digital transmission. However, the performance of DA-RoF critically depends on several tightly coupled parameters, including the rounding factor (RF), scaling factor (SF), geometric shaping (GS) factor, and pre-equalization… ▽ More Digital-analog radio-over-fiber (DA-RoF) has emerged as a promising fronthaul solution that combines the high spectral efficiency of analog transmission with the robustness of digital transmission. However, the performance of DA-RoF critically depends on several tightly coupled parameters, including the rounding factor (RF), scaling factor (SF), geometric shaping (GS) factor, and pre-equalization taps coefficients, which jointly affect quantization noise, nonlinear distortion, and bandwidth-induced inter-symbol interference (ISI). Conventional grid search-based optimization is computationally prohibitive and impractical for optical communication. In this work, we propose a reinforcement-learning (RL)-enabled DA-RoF fronthaul agent architecture, capable of autonomously learning optimal transmitter parameters from end-to-end signal-to-noise ratio (SNR) feedback without a differentiable channel model. Experimental results demonstrate that the trained agent steadily improves SNR through sequential decision making and outperforms baseline, achieving ~2.7-dB SNR improvement for 1- to 4-order DA-RoF transmission, reaching final SNR of 35.8 dB, 42.9 dB, 53.8 dB, and 63.2 dB and supporting 1024-, 4096-, 16384-, 65536-quadrature amplitude modulation (QAM) format, respectively. These results validate that the proposed RL-enabled framework provides online, scalable, and hardware-efficient parameter optimization for DA-RoF fronthaul systems, paving the way toward high-order modulation format and intelligent next-generation radio access networks. △ Less

Submitted 3 June, 2026; originally announced June 2026.

RIFTES: An RTM- and iteration-free temperature-emissivity separation framework for accurate and efficient clear-sky land surface temperature retrieval

Authors: Huanyu Zhang, Bo-Hui Tang, Yun Jiang, Menglin Si, Frank M. Göttsche, Tian Hu, Yuanliang Cheng, Zhao-Liang Li

Abstract: This study proposes an RTM- and iteration-free TES (RIFTES) framework to improve both computational efficiency and retrieval accuracy of the temperature-emissivity separation (TES) algorithm for clear-sky land surface temperature (LST) retrieval. Based on physical derivations, a non-iterative TES algorithm was first developed by reformulating the original iterative procedure into a mathematically… ▽ More This study proposes an RTM- and iteration-free TES (RIFTES) framework to improve both computational efficiency and retrieval accuracy of the temperature-emissivity separation (TES) algorithm for clear-sky land surface temperature (LST) retrieval. Based on physical derivations, a non-iterative TES algorithm was first developed by reformulating the original iterative procedure into a mathematically equivalent closed-form solution, thereby eliminating the need for cumbersome iterations. To further reduce error propagation risks and computational burdens, a deep residual neural network that integrates atmospheric radiative transfer physics was adopted to conduct atmospheric correction using easily accessible parameters, with a masking mechanism introduced to flexibly incorporate atmospheric constraints when available. Comprehensive validations demonstrate the effectiveness of the proposed algorithm. Simulation results show that RIFTES remains robust to input uncertainties and achieves the lowest root mean squared error (RMSE) of 1.06 K among representative existing algorithms, including split-window (SW), TES, and SW-TES hybrid methods. In-situ measurements from globally distributed sites were then used to evaluate the practical performance of RIFTES when applied to both the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the Advanced Baseline Imager (ABI). The new algorithm achieves RMSE values of 1.51 K and 1.97 K for ECOSTRESS and ABI, respectively, reducing retrieval uncertainties by up to 24% and 32% compared with existing methods. Furthermore, by simplifying both the iterative procedure and atmospheric correction, RIFTES reduces the overall computational time by 74.0% and 62.5% compared with the TES and hybrid algorithms, respectively. △ Less

Submitted 1 June, 2026; originally announced June 2026.

