Computational Physics · Cross-listed from cond-mat.str-el
Enhancing Neural-Network Variational Monte Carlo through Basis Transformation
Zhixuan Liu, Dongheng Qian and Jing Wang
Neural-network variational Monte Carlo (NNVMC) has emerged as a powerful tool for solving
quantum many-body problems, yet systematic pathways for improving its accuracy remain largely heuristic. Here, we introduce a physically motivated basis transformation for NNVMC that enhances variational expressivity without increasing the complexity of the neural-network ansatz itself. By formulating the many-body wave function in a Gaussian basis, we introduce a single learnable locality parameter, $\alpha$, that reshapes the target ground state into a more learnable representation. This approach introduces minimal computational overhead and can be readily combined with existing neural-network architectures. Using the three-dimensional homogeneous electron gas as a
benchmark, we show that the optimized basis transformation consistently lowers the variational energy for both FermiNet and message-passing neural-network architectures. Notably, for the latter, it enables a more precise determination of the
Fermi liquid to Wigner
crystal phase transition. More broadly, our results highlight basis transformation as a new route to improving NNVMC in continuous space, showing that accuracy can be enhanced not only by refining the ansatz but also by making the target ground state easier to represent.
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Data Analysis and Statistics · Cross-listed from gr-qc
Gravitational-wave astronomy requires population-informed parameter estimation
Matthew Mould, Rodrigo Tenorio and Davide Gerosa
Gravitational-wave events are interpreted in terms of
Bayesian posteriors for their source properties inferred under unphysical reference priors. Though these parameter estimates are important intermediate data products for downstream analyses, we demonstrate that they are generically biased and therefore should not be used for astrophysical interpretation directly, as is common. Hierarchical parameter estimation is the solution, as joint analysis of the entire catalog of observations reduces statistical uncertainties and actually informs the correct prior, with population-informed event parameters now appropriate for astrophysical interpretation. As an example, we show how the most extreme measurements from a catalog can be derived and used to identify exceptional events from previous and ongoing observing runs, pointing out they are more informative about the population than any individual event. Using LIGO-Virgo-KAGRA data, we thus demonstrate that population inference is not optional to interpret gravitational-wave observations.
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Fluid Dynamics
Large-eddy simulation of the FDA benchmark blood pump: validation against experiments and implications for turbulent flow mechanisms
Xuanming Huang, Chi Ding, Yujie Sun et al. (+4 more)
This study presents a systematic validation and comparative assessment of computational fluid dynamics (CFD) strategies for centrifugal blood pump simulations using the U.S. Food and Drug Administration
benchmark model. A scale-resolving large eddy simulation (LES) with transient sliding-interface (SI) coupling is evaluated and compared against Reynolds-averaged Navier-Stokes (RANS) approaches employing both multiple reference frame and SI formulations. Numerical predictions are validated through direct comparison with particle image velocimetry measurements under two representative operating conditions. The results indicate that LES with transient rotor-stator coupling achieves consistently improved agreement with experimental velocity fields compared with RANS-based methods, particularly in the diffuser region where strong intermittency and wall-bounded
turbulence are present. In contrast, RANS-based approaches exhibit noticeable discrepancies in these regions. A mesh sensitivity study and an assessment of temporal averaging effects are conducted for LES. The quality of the LES results is further quantified using three complementary metrics, demonstrating that a mesh resolution of approximately 80 million
cells achieves a well-resolved LES regime. Building on the validated scale-resolving simulations, detailed analyses of vortical structures, turbulent kinetic energy distributions, and velocity energy spectra are performed to characterize the internal flow physics of the pump. This study demonstrates that scale-resolving, transient simulation approaches are essential for accurately capturing the highly unsteady,
turbulence-dominated flow features in ventricular assist devices and provides practical guidance for future high-fidelity hemodynamic and hemocompatibility studies.
