Papers

Large language models (LLMs) and agentic systems have recently demonstrated potential for automating scientific workflows, including atomistic simulations. However, their deployment in high-performance computing (HPC) environments remains limited by the lack of mechanisms ensuring correctness, reproducibility, and safe interaction with computational resources. Generated workflows suffer from inconsistencies, incorrect API usage, or invalid physical configurations - leading to failed or unreliable simulations. In this work, we introduce LARA-HPC, a validation-driven agentic framework to enable reliable workflow generation for atomistic modeling on HPC systems. Our approach is based on three key components: (i) a controlled execution layer that mediates all interactions with HPC resources; (ii) simulation-native validation through dry-run capabilities, enabling execution-level verification without incurring resource cost; and (iii) a multi-phase agentic pipeline combining retrieval-augmented generation and iterative refinement. We demonstrate the effectiveness of this approach performing an end-to-end atomistic simulation workflow on HPC by applying LARA-HPC to Density Functional Theory simulations. The results show that validation-driven generation significantly improves robustness and enables iterative correction of both syntactic and physical inconsistencies. More broadly, this work advocates for a shift from generation-first to validation-first paradigms in Artificial Intelligence (AI) assisted scientific computing. We argue that the future task of the computational physics community is to develop domain specific agentic systems based on structured tooling to realize an HPC enabled co-piloted research ecosystem.

Despite their ubiquity, the rich physics present in a plasma sheath has inhibited the development of a generally applicable description of this critical region. The present study utilizes a physics-informed neural network (PINN) to evaluate a hierarchy of models of the plasma sheath. Unlike traditional deep learning methods, PINNs use the governing PDEs to constrain the predictions of a neural network, and thus do not require any experimental or simulation data to train. In this work, we utilize a PINN to identify the parametric solution to fluid models of different physics fidelity of the plasma sheath. While the offline training time of the PINN is often longer than a traditional solver, once trained, the PINN is able to efficiently predict the sheath profiles across a broad range of parameter regimes, thus yielding an effective surrogate of the plasma sheath.

Precipitation trends can arise from both dynamic factors (changes in atmospheric circulation) and thermodynamic factors (changes in atmospheric moisture content). Disentangling these contributions can aid in understanding regional climate change and improving projections. We compare two approaches which separate dynamic and thermodynamic contributions to precipitation trends over Central Chile: a moisture budget analysis and constructed circulation analogs. Both methods are applied to fields from the CESM2 Large Ensemble as well as two reanalyses. We analyze the methodological differences that lead to distinct results in each approach and evaluate their respective capabilities in capturing dynamic, thermodynamic and coupled trends. We find that the estimated dynamic trends from both methods often differ substantially for individual ensemble members, although the ensemble mean generally agrees in sign but not in magnitude. Finally, we apply circulation analogs to moisture budget terms to refine estimates of historical and future precipitation change in Central Chile. This combined framework reduces dynamic contamination of thermodynamic trends and yields projections of thermodynamic precipitation change that are weaker than that suggested by either method alone.

Direct Numerical Simulation (DNS) of a Mach 6 boundary layer over a flat plate is performed to assess the effect of spanwise non-uniform surface temperature on breakdown to turbulence under deterministic forcing. The streamwise location of laminar to turbulent transition in hypersonic boundary layers has a significant influence on viscous drag and aerodynamic heating of external surfaces of hypersonic vehicles. Previous work investigated the stabilization of hypersonic boundary layers by optimally growing streaks. More recently, DNS for a hypersonic boundary layer showed that it is possible to generate streaks through a spanwise non-uniform surface temperature distribution. The laminar computations showed the control method can stabilize the second Mack mode and it is robust across a range of Mach numbers and wall temperature ratios. In this work, two scenarios are investigated where two-dimensional (second Mack mode) and oblique (first Mack mode) disturbances dominate the initial linear stage of transition. It is found that weak control streaks with amplitude below 5% of the freestream velocity can reduce high-frequency shear-stress due to the second Mack mode by approximately 30% relative to the uncontrolled configuration, and delay transition. For first Mack mode dominated breakdown, the control streaks have no effect on transition location, but the peak amplitude of the spanwise-integrated wall heat flux is reduced. For the first and second Mack mode-dominated scenarios, the mean and high-frequency peak heat transfer are reduced approximately by 15% and 34%, respectively. The dominant mechanisms are identified and attributed to the pressure work contribution to turbulent kinetic energy and the second Mack mode dilatation work.