A Method for Neutron-Gamma Pulse Shape Discrimination of CLYC Detector Based on a Gated Residual-Linear Attention Network

Authors: Shiwei Jing, Shengduo Liu, Weiyang Zhang, Jia Song, Sijia Zhou, Hailong Xu, Yue Sun, Zebin Li, Yuxuan Gu, Siqi Liu, Tian Zhang, Zhihua Gao, Guofeng Qu, Fuquan Jia

Abstract: The discrimination of neutron and gamma pulse shapes is a key technology in fields such as nuclear safety monitoring and radiation assessment. An enhanced recursive gated cyclic residual-sparse linear attention network is developed on the CLYC detector experimental platform to overcome weak noise resistance, limited feature extraction and inferior real-time performance of conventional algorithms.… ▽ More The discrimination of neutron and gamma pulse shapes is a key technology in fields such as nuclear safety monitoring and radiation assessment. An enhanced recursive gated cyclic residual-sparse linear attention network is developed on the CLYC detector experimental platform to overcome weak noise resistance, limited feature extraction and inferior real-time performance of conventional algorithms. The experimental dataset comprises 19,971 samples, which were pre-processed and stratified for model training and testing. Results indicate that the proposed algorithm achieves a quality factor of 2.2, with a classification accuracy of 98.7% and a recall rate of 99.4%. It achieves an accuracy of 95.1% under the 20 dB low signal-to-noise ratio condition, exhibiting excellent anti-noise ability.With around 2.8 million parameters, the model takes merely 0.05 ms to process a single pulse on GPU, satisfying real-time monitoring and embedded deployment demands. △ Less

Submitted 25 May, 2026; originally announced June 2026.

Manifold partitioning induced sequential optical reasoning and decision framework for photonic computing

Authors: Zhihao Li, Jing Pan, Wei Yan, Yu Xie, Lingmei Ma, Xiaoyu Sun, Min Qiu

Abstract: Real-world data are intrinsically embedded in highly entangled manifolds, making the extraction of separable representations a central challenge for artificial intelligent (AI) systems. While optical neural networks (ONNs) offer ultrafast and energy-efficient data processing, their capacity is constrained by limited physical depth. Here, we introduce a sequential optical reasoning and decision (SO… ▽ More Real-world data are intrinsically embedded in highly entangled manifolds, making the extraction of separable representations a central challenge for artificial intelligent (AI) systems. While optical neural networks (ONNs) offer ultrafast and energy-efficient data processing, their capacity is constrained by limited physical depth. Here, we introduce a sequential optical reasoning and decision (SORD) framework, an architecture that performs time-sequenced hierarchical inference by decomposing global tasks into coarse-to-fine steps via geometry-guided data partitioning. At each step, SORD executes small reasoning via dynamic operator selection, effectively reducing the overall task complexity without scaling up physical architecture. Experimentally, SORD enables a single-layer diffractive ONN to achieve otherwise intractable 100-class optical fiber speckle classification with 94% accuracy and a system energy efficiency of 23.3 TOPS/W. This high-fidelity recognition is further examined in a human-machine interface, featuring real-time interactive all-optical sensing. Overall, our work establishes a scalable and hardware-efficient approach to expanding the effective expressivity of compact photonic AI systems, and may advance their deployment in applications requiring real-time sensing, inference, and control. △ Less

Submitted 31 May, 2026; originally announced June 2026.

Comments: 25 pages, 5 figures, submitted to a journal

Möbius-like Real-Space Topology Reshapes Spectral Winding Topology in Hatano-Nelson Rings