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All authors Xuanming Huang, Chi Ding, Yujie Sun, Shidi Huang, Andrea Cioncolini, Damiano Padovani, Ju Liu
Optics
Programmable photonic nanojets via phase-only time-reversal: a numerical study
Tobias Abilock Mikkelsen, Cristian Placinta, Jesper Glückstad et al. (+1 more)
We present a phase-only time-reversal framework for steering photonic nanojets without mechanical motion or amplitude modulation. Time-reversed radiation by a synthetic source placed at the target PNJ location helps define a phase-only modulation on a control line, compatible with a spatial light modulator, that produces the desired PNJ. Full-wave finite-difference frequency-domain (FDFD) simulations demonstrate robust lateral and axial steering with subwavelength confinement and low sidelobes. A parametric study of microelement geometries shows that nanojet formation is largely insensitive to moderate boundary variations, with simple shapes providing competitive performance. Robustness to fabrication and
alignment errors is confirmed via uncertainty analysis.
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All authors Tobias Abilock Mikkelsen, Cristian Placinta, Jesper Glückstad, Mirza Karamehmedović
Applied Physics
Frequency-Selective Rain Attenuation on Terahertz Channels
Yuheng Song, Wanzhu Chang, Kefeng Huang et al. (+3 more)
Rain introduces broadband and frequency-selective attenuation in wideband terahertz (THz) links, making it necessary to identify a compact spectral descriptor that captures how the dominant loss region evolves with rainfall conditions. This article investigates the peak-frequency behavior of rain attenuation by combining Mie theory calculations with one separable laboratory Gaussian drop-size distribution (DSD) and eight outdoor empirical DSD models whose spectral shapes vary with rainfall rate. The analysis compares total loss, absorption, and scattering components, examines the roles of the characteristic DSD scale and representative drop size statistics, and evaluates the effect of temperature on the peak location. The results show that, unlike the fixed-shape laboratory case where the peak frequency remains unchanged with rainfall rate, all outdoor empirical DSD models exhibit a monotonic migration of the attenuation peak toward lower frequencies as rainfall rate increases. This migration follows a quasi-exact inverse scaling with the rainfall-dependent DSD characteristic scale, follows a family-specific asymptotic power law in rainfall rate, and is governed mainly by characteristic drop size, while fixed-temperature dielectric dispersion contributes only secondary corrections in the broad-peak, low-rainfall regime.
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All authors Yuheng Song, Wanzhu Chang, Kefeng Huang, Kaixin Sun, Weidong Hu, Jianjun Ma
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Direct Orientation Contrast Imaging of Anti-Phase Domains on III-V Materials Using Scanning Electron Microscopy
Brieg Le Corre, Clothilde Grenèche, Rozenn Bernard et al. (+16 more)
Direct orientation contrast imaging of zinc-blende III-V materials is studied using scanning electron microscopy. A quantitative approach is taken using a 3 {\mu}m thick orientation-patterned GaP grown on GaAs sample, studying the anti-phase domain contrast with respect to the electron beam energy and the tilt angle. A qualitative approach is taken for III-V grown on non-polar materials with and without chemical mechanical polishing. Finally, a processing of the acquired image for GaP on Si reveals in plane preferential anti-phase boundaries.