Mueller matrix (MM) polarimetry is an effective, non-invasive tool for retrieving information from complex media. However, the finite dynamic range of optical detectors poses a significant challenge when measurements involve strong intensity contrasts, where bright regions risk saturation while dark regions suffer from poor signal-to-noise ratio. To address this challenge, this article presents a straightforward, high dynamic range methodology that does not require non-linear algorithms. The proposed technique relies on the direct addition of raw intensities captured at multiple exposure times prior to the calculation of the MM. By extending the effective well-depth of the detector, this technique allows the 16 MM elements to be calculated across different hardware configurations with a significantly improved signal-to-noise ratio in low-intensity regions while eliminating artifacts caused by saturation. This approach offers a simple yet efficient solution for the characterization of samples, eliminating the need for hardware modifications or software trade-offs.

Ocean mesoscale eddies can be thought of as the "weather" of the ocean and strongly influence the ocean's physics, chemistry, and biology; they influence other components of the Earth system via air-sea and sea-ice interactions, and are crucial drivers of marine heat waves. Thus, proper modeling of eddies in both historical and future climates is crucial to accurately capturing the Earth system. Climate projections using global coupled models with eddying ocean components are only recently starting to be more widely used. Despite their critical role in understanding and forecasting climate characteristics, these so-called eddy-permitting models have not been explored to verify that resolved eddies are realistic, and thus any downstream scientific testing of hypotheses in biogeochemistry, ocean physics or other associated Earth systems impacted by eddies hinge on this critical assumption. This paper compares observed eddies with lifetimes longer than 6 weeks present in $1/4^\circ$ satellite altimetry data with observed eddies in $1/4^\circ$ reanalysis data and ocean model output. When compared to eddies observed in satellite altimetry data, eddies in reanalysis data and ocean model output are missing almost 30% of the number of eddy trajectories. In addition to missing eddy trajectories, the characteristics of eddies in reanalysis data and ocean model output differ from eddies observed in satellite altimetry data. At a high level, eddies in reanalysis data and ocean model output tend to live longer, are larger, and are weaker than eddies in observed altimetry data. This paper presents a variety of statistics describing these differences both spatially and in global aggregate.

A hybrid approach to numerical weather prediction is investigated, in which the unperturbed physics-based ECMWF Integrated Forecasting System (IFS) is spectrally nudged toward forecasts from a machine-learned weather forecast model, trained to forecast on model levels. Nudging is applied only to the large scales of virtual temperature and vorticity, with the objective of improving large-scale forecast skill while preserving the dynamical and physical behaviour of the underlying physics-based model at smaller scales. Consistent with previous studies, spectral nudging substantially improves large-scale forecast skill relative to the free-running IFS, with gains of up to 1.5 days in the tropics and 12-18 hours in the extra-tropics, and a reduced frequency of forecast busts. These improvements are achieved while preserving forecast variability. The representation of extreme near-surface weather is maintained or improved. Tropical cyclone track forecasts benefit from improved large-scale steering flow, while storm intensity remains comparable to that of the physics-based model and more physically consistent than in pure machine-learned weather forecast models. These results confirm that scale-selective spectral nudging provides a practical pathway for combining machine-learning and physics-based forecasting systems.