Authors: Yekai Shen, Shuhang Chen, Zishun Liao, Zhipeng Li

Abstract: The spectral winding number serves as a bulk topological invariant in non-Hermitian systems, governing the emergence of skin modes and encoding the non-Hermitian bulk-boundary correspondence. However, most existing studies are built on conventional lattice geometries such as linear chains, rings, or planar arrays, leaving the role of real-space topological connectivity as an independent degree of… ▽ More The spectral winding number serves as a bulk topological invariant in non-Hermitian systems, governing the emergence of skin modes and encoding the non-Hermitian bulk-boundary correspondence. However, most existing studies are built on conventional lattice geometries such as linear chains, rings, or planar arrays, leaving the role of real-space topological connectivity as an independent degree of freedom largely unexplored. Here, we construct a Möbius ring system by cutting two parallel Hatano-Nelson (HN) rings and reconnecting them with a half-twist, without altering any local hopping parameter. This topological reconstruction transforms the periodic-boundary spectrum from two disjoint ellipses into a multi-petalled rose curve, and leads to distinct decay lengths for different eigenstates under open boundary conditions. Moreover, the spectral winding number can be driven through discrete winding-number jumps by tuning the coupling strength, with critical values obtained analytically. Our results demonstrate that real-space Möbius connectivity, mediated by the coupling strength, provides an independent and tunable foundation for the systematic control of non-Hermitian topology, with implications for the design of topological devices and sensing schemes. △ Less

Submitted 30 May, 2026; originally announced June 2026.

Comments: 3 pages,3 figures. Submitted to Light Conference 2026

Optimized design of a Penning ion source for sealed neutron tube

Authors: Shiwei Jing, Jia Song, Shengduo Liu, Weiyang Zhang, Sijia Zhou, Hailong Xu, Zebin Li, Tin Zhang, Zhihu Gao, Guofeng Qu

Abstract: Sealed neutron tubes have a wide range of applications, and the ion source is their core component. Penning ion sources commonly suffer from issues such as uneven magnetic field distribution and a low proportion of monoatomic ions. Improving the performance of the ion source can effectively address the problems of low neutron flux and short operational lifespan. This study aims to optimise the mag… ▽ More Sealed neutron tubes have a wide range of applications, and the ion source is their core component. Penning ion sources commonly suffer from issues such as uneven magnetic field distribution and a low proportion of monoatomic ions. Improving the performance of the ion source can effectively address the problems of low neutron flux and short operational lifespan. This study aims to optimise the magnetic field configuration and discharge parameters of the ion source, thereby increasing the proportion of monoatomic and enhancing discharge stability, and to provide a design basis for high-performance sealed neutron tubes. Develop a magnetic field-plasma coupling model to compare and analyze the magnetic field distribution patterns of traditional magnetic block structures and soft iron-reinforced structures, and investigate the mechanisms by which operating pressure and anode voltage affect plasma density and ion composition using COMSOL multiphysics simulation methods. Simulation results indicate that the soft iron structure significantly enhances the axial magnetic field strength and uniformity within the discharge region; under conditions of 0.06 Pa gas pressure and 1500 V anode voltage, the proportion of monoatomic ions increased from the conventional 9% to 30%. △ Less

Submitted 25 May, 2026; originally announced May 2026.

Hermite-NGP: Gradient-Augmented Hash Encoding for Learning PDEs

Authors: Jinjin He, Zhiqi Li, Sinan Wang, Bo Zhu

Abstract: We propose Hermite-NGP, a gradient-augmented multi-resolution hash encoding designed to enable fast and accurate computation of spatial derivatives for neural PDE solvers. Unlike existing NGP-based approaches that rely on automatic differentiation or finite differences and suffer from instability or high cost, Hermite-NGP explicitly stores function values and mixed partial derivatives at hash grid… ▽ More We propose Hermite-NGP, a gradient-augmented multi-resolution hash encoding designed to enable fast and accurate computation of spatial derivatives for neural PDE solvers. Unlike existing NGP-based approaches that rely on automatic differentiation or finite differences and suffer from instability or high cost, Hermite-NGP explicitly stores function values and mixed partial derivatives at hash grid vertices, allowing fully analytic evaluation of gradients, Jacobians, and Hessians via Hermite interpolation. This design preserves the efficiency and spatial adaptivity of NGP while supporting analytic differential operators up to second order. We further introduce a multi-resolution curriculum training strategy analogous to multigrid V-cycles to enable coarse-to-fine optimization. Across a range of 2D and 3D PDE benchmarks, Hermite-NGP achieves up to approximately 20 times lower error than prior neural PDE methods, and reduces wall-clock convergence time by 2 to 10 times compared to other solvers, with per-epoch training times as low as 3.5 ms for models with up to 17M parameters. △ Less

Submitted 23 May, 2026; originally announced May 2026.