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All authors Brieg Le Corre, Clothilde Grenèche, Rozenn Bernard, Tony Rohel, Antoine Létoublon, Wijden Khelifi, Julie Le Pouliquen, Arnaud Grisard, Sylvain Combrié, Bruno Gérard, Abdelmounaim Harouri, Luc Le Gratiet, Grégoire Beaudoin, Konstantinos Pantzas, Isabelle Sagnes, Oliver Skibitzki, Gilles Patriarche, Charles Cornet, Yoan Léger
Fluid Dynamics
Stabilisation of second Mack mode in hypersonic boundary layers through spanwise non-uniform surface temperature distribution
L. Boscagli, G. Rigas, O. Marxen et al. (+1 more)
The extreme heat fluxes characteristic of hypersonic flows significantly limit the flight envelope of hypersonic vehicles. The role of hydrodynamic instability and the onset of laminar to turbulent boundary layer transition is of notable importance. The effect of streaks on the suppression of planar (second Mack mode) instabilities has been previously investigated, but a potentially passive and non-intrusive control method has not been established yet. Recent work shows that streaks can be generated through a spanwise variation in surface temperature. This method exploits the aerothermodynamic characteristics of the flow, and therefore promises to be robust. This work uses direct numerical simulations to determine and quantify the effectiveness of this novel control method in the suppression of second Mack mode instability for a hypersonic boundary layer over a flat plate. The computational analyses cover a range of Mach numbers 4.8 to 6 and wall temperature ratios representative of both wind tunnel testing and flight scenarios. Among the range of configurations investigated the energy of the second Mack mode is reduced by up to approximately 60% by the steady streaks. The streak wavelength parameter plays a significant role in the stabilisation benefits. For a Mach 6 configuration, for the most linearly amplified second Mack mode disturbance frequency, nearly optimum performance is achieved for a spanwise wavelength of approximately 8 to 10 times the local boundary layer thickness. These findings open new avenues for controlling hypersonic boundary layers and offer valuable guidance for future experimental campaigns aimed at validating this novel control strategy.
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All authors L. Boscagli, G. Rigas, O. Marxen, P. J. K. Bruce
Atomic Physics · Cross-listed from quant-ph
Spectral design principles for local-excitation retention in impurity-assisted atomic arrays
Junpei Oba
Enhanced local-excitation retention in atomic arrays allows to exploit cooperative radiative effects to suppress emission and prolong excited-state lifetimes. We consider an
impurity-assisted setting involving a single storage atom being initially excited and study the survival of local excitation under neither write nor
retrieval fields. Because the corresponding dynamics can involve multiple interfering collective modes, the survival dynamics cannot determined from the smallest collective decay rate alone. Thus, using a biorthogonal eigenmode decomposition of an effective non-Hermitian
Hamiltonian, we show that the survival dynamics are jointly governed by the decay rates of the eigenmodes and their overlaps with the initial excitation. Large oscillations occur when multiple long-lived modes have comparable weights. Accordingly, we introduce a physically motivated spectral surrogate objective that favors both small weighted decay rates and an initial-state weight concentrated on a single subradiant mode. As a proof of principle of this spectral design, we apply the surrogate to constrained atom-position optimization under minimum-distance constraints and obtain nontrivial aperiodic configurations with enhanced local-excitation retention. Our findings unveil spectral design principles for local-excitation retention in
impurity-assisted atomic arrays and provide a proof of principle for their inverse design.
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Plasma Physics · Cross-listed from astro-ph.HE
Induced Scattering of Strong Waves in Pair Plasmas
Masanori Iwamoto and Kunihito Ioka
We study induced (stimulated) scattering of linearly polarized, strong electromagnetic waves in pair
plasmas, which is crucial for understanding the propagation of fast
radio bursts (FRBs). Magnetars are the most promising progenitors of FRBs, and FRBs propagate through the magnetar wind and successfully escape before being significantly scattered. We revisit the steady-state solution of linearly polarized electromagnetic waves in pair
plasmas with arbitrary amplitude, and demonstrate that the nonlinearity is characterized by the nonlinearity parameter $a_0\omega_{pe}/\omega_0$ rather than the dimensionless amplitude $a_0$, where $\omega_{pe}$ is the electron
plasma frequency and $\omega_0$ is the wave frequency. We follow the time
evolution of the steady-state solution for the linear regime $a_0\omega_{pe}/\omega_0 \ll 1$ by performing one-dimensional particle-in-
cell simulations, and show that the conventional linear analysis of induced scattering assuming $a_0 \ll 1$ is applicable even for $a_0 > 1$ when the Lorentz boost due to the
plasma motion in the incident wave is considered. The saturation level is controlled by $a_0\omega_0/\omega_{pe}$, which corresponds to the ratio of the wave energy to the
plasma energy, and the incident wave is hardly scattered for $a_0\omega_0/\omega_{pe} \gg 1$. We discuss the application of our results to FRBs.