Predicting three-dimensional thermal loads during tokamak disruptions is essential for ITER yet remains weakly developed. We present a physics-based workflow that couples MHD simulations of vertical displacement events with field line tracing on a realistic 3D first wall model and a transient wall thermal response. The approach is validated against JET discharges with beryllium main chamber armour, reproducing key global dynamics, non-axisymmetric current features, and the occurrence (or absence) of melting, thereby building confidence in the methodology. We then apply the same workflow to ITER-relevant conditions with tungsten (W) armour, consistent with the new 2024 ITER re-baseline, to assess disruption heat loads and their 3D localization. The resulting analysis demonstrates the resilience of the ITER W first wall against these events and provides predictions for the energy deposition and current flow profiles. Beyond these studies, the workflow enables scenario-by-scenario estimates of disruption-induced thermal loading, allowing to assess the disruption-budget consumption for these events in future devices.

Accurately reconstructing and forecasting ocean fields from sparse observations is critical for both operational and scientific purposes. Optimizing sensor placement to maximize reconstruction skill remains challenging due to evolving ocean dynamics and practical deployment constraints. Traditional approaches, such as Empirical Orthogonal Functions, greedy search, or Gaussian processes, either assume static observation networks or scale poorly in high-resolution and non-stationary regimes. We introduce a differentiable adaptive sensor placement framework based on a Gumbel-Softmax sampling operator. Given an ensemble of forecasts or simulations, the method jointly optimizes a probabilistic sampling mask and the reconstruction mapping (e.g., Optimal Interpolation correlation lengths) under strict observation budgets. Numerical experiments are conducted for Sea Surface Height reconstruction in a Gulf Stream region through Observing-System Simulation Experiments using a state-of-the-art high-resolution ocean simulation. With a sensor budget of only 0.1% (fewer than 100 point-wise observations on a 14{\deg}x14{\deg} domain) the optimized sampling reduces the reconstruction RMSE by more than half (0.0908 m versus 0.1750 m) and increases explained variance by about 20% (93.1% versus 74.4%) compared with a uniform random strategy. The method remains robust when trained on noisy ensembles with significant spatial displacement (up to 1{\deg}), demonstrating practical applicability under forecast uncertainty. Overall, the framework provides a scalable, budget-aware approach to designing observation networks. Beyond improved skill, it yields interpretable sampling patterns that consistently target energetic regions such as eddies and fronts, offering a transferable tool for adaptive sensing in geophysical systems.

This lecture reviews the principles of particle-matter interactions, providing the essential physics background required to understand beam loss mechanisms in high-energy accelerators and their associated implications. The main interaction processes of photons and charged particles are introduced, together with an overview of nuclear reactions. The lecture then addresses electromagnetic and hadronic showers, which play a central role in particle-matter interaction physics. Following a brief overview of Monte Carlo simulation tools, with emphasis on FLUKA, the lecture concludes with a detailed examination of a representative LHC-type radiation shower.

The operation of high-energy and high-intensity particle accelerators inevitably leads to the loss of a fraction of beam particles, either through controlled processes or accidental events. This article builds on a first lecture on particle-matter interactions to review the main beam loss mechanisms in high-energy and high-intensity accelerators and their implications for safe and efficient operation. It discusses the resulting risks of equipment and material damage, radiation effects on electronics, and radiation-protection hazards. The focus is on beam losses in hadron accelerators, with particular emphasis on the Large Hadron Collider at CERN, while also addressing proposed future facilities such as the Future Circular Collider and muon colliders.