Comments: Accepted by ICML 2026.Project page: https://jinjinhe2001.github.io/hermite-ngp/

Transformer refined quantum sampling for strongly correlated electronic structure

Authors: Xiongzhi Zeng, Ming Gong, Bowen Kan, Yi Fan, Huan Ma, Jianbin Cai, Yancheng Liu, Naibin Zhou, Tao Jiang, Shaojun Guo, Zhijie Fan, Zongkang Zhang, Yuan Li, Sirui Cao, Kai Yan, Xiaobo Zhu, Yi Luo, Honghui Shang, Zhenyu Li, Jian-Wei Pan, Jinlong Yang

Abstract: Although quantum computing offers a promising solution for strongly correlated system simulation, existing algorithms face significant bottlenecks on current noisy intermediate-scale quantum (NISQ) devices. Here, we introduce QiankunNet-QSCI, a hybrid quantum-classical framework that addresses this challenge by combining efficient quantum-sampling with a transformer neural network. An efficient un… ▽ More Although quantum computing offers a promising solution for strongly correlated system simulation, existing algorithms face significant bottlenecks on current noisy intermediate-scale quantum (NISQ) devices. Here, we introduce QiankunNet-QSCI, a hybrid quantum-classical framework that addresses this challenge by combining efficient quantum-sampling with a transformer neural network. An efficient unitary selected configuration Interaction (USCI) ansatz especially designed for quantum sampling is proposed to identify the most chemically significant electronic configurations on the Zuchongzhi 3.1 quantum processor. Subsequently, the transformer model QiankunNet learns from these sparse yet critical quantum data to infer and reconstruct the complete electronic wavefunction with high fidelity. Simulation of the challenging 40-qubit [2Fe-2S] ferredoxin active center achieves chemical accuracy. Simulation of the nitrogenase P-cluster in a 114-electron 73-orbital active space also reaches 12 milli-Hartree-level agreement with the best density matrix renormalization group (DMRG) result. QiankunNet-QSCI thus offers a practical route to accurate quantum-assisted electronic structure calculations on current devices. △ Less

Submitted 23 May, 2026; originally announced May 2026.

All-band photonic integrated optical parametric amplification

Authors: Nikolai Kuznetsov, Zihan Li, Tobias J. Kippenberg

Abstract: Optical amplifiers are ubiquitous in science and technology and are the workhorse of modern communications. Currently, virtually all amplifiers rely on atomic resonances, such as rare-earth-doped fibers, or are based on III-V semiconductors. Fueled by emerging applications, there is increased demand for amplifiers that are high-gain, broadband, low-noise, and deliver high output power outside trad… ▽ More Optical amplifiers are ubiquitous in science and technology and are the workhorse of modern communications. Currently, virtually all amplifiers rely on atomic resonances, such as rare-earth-doped fibers, or are based on III-V semiconductors. Fueled by emerging applications, there is increased demand for amplifiers that are high-gain, broadband, low-noise, and deliver high output power outside traditional wavelength ranges. Over the past few decades, it has been shown that optical parametric amplifiers (OPAs) can address this challenge. Pioneering works on highly nonlinear optical fibers or bulk crystals have demonstrated their potential, but high pump powers and long fiber length limited their practical use. Recently, a renaissance of OPAs has occurred with the demonstration of photonic integrated circuits, which exhibit higher effective nonlinearity and enable wider bandwidths. Yet they require ultra-low loss, highly precise dispersion engineering, and large chip footprints, limiting OPA performance to date. Here, we overcome these limitations and, using periodically poled thin-film lithium tantalate (PPLT) photonic integrated circuits, we demonstrate continuous-wave optical parametric gain up to 23.5 dB, with a flat-top profile spanning across an 850 nm-wide optical wavelength window, corresponding to 100 THz and covering all communication bands. Moreover, on-chip output signal power as large as 313 mW in the optical O-band is achieved. We further realize all-optical inter-band modulation transfer between the C- and O-bands. Our approach uses cascaded second-order nonlinear processes that provide high effective third-order nonlinearities while preserving the wide material bandgap. These results establish PPLT integrated photonic circuits as a scalable platform for broadband optical amplification and frequency conversion across wavelengths where rare-earth doped amplifiers are absent. △ Less