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Computational Physics
Probabilistic Upscaling of Hydrodynamics in Geological Fractures Under Uncertainty
Sarah Perez, Florian Doster, Hannah Menke et al. (+2 more)
Flow and transport in fractured geological media are strongly controlled by aperture heterogeneity and uncertainty in subsurface characterisation, yet most upscaling approaches rely on deterministic representations of fracture permeability. This study presents a scalable probabilistic workflow that bridges image-based fracture geometry and uncertainty-aware hydraulic predictions across scales. The approach integrates
Bayesian correction of aperture-permeability model misspecification, a deep learning surrogate for predicting spatially distributed permeability statistics, and Darcy-scale flow upscaling to propagate uncertainty to effective transmissivity. The workflow is applied to natural shear fractures from core material in the Little Grand Wash Fault damage zone (Utah) and to simplified geometries derived from the same
datasets. The
Bayesian component quantifies uncertainty due to measurement errors and imperfect constitutive relations, while a Residual U-Net learns the effects of local heterogeneity and spatial correlation on predicted permeability uncertainty. Together, these components generate ensembles of permeability fields that are subsequently upscaled to probabilistic macroscopic flow responses. Results show that common empirical aperture-permeability relations are systematically biased for natural fractures, whereas the proposed probabilistic workflow yields uncertainty-aware permeability estimates consistent with physics-based behaviour. The method captures the impact of channelisation, connectivity, and complex 3D void geometries on transmissivity while quantifying the resulting uncertainty bounds. Computational efficiency arises from the proposed hybrid strategy for probabilistic upscaling, which combines physics-informed and data-driven approaches, preserves Stokes-flow consistency and supports uncertainty propagation without repeated high-fidelity simulations.
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All authors Sarah Perez, Florian Doster, Hannah Menke, Ahmed ElSheikh, Andreas Busch
Physics and Society
Comparing Analytical Approaches for Bike Station Expansion: A Location-Allocation Study in Trondheim, Norway
M. Tsaqif Wismadi, Oluwaleke Yusuf, Adil Rasheed et al. (+2 more)
The strategic placement of bike-sharing infrastructure shapes urban accessibility and mobility outcomes. However, station-allocation approaches vary in their assumptions and decision logic. This study examines how alternative modelling paradigms prioritise urban space when applied to the same planning problem in Trondheim, Norway. We developed a unified analytical framework to compare three location-allocation approaches: weighted linear combination (WLC), maximal covering location problem (MCLP), and a data-driven suitability score based on exogenous spatial features (SSE). Each model designs a 68-station bike-sharing network from scratch using the same 24 spatial features and hierarchical weighting scheme. The resulting configurations are compared with the existing network, and consensus-based synthesis identifies 12 priority locations for expansion. The findings reveal systematic differences in spatial prioritisation across modelling approaches. WLC achieves the strongest coverage of population and transit demand, MCLP produces the widest spatial distribution prioritising geographic reach, and SSE balances demand intensity with accessibility. All model-derived configurations diverge from the existing network, highlighting the influence of historical and institutional factors on real-world deployment. Consensus synthesis identifies 12 expansion sites characterised by
multimodal integration potential, underserved residential clusters, and high latent demand. This analysis demonstrates that methodological choices fundamentally shape spatial decision-support outcomes. By systematically evaluating classical optimisation and data-driven approaches under controlled conditions, the study provides evidence-based recommendations for bike-sharing network expansion and clarifies the strengths and limitations of alternative analytical frameworks for location-allocation planning.