Parametric decay instability (PDI) of Alfvén wave is thought to play an important role in the dissipation of the large-amplitude Alfvén waves and in the heating of magnetized plasmas. Temperature anisotropy is frequently observed by spacecraft, including Parker Solar Probe (PSP), in the near-Sun solar wind, yet its impact on PDI in the near-Sun solar wind has been understudied. We calculate the maximum growth rates of PDI, $\gamma_{\max}/\omega_{0}$, where $\omega_0$ is the frequency of the parent wave, by solving the linear dispersion relation of Chew-Goldberger-Low (CGL) equations under several expanding background models. To assess the effect of temperature anisotropy, the growth rate is compared with that derived from ideal magnetohydrodynamics (MHD). From $R_0$ ($ = 1.02R_\odot$) to $30R_0$, we consider three expansion cases: (i) spherically symmetric adiabatic expansion with constant wind speed, (ii) Multi-source observation- and model-constrained expansion, and (iii) a PSP-constrained profile of $(\beta_{\parallel},\xi)$, where $\beta_\parallel=8\pi p_{\parallel0}/B_0^2$ is the parallel plasma beta and $\xi=T_{\perp0} / T_{\parallel0}$ is the temperature anisotropy, that includes Parker-spiral effects. We find that temperature anisotropy increases $\gamma_{\max}/\omega_{0}$ for $\beta\lesssim 0.1$ in the near-Sun solar wind: in the case of (iii), temperature anisotropy with $T_{\perp0} > T_{\parallel0}$ increases $\gamma_{\max}/\omega_{0}$ by factors of $\sim 1.5$ over $R\simeq 1$–$10\,R_0$, whereas temperature anisotropy with $T_{\parallel0}>T_{\perp0}$ decreases $\gamma_{\max}/\omega_{0}$ at larger $R$. Our results suggest that the temperature anisotropy plays an important role in the onset of PDI even in low-$\beta$ regimes, such as the near-Sun solar wind.

Searches for neutrino electromagnetic interactions at reactor sites require an unusual combination of ultra-low thresholds and a stable low-background environment. It is shown here that cryogenic undoped cesium iodide (CsI) naturally satisfies these conditions in a way prior detectors have not. Although suppression of nuclear recoil ionization efficiency at low energies limits the use of this scintillator for coherent elastic neutrino-nucleus scattering, that same property renders the detector effectively blind to those nuclear recoils from MeV-scale reactor antineutrinos. This leaves the low-energy regime free to expose neutrino-electron ($\bar{\nu}_{e} -e^{-}$) scattering as the dominant observable channel and converts cryogenic CsI into a targeted probe of electromagnetic couplings. This work presents a conceptual design based on pure CsI crystals immersed in an active xenon-doped liquid argon veto evaluated under realistic intrinsic and environmental backgrounds. Under present detector capabilities, order-of-magnitude improvements over current reactor limits on the neutrino magnetic moment and millicharge are achievable. Cryogenic pure CsI therefore offers a distinctive and scalable route to leading studies of $\bar{\nu}_{e} -e^{-}$ physics.

Row column addressed (RCA) transducers present a promising solution for ultrafast volumetric imaging with a reduced channel count and a large field of view. However, RCA-based 3D imaging is fundamentally limited by severe sidelobe artifacts and a low signal-to-noise ratio (SNR), primarily due to weak transmit focusing inherent in RCA based ultrafast imaging strategies. To overcome these challenges, we propose a cross fusion and correlation (CFAC) method that leverages the incoherence of sidelobe artifacts and noise across datasets acquired using orthogonal apertures and multiple steering angle sets. The performance of the proposed method was validated through simulations, in vitro imaging of a multi-purpose ultrasound phantom, and in vivo experiments, and benchmarked against four established techniques: orthogonal plane wave (OPW) imaging, XDoppler method, row-column-specific frame-multiply-and-sum beamforming (RC-FMAS), and coherent factor (CF) imaging. Simulation results demonstrated that CFAC reduced sidelobe levels by 42.0 dB, 38.9 dB, 28.3 dB, and 25.5 dB compared to OPW, XDoppler, RC-FMAS, and CF, respectively. In phantom experiments, CFAC improved the CNR by up to 17.5 dB. Furthermore, in vivo imaging of a rat kidney showed that CFAC enables visualization of a significantly more detailed microvascular network, achieving a CNR improvement of over 25 dB against all benchmarked methods. In conclusion, the proposed CFAC method effectively suppresses sidelobe artifacts and noise in RCA-based imaging under low-SNR conditions, enabling high-contrast 3D visualization while preserving the high frame rate capabilities of ultrafast ultrasound imaging.