Submitted 21 May, 2026; originally announced May 2026.

Comments: Main text: 9 pages, 4 figures. Supplementary Material: 11 pages, 9 figures

Holographic EUV Lithography at 40 nm Resolution

Authors: Ziqi Li, Iason Giannopoulos, Lisong Dong, Dimitrios Kazazis, Xu Ma, Zongqiang Yu, Zhiyuan Niu, Yasin Ekinci, Yayi Wei, Iacopo Mochi

Abstract: Extreme ultraviolet (EUV) lithography is the cornerstone of the fabrication of advanced integrated circuits at the 7-nm node and beyond, but its reliance on multi-element reflective projection optics makes it inaccessible for small-scale research and prototyping. EUV interference lithography (EUV-IL) provides a lensless alternative but is intrinsically restricted to periodic structures. Here we de… ▽ More Extreme ultraviolet (EUV) lithography is the cornerstone of the fabrication of advanced integrated circuits at the 7-nm node and beyond, but its reliance on multi-element reflective projection optics makes it inaccessible for small-scale research and prototyping. EUV interference lithography (EUV-IL) provides a lensless alternative but is intrinsically restricted to periodic structures. Here we demonstrate EUV holographic lithography (EUV-HL) as a lensless route to arbitrary, non-periodic, curvilinear patterning at the EUV wavelength of 13.5 nm. We introduce an inverse-design framework for computer-generated holograms that captures the dominant physical effects of EUV mask diffraction within a shift-invariant convolution model that is tractable for full mask layouts. Using this framework, we design and fabricate transmissive holographic masks by direct-write electron-beam lithography in hydrogen silsesquioxane, expose them with synchrotron-generated EUV radiation, and print target layouts with critical dimensions down to 40 nm, nearly an order of magnitude finer than the previous state of the art in EUV-HL. The demonstrated combination of sub-50 nm resolution, curvilinear design freedom, and a lensless optical setup establishes EUV-HL as a uniquely flexible tool for nanostructure prototyping at EUV wavelengths, and provides a natural pathway to non-periodic pattern prototyping at beyond-EUV (BEUV) wavelengths, which is currently inaccessible to interference-based methods. △ Less

Submitted 20 May, 2026; originally announced May 2026.

Comments: 20 pages, 5 figures

HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL

Authors: Zhi Li, Songkun Yan, Jie Cao, Mofan Zhang, Anjiang Wei, Jinwoong Yoo, Yang Hong