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All authors M. Tsaqif Wismadi, Oluwaleke Yusuf, Adil Rasheed, Yngve Karl Frøyen, Thomas Alexander Sick Nielsen
Physics and Society · Cross-listed from cs.LG
Fusing Cellular Network Data and Tollbooth Counts for Urban Traffic Flow Estimation
Oluwaleke Yusuf and Shaira Tabassum
Traffic simulations, essential for planning urban transit infrastructure interventions, require vehicle-category-specific origin-destination (OD) data. Existing data sources are imperfect: sparse tollbooth sensors provide accurate vehicle counts by category, while extensive mobility data from cellular network activity captures aggregated crowd movement, but lack modal disaggregation and have systematic biases. This study develops a machine learning framework to correct and disaggregate cellular network data using sparse tollbooth counts as ground truth. The model uses temporal and spatial features to learn the complex relationship between aggregated mobility data and vehicular data. The framework infers destinations from transit routes and implements routing logic to distribute corrected flows between OD pairs. This approach is applied to a bus depot expansion in Trondheim, Norway, generating hourly OD
matrices by vehicle length category. The results show how limited but accurate sensor measurements can correct extensive but aggregated mobility data to produce grounded estimates of background vehicular traffic flows. These macro-scale estimates can be refined for micro-scale analysis at desired locations. The framework provides a generalisable approach for generating origin-destination data from cellular network data. This enables downstream tasks, like detailed traffic simulations for infrastructure planning in data-scarce contexts, supporting urban planners in making informed decisions.
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Optics · Cross-listed from quant-ph
Machine-learning-assisted material and geometry characterization from Casimir force measurement
Hideo Iizuka and Shanhui Fan
A broadband electromagnetic source is important for scientific and technological applications.
Quantum vacuum fluctuations, which manifest most prominently in the Casimir effect, provide a fundamentally broadband electromagnetic source. Here we explore a potential consequence of the broadband nature of
quantum vacuum fluctuations, by showing that such fluctuations can enable measurement of material permittivity over a broad frequency range. Specifically, we consider the Casimir force in a parallel-plate geometry, with one plate covered by a nanoscopic thin film. Using a machine learning approach, we show that one can infer both the thickness of the film and its permittivity over a broad frequency range, starting from the dependency of the Casimir forces on the spacing between the two plates. Our work highlights the application potential of using
vacuum fluctuations as a naturally-existing broadband electromagnetic source for material characterization, and shows that the inverse problem in Casimir force calculation can be solved with machine learning.
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Applied Physics · Cross-listed from quant-ph
Optically detected magnetic resonance of nitrogen-vacancy centers in diamond using two-photon excitation
Lam T. Nguyen and Khanh Kieu
We demonstrate the use of two-
photon excitation for observing the ground state optically detected
magnetic resonance (ODMR) of nitrogen-vacancy centers in diamonds at room temperature. An ultrafast femtosecond
laser at 1040 nm was used for excitation, while fluorescence signal read out was achieved through a combination of a PMT and a lock-in amplifier. The imaging capability of two-
photon excitation fluorescence (2PEF) was utilized to map the distribution of NV centers in a bulk diamond and micro-sized diamonds. For the first time, ODMR traces of the nitrogen-vacancy center are observed with two-
photon excitation, providing a promising tool for fast 3D
quantum sensing and imaging.