Motivated by the importance ascribed to correlations in random matrices used to model phenomena in various scientific disciplines, we report how algebraic correlations between matrix elements affect the eigenvalue statistics and spectral density of random matrices. These correlations, introduced through thresholded Gaussian fields on a square lattice, decay as a power law $\propto r^{-2H}$, with exponent $H>0$. As $H$ varies, both the eigenvalue distribution and excess kurtosis undergo qualitative changes. The spectral variance approaches $1/2$, while the moment of order $2n$ is finite only for $H>(n-1)/(2n)$. Long-wavelength scaling gives an algebraic density tail $|\lambda|^{-1-2/(1-2H)}$ for $0<H<1/2$ and the corresponding finite-size powers and logarithms. At $H=1/2$ the support is unbounded although every fixed moment is finite; for $H>1/2$ it is bounded and approaches the semicircle as $H\to\infty$. A self-consistent equation determines the limiting density from the binary covariance and fixes the excess kurtosis through the squared correlations along a row. Our results link real-space correlations to spectral regimes, distinguishing the roles of the long-distance exponent and the full covariance.

Surface functionalization plays a decisive role in the performance of biosensors, as it governs the efficiency and stability of biomolecule immobilization at the sensor interface and, consequently, the overall performance of the biosensing platforms. In this work, we present a comparative study of three organosilane chemistries - APTES, APDMS, and CPTES - applied to a SiO2 terminated 1D photonic crystal able to sustain Bloch surface waves and designed to operate as optical biosensors in both label free and fluorescence enhanced modes. Each chemistry was evaluated through a standardized label-free protocol based on the interaction between immobilized SARS CoV 2 spike protein and its corresponding antibodies, enabling quantitative assessment of binding efficiency, nonspecific adsorption, and signal repeatability. CPTES exhibited the most favorable balance between specific signals, reduced variability, and low nonspecific adsorption. The three chemistries were subsequently tested in fluorescence mode for the detection of anti SARS CoV 2 IgG antibodies in human serum, demonstrating the suitability of BSW enhanced fluorescence for rapid serological analysis. Overall, the study identifies CPTES as the most robust and reproducible functionalization strategy among the three investigated for BSW biosensing and highlights the potential of the platform for fast, sensitive detection of clinically relevant antibodies.

We investigate a bi-directionally coupled system consisting of a Kerr-nonlinear microresonator and a continuous-wave single-mode semiconductor laser. Inside the resonator, a forward-propagating and a backscattered field interact nonlinearly, while a fraction of the backscattered field is fed back into the laser cavity. We show in this paper that the interaction of the laser with the feedback opens up new ways of stabilizing $1$-solitons. Using numerical bifurcation analysis, we systematically identify existence ranges of time-harmonic 1-soliton states in the anomalous dispersion regime. We demonstrate that, in contrast to the uni-directional configuration, the bi-directional coupling introduces a dynamic self-correcting response of the laser frequency that stabilizes $1$-solitons. These enhanced stability properties of $1$-solitons thus enable robust and self-started frequency-comb generation, consistent with the existing experimental observations.

The non-monotonic dependence of Al dopant activation on implantation temperature in 4H-SiC has been experimentally observed, but its atomistic origin remains unclear. We present a molecular dynamics (MD) study of Al implantation at $500$,K and $900$,K over seven doses from $1\times10^{13}$ to $7.5\times10^{14}$,cm$^{-2}$, followed by up to $100$,ns of annealing at $1500$–$2500$,K. Using the Gao-Weber potential combined with a reparameterized Morse potential for Al-SiC interactions fitted to DFT migration and kick-in/out barriers, we show that implantation at both temperatures reduces Frenkel-pair formation and extended amorphous pockets compared with room-temperature implantation. Above the Al solubility limit ($>10^{20}$,cm$^{-3}$), however, annealing reveals a non-monotonic temperature dependence. Samples implanted at $900$,K form larger, kinetically stable interstitial clusters that persist throughout annealing and act as sinks and trapping centers for Al, reducing substitutional incorporation. Although the $500$,K samples initially exhibit lower crystallinity, they contain a significantly larger fraction of substitutional Al after annealing. The simulations identify two regimes: a low-dose regime dominated by isolated point defects and small complexes, and a high-dose regime characterized by defect clustering and planar-defect formation with strong implantation-temperature dependence. These results explain the experimentally observed optimal implantation window between $500$ and $900$,K and suggest that controlled nanoscale amorphization at $500$,K enhances activation through regrowth-assisted incorporation while suppressing extended defects. The simulations also identify a new basal-plane diffusion path for Al and an activation mechanism involving kick-out of a carbon antisite; both were confirmed by DFT-NEB calculations.