Abstract: Calibrating distributed hydrologic models is a critical bottleneck across operational water resources management - streamflow prediction, reservoir operation, drought monitoring, infrastructure design, and flood forecasting all depend on it. Each basin demands an expert to translate hydrograph signatures into adjustments of a high-dimensional parameter vector, and the resulting workflow does not t… ▽ More Calibrating distributed hydrologic models is a critical bottleneck across operational water resources management - streamflow prediction, reservoir operation, drought monitoring, infrastructure design, and flood forecasting all depend on it. Each basin demands an expert to translate hydrograph signatures into adjustments of a high-dimensional parameter vector, and the resulting workflow does not transfer between watersheds. We ask: can frontier large language model (LLM) agents replace the human hydrologic modeler, and if not, what would it take? We benchmark nine frontier LLM agents - Claude Opus 4.6/4.7, Sonnet 4.6, GPT-5/5.4/5.4-pro, and Gemini 2.5-pro/3.1-pro/3-flash - on the operational CREST distributed hydrologic model used by the U.S. National Weather Service for flash-flood forecasting. Best-of-twenty-rounds Nash-Sutcliffe Efficiency (NSE) across four held-out gauges spanning 329-40,792 km2 ranges from -0.16 (GPT-5.4) to 0.75 (Sonnet 4.6); the ceiling reproduces across all three vendors and capability tiers, with the strongest models concentrating in the 0.65-0.75 band, and no model reaches the human-expert reference except Opus-4.7 on one gauge. We argue this gap is not a parameter-count problem but a domain-grounding problem. We then propose HYDROAGENT, fine-tuning open-weight Qwen3-4B with supervised fine-tuning on 2,576 expert calibration trajectories and Group-Relative Policy Optimization using NSE as a verifiable reward from online CREST simulations - reinforcement learning with simulation feedback (RLSF). For Earth system science, a small domain-tuned policy with simulator-in-the-loop RL is a more compute-efficient and physically faithful path than scaling generic frontier models, and the multi-modal richness of Earth data - remote sensing, in-situ time series, and forecaster narrative - makes domain agents a leveraged direction for AI in physical science. △ Less

Submitted 17 May, 2026; originally announced May 2026.

Overcoming noise-agility trade-off in integrated lasers for precision sensing

Authors: Di Yu, Yitian Tong, Yu Xia, Yuntao Zhu, Yuemin Li, Mingfei Liu, Zhaoting Geng, Yuhao Huang, Yaoran Huang, Zheng Li, Jie Wang, Yunqi Fu, Hongjie Liang, Hao Fang, Jinwen Lin, Xuewen Chen, Kang Li, Xinlun Cai, Chao Xiang

Abstract: Lasers that combine narrow linewidths with rapid tunability are critical for applications such as coherent optical ranging, distributed fiber-optic sensing, and precision spectroscopy. Despite significant progress in integrated laser technologies, the concurrent realization of low phase noise and frequency agility on a single integrated platform remains challenging owing to a fundamental architect… ▽ More Lasers that combine narrow linewidths with rapid tunability are critical for applications such as coherent optical ranging, distributed fiber-optic sensing, and precision spectroscopy. Despite significant progress in integrated laser technologies, the concurrent realization of low phase noise and frequency agility on a single integrated platform remains challenging owing to a fundamental architectural trade-off: conventional integrated laser designs typically suppress phase noise via high-$Q$ resonators, yet the extended photon lifetimes inherent to such resonators intrinsically constrain tuning speed. Here, we address this noise-agility trade-off by introducing a laser architecture that achieves ultralow phase noise and ultrafast tunability simultaneously. Rather than relying on ultrahigh-$Q$ resonators for self-injection locking, our design employs strong synthetic feedback within a Pockels-tunable, resonator-enhanced distributed Bragg reflector to suppress phase noise. As a proof of concept, we demonstrate a hybrid integrated laser with a short-term linewidth of 29 Hz, realized using a lithium niobate external cavity with a loaded $Q$ of only 0.62 million. The adoption of a moderate resonator $Q$ relaxes the photon-lifetime constraint on tuning speed, enabling sub-exahertz-per-second tuning rates and a chirp nonlinearity as low as 0.14%. Leveraging this laser, we implement a frequency-modulated continuous-wave LiDAR system that achieves a relative ranging precision of $1.7 \times 10^{-4}$ at a measurement rate of $1\,\text{MSa s}^{-1}$, without requiring complex chirp linearization techniques. We further demonstrate fiber-optic acoustic sensing capable of detecting sub-$με$ dynamic strain, underscoring the platform's versatility for high-speed precision optical measurements. Our work provides a route toward cost-effective yet high-performance sensing and metrology systems. △ Less

Submitted 17 May, 2026; originally announced May 2026.

Comments: 38 pages, 17 figures