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Biological Physics · Cross-listed from q-bio.NC
Role of chloride concentration in modulating seizure transitions in excitatory and inhibitory networks
Qianchen Gong, Yingpeng Liu, Yan Zhang et al. (+2 more)
Experimental evidence indicates that intracellular chloride concentration regulates the excitation and inhibition (EI) balance, yet the mechanisms by which activity-dependent chloride dynamics drive seizure
evolution and stage transitions remain unclear. We present a conductance-based neuronal network in which EI balance emerges from chloride homeostasis via channel-mediated influx and transporter-mediated extrusion. We show that the fraction of inhibitory synaptic conductance contributing to channel-mediated influx acts as a control parameter that organizes seizure dynamics into distinct stages,pre-ictal, ictal-tonic, and ictal-clonic,distinguished by characteristic amplitude and frequency signatures. Decreasing this fraction shortens ictal activity and suppresses seizure initiation, whereas high fraction promotes the emergence of ictal-tonic and ictal-clonic stages and spiral-wave dynamics, rendering seizure dynamics largely insensitive to inhibition. At intermediate values, seizures bypass the ictal-tonic stage and emerge directly as the icta,clonic stage. Moreover, joint variation of fractions with synaptic strengths reveals that recurrent excitation expands the tonic-clonic seizure, while recurrent inhibition prolongs pre-ictal states and suppresses ictal-clonic activity.
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All authors Qianchen Gong, Yingpeng Liu, Yan Zhang, Muhua Zheng, Kesheng Xu
Chemical Physics
Linear and nonlinear vibrational excitation driven by molecular polaritons
Wenxiang Ying, Carlos M. Bustamante, Franco P. Bonafé et al. (+5 more)
Following our recent numerical study [arXiv:2601.16299 (2026)], we investigate vibrational excitation induced by transient optical driving in molecular ensembles strongly coupled to a cavity mode using the field-driven Holstein–Tavis–Cummings model. We analyze how pulsed excitation redistributes energy among electronic, photonic, and vibrational degrees of freedom in molecular polaritons. Vibrational dynamics are examined over a broad range of pulse durations and intensities within both the single-excitation approximation and a mean-field description of collective light–matter coupling. Despite their distinct formulations and microscopic descriptions, these two approaches yield consistent scaling relations for vibrational excitation. In particular, we disentangle linear and nonlinear contributions to vibrational excitation, which are reflected in distinct quadratic and quartic scaling behaviors with respect to the driving field amplitude (that is, linear and quadratic dependence on the incident pulse intensity). The microscopic origin of the nonlinear component is identified as a polariton-mediated intrapulse stimulated Raman-like process, enabled by a pulse spectral bandwidth large enough to overlap both upper and lower polaritons (rather than a conventional multi-pulse scheme). These results establish a unified framework for understanding vibrational excitation under pulsed polariton driving and provide guidance for the interpretation and control of ultrafast polariton experiments. Discrepancies between the mean-field and single-excitation approaches under certain pulsed conditions are identified and analyzed.
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All authors Wenxiang Ying, Carlos M. Bustamante, Franco P. Bonafé, Richard Richardson, Michael Ruggenthaler, Maxim Sukharev, Angel Rubio, Abraham Nitzan
Computational Physics · Cross-listed from cs.LG
Hybrid Quantum-Classical PINNs for Scientific Computing: A Multi-GPU Open-Source Framework
Ziv Chen, Hemanth Chandravamsi, Shimon Pisnoy et al. (+4 more)
We present QPINNACLE, an open-source computational framework for physics-informed neural networks (PINNs) that integrates modern training strategies, multi-GPU acceleration, and hybrid
quantum-classical architectures within a unified modular workflow. The framework enables systematic evaluation of PINN performance across
benchmark problems including 1D
hyperbolic conservation laws, incompressible flows, and electromagnetic wave propagation. It supports a range of architectural and training enhancements, including
Fourier feature
embeddings, random weight factorization, strict boundary condition enforcement, adaptive loss balancing, curriculum training, and second-order optimization strategies, with extensibility to additional methods. We provide a comprehensive
benchmark study quantifying the impact of these methods on convergence, accuracy, and computational cost, and analyze distributed data parallel scaling in terms of runtime and
memory efficiency. In addition, we extend the framework to hybrid
quantum-classical PINNs and derive a formal estimate for
circuit-evaluation complexity under parameter-shift differentiation. Results highlight the sensitivity of PINNs to architectural and training choices, confirm their high computational cost relative to classical solvers, and identify regimes where hybrid
quantum models offer improved parameter efficiency. QPINNACLE provides a foundation for benchmarking physics-informed learning methods and guiding future developments through quantitative assessment of their trade-offs.