Optical tweezers are a powerful tool for creating defect-free arrays of atoms and molecules, enabling advances in quantum simulation, computation, and precision metrology. However, the achievable array size is limited by the initial loading fraction, typically $50\,\%$ for atoms and $35\,\%$ for molecules. Here, we propose a general scheme for enabling multiple loading cycles by protecting trapped particles using a repulsive barrier. We show that collision-limited lifetimes of particles in protected tweezers can exceed one second, leading to filling fractions of over $80\%$ after four loading cycles. Combined with existing rearrangement techniques, this approach enables efficient unity filling of tweezer arrays and provides a scalable pathway towards larger quantum technology platforms.

Nanophotonic supercontinuum generation offers a practical route to chip-based f-2f interferometry by leveraging coexisting chi(2) and chi(3) nonlinearities. In conventional uniform waveguides, the phase-matching bandwidth for second-harmonic generation (SHG) is intrinsically narrow, restricting the spectral overlap factor for heterodyne beating. To address this limitation, we introduce a periodically-tapered nanophotonic waveguide made from MgO-doped, z-cut thin-film lithium niobate for energy-efficient and fabrication-robust f-2f operation. By adiabatically varying the waveguide width within a dual phase-matching window that supports concurrent dispersive wave (DW) emission and SHG, we routinely achieved a broad spectral overlap between the SHG and DW components. This capability enables robust detection of the carrier-envelope offset frequency (fceo) at substantially lower pulse energies than that in uniform-waveguide approaches. We further developed a compact waveguide module that operates reliably under temperature fluctuations and is capable of interfacing with high-repetition-rate (500 MHz) mode-locked lasers, enabling detection and phase locking of fceo with a signal-to-noise ratio of 48 dB. These results highlight the potential of nanophotonic chips for developing compact, field-deployable frequency comb systems.

Rare-earth-doped materials constitute the foundation of conventional solid-state lasers, but their bulk-crystal form is inherently incompatible with photonic integration, making it challenging to realize compact, high performance nanoscale laser sources. Lithium niobate on insulator (LNOI), with its exceptional electro-optic and nonlinear optical properties, has emerged as one of the most promising platforms for integrated photonics. Combining Nd3+ doping with LNOI offers the unique possibility of uniting the efficient gain provided by Nd3+ ions with the excellent characteristics of LNOI. However, on-chip laser emission from Nd:LNOI has not been demonstrated previously. In this work, we report the first realization of an integrated Nd:LNOI microdisk laser, demonstrating lasing at 1094.17 nm under 785.10 nm pumping with a low threshold of 146 uW and a slope efficiency of 1.962*10^(-5). Beyond continuous-wave operation, we further observe self-induced laser pulsing on the hundred-microsecond scale, with a laser-pulse duration down to 500 us and an oscillation period of 6.45 ms, arising from nonlinear thermo-optic-photorefractive dynamics. We demonstrate stable continuous wave lasing and self-induced pulsed emission within a monolithically integrated Nd:LNOI cavity. Our results expand the operational degrees of freedom for LNOI-based lasers and open a new direction toward deeply integrated gain with intrinsic nonlinear dynamical processes.