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All authors Ziv Chen, Hemanth Chandravamsi, Shimon Pisnoy, Aaron Goldgewert, Gal Shaviner, Boris Shragner, Steven H. Frankel
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Facet-dependent Chemical Kinetics Governed Growth of Twisted Graphene Layers with Pre-designed Angles
Chaowu Xue, Mengzhao Sun, Zixuan Zhou et al. (+8 more)
Twisted
graphene layers (TGLs) provide a powerful platform for investigating multiple
quantum phenomena, yet their scalable deployment is hindered by the lack of reliable synthesis with precise angle. Here, benefited from a deeper understanding of the interplay between grain index and
graphene growth kinetics, we report a scalable strategy to grow TGLs with pre-designed twist angles on platinum (Pt) via chemical vapor deposition (CVD), Through a combination of complementary in situ methods, we identified the activity sequence of different Pt grains and attributed it to the area ratio of exposed (110) facets during
graphene-induced surface reconstruction. Moreover, we revealed that CVD-grown
graphene orientation is determined by the grain-orientation-dependent surface morphology. By leveraging the so-established correlations between grain index with both
graphene growth priority and its orientation, we achieve controlled folding and tearing of
graphene overlayer using a pair of adjacent grains with dramatically different catalytical activity and kink-free atomic steps. We reveal that overlayer-induced step bunching and terrace reconfiguration critically govern the domain morphology and folding direction. Building on this mechanistic insight, we demonstrate a substrate-engineering framework where specific platinum grains are rationally selected to yield TGLs with pre-designed twist angles, including magic angle with flat band dispersion. This work not only highlights fundamental kinetics of Pt catalyzed
graphene CVD growth, but also offers a generalizable methodology for manipulating foldable two-dimensional materials via dynamic substrate reconstruction, exampled by programmable growth of high-quality TGLs on open surfaces.
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All authors Chaowu Xue, Mengzhao Sun, Zixuan Zhou, Zhuoran Yao, Li-Qun Shen, Xiao Kong, Honglong Zhao, Feng Ding, Marc Willinger, Zhongkai Liu, Zhu-Jun Wang
Applied Physics · Cross-listed from cond-mat.str-el
Inductance Meets Memory in the Quantum Magnet Mn3Si2Te6
Tristan R. Cao, Gabriel Schebel, Arabella Quane et al. (+5 more)
Orbital degrees of freedom offer a largely untapped route to emergent dynamical phenomena in correlated
quantum materials. However, it remains unclear whether collective orbital states can intrinsically generate both reactive and
memory functionalities in a bulk system. Here we show that in the ferrimagnet Mn3Si2Te6, nonequilibrium reconfiguration of
chiral orbital currents produces both emergent inductance and nonvolatile memristance as intrinsic properties of a single
crystal. At low frequency and under a
magnetic field along the c axis, coherent orbital-current domains generate robust clockwise inductive I-V loops. At higher frequency and low field, current-driven first-order reconfiguration leads to incomplete reversal and metastable trapping, producing an intrinsic electromotive force and a finite remanent voltage at zero current. These results establish orbital currents as a class of
quantum state variables that encode both reactive and
memory functionalities, opening routes toward intrinsically reconfigurable and energy-efficient electronic systems.
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All authors Tristan R. Cao, Gabriel Schebel, Arabella Quane, Hengdi Zhao, Yu Zhang, Feng Ye, Longji Cui, Gang Cao
Optics · Cross-listed from nlin.PS
Dark solitons in nonlinear Su-Schrieffer-Heeger lattices
Rujiang Li, Muhammad Imran, Wencai Wang et al. (+2 more)
The introduction of nonlinearities into
lattices with topological band structures has led to the discovery of various types of
solitons. The Su-Schrieffer-Heeger (SSH)
lattice, as the most fundamental topological model, has been extended into the nonlinear regime. In particular, nonlinear edge states and bulk
solitons exhibiting intensity humps against a zero background have been extensively studied in nonlinear SSH
lattices. In this paper, we systematically investigate dark
solitons in nonlinear SSH
lattices. These dark
solitons maintain a nonzero and constant background, featuring intensity dips either in the bulk of the
lattice or at its edges, and residing spectrally in the semi-infinite gap or the middle finite gap. Regardless of the specific type of dark
soliton, the intensity dip remains wellpreserved and is not affected by the band structure of the original linear
lattice. Although the dark
solitons we have identified are generally dynamically unstable across a broad range of parameters, several types exhibit linear stability when the intracell coupling is much larger than the intercell coupling. Our findings may provide valuable insights for the exploration of novel types of
solitons in nonlinear topological
lattices.
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All authors Rujiang Li, Muhammad Imran, Wencai Wang, Yongtao Jia, Ying Liu
Optics
Fundamentals and Applications of Time-varying Media: A Review
Youxiu Yu, Hao Hu, Qianru Yang et al. (+4 more)
Time-varying media, characterized by dynamic or spacetime-modulated constitutive parameters such as permittivity and permeability, have recently emerged as a transformative paradigm for advanced wave control, transcending the constraints imposed by temporal translation
symmetry and energy conservation in static systems. By incorporating time as an active degree of freedom, such media unlock unique phenomena including broadband frequency conversion, temporal refraction, significant field enhancement, and magnet-free nonreciprocity. These capabilities are reshaping the landscape of photonic technologies, enabling groundbreaking applications such as broadband nonreciprocal amplifiers, non-resonant
lasers, and highly efficient particle accelerators. This review systematically classifies time-varying media based on their modulation schemes and elucidates the underlying physical principles and distinctive wave-matter interactions. We comprehensively survey the latest advances in this rapidly evolving field, highlighting exotic wave behaviors and practical implementations across electromagnetic and photonic systems. Furthermore, we summarize experimental platforms that realize time-varying responses across different frequency regimes. Finally, we assess the current state of progress, identify key challenges, and offer a forward-looking perspective on future research directions in this dynamic and promising area.
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All authors Youxiu Yu, Hao Hu, Qianru Yang, Linyang Zou, Dongjue Liu, Hao Chi Zhang, Yu Luo
Computational Physics
A Structure-Preserving Graph Neural Solver for Parametric Hyperbolic Conservation Laws
Jiamin Jiang, Shanglin Lv and Jingrun Chen
Hyperbolic conservation laws govern a wide range of transport-driven dynamics featuring shocks, contact discontinuities, and complex wave interactions, posing distinct challenges for deep-learning-based surrogate modeling. While classical numerical methods provide robust and physically admissible solutions, their computational cost restricts applicability in many-query tasks such as parametric studies and design optimization. Conversely, existing neural surrogates offer rapid inference but often fail to respect intrinsic PDE structures, leading to non-physical artifacts, rollout instability, and poor generalization. We present an interpretable, structure-preserving graph neural solver that bridges classical numerical principles with graph neural networks (GNNs). The network is designed as a learned reconstruction-and-flux operator rather than a black-box state updater, thereby inherently preserving key properties such as local conservation and upwinding. Inspired by Arbitrary high-order DERivatives schemes, we further recast message-passing GNNs as high-order space-time predictors, enabling conservative and stable neural updates with large time steps. Evaluation is performed on challenging supersonic flow
benchmarks spanning broad parametric variations in geometry, initial/boundary conditions, and flow regimes. The neural solver achieves superior long-horizon rollout stability and accuracy compared with strong surrogate baselines, outperforms low-order discretizations, and delivers orders-of-magnitude runtime speedups over high-resolution simulations.
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