Hexagonal diamond (HD), an exotic carbon allotrope recently synthesized in bulk form, exhibits superior mechanical properties compared to cubic diamond (CD) and holds promise for advanced industrial and quantum applications. Using first-principles calcu-lations, we systematically investigate intrinsic defects, extrinsic dopants, and defect complexes in HD. Our study shows that VC dominates intrinsic conductivity, while Ci is unstable. Among extrinsic dopants, boron acts as a benign acceptor enhancing p-type conductivity, whereas nitrogen and phosphorus serve as effective donors for n-type conductivity. Group II and Group IV dopants, however, introduce high formation energies or neutral charge states with limited impact. Furthermore, VC, MgC and XV defect com-plexes display multiple spin and charge states within the HD band gap, highlighting their potential as color centers for hosting qubits. These results not only clarify the defect physics of HD but also demonstrate its broader implications for conductivity engineering and quantum technologies.

Pinching of the driver beam in plasma wakefield acceleration is generally considered an unwanted effect that needs to be mitigated. Here, we propose that this effect can be utilized for the injection of spin-polarized electron beams from hydrogen halide targets into wakefields. Particle-in-cell simulations show that the electron spin is preserved on a level of 50% for a wide range of parameters due to the injection geometry. The presented injection scheme provides a possible pathway to alleviate some of the restrictions associated with pre-polarized hydrogen halide targets.

Understanding the dynamical evolution of large-scale moiré systems is crucial for connecting theoretical predictions with experimental observations. Here we develop a machine-learning-based workflow, integrating DeePMD and DeepH frameworks with first-principles calculations, to efficiently investigate time-dependent structural and electronic responses in twisted bilayer transition metal dichalcogenides (TMDs) with experimentally relevant moiré supercells containing over 3000 atoms. Using $\mathrm{WS_2}$ as a representative system, we show that low-temperature lattice vibrations and relaxation deepen the moiré potential wells, narrow the lowest conduction band, and facilitate the formation of strongly localized electronic states. Based on DFT-derived moiré potentials that incorporate these dynamical effects, density-matrix-renormalization-group (DMRG) simulations reveal robust Wigner crystallization and a kagomé-patterned three-electron state, consistent with recent experimental observations. Our workflow provides a practical route for exploring large moiré supercells beyond static configurations and offers new insight into the interplay between lattice dynamics, electronic localization, and emergent correlated states in twisted two-dimensional materials.

International student mobility plays a critical role in shaping future research careers, particularly in highly globalized fields such as astrophysics. The Leiden/ESA Astrophysics Program for Summer Students (LEAPS) offers a 10-week, fully funded research program at Leiden Observatory and the European Space Agency's ESTEC centre for undergraduate and master's students. Designed to foster early research involvement, LEAPS supports students from diverse academic and cultural backgrounds. Since its inception in 2013, LEAPS has hosted 194 students from over 40 countries. Data collected for 165 participants reveal that over 50% have progressed to Ph.D. studies, with some members of earlier cohorts already securing competitive international fellowships in astronomy. LEAPS participants have collectively contributed to at least 25 peer-reviewed publications and 13 international conference presentations. LEAPS has contributed successfully in preparing undergraduates for research careers in astrophysics through hands-on experience, mentorship, and scientific exposure. By addressing barriers related to financial means and promoting diversity, the program not only enhances individual career trajectories but also contributes to the broader goal of inclusive academic mobility. Continued efforts are needed to further increase global representation and assess long-term impacts on participants' scientific careers.

We propose a quantum-enhanced sensing scheme for the detection of wave-like dark matter and high-frequency gravitational waves using two-dimensional ion crystals in a Penning trap. The protocol employs spin-motion squeezed states to improve the signal-to-noise ratio and enable a super-Heisenberg scaling with respect to the number of ions over a broad parameter range. We analyze the sensitivity of the protocol to representative wave-like dark matter candidates, including the axion-like particle and the dark photon, as well as to high-frequency gravitational waves, taking into account the decoherence and dephasing of the ion spins. Our results indicate that two-dimensional ion crystals and this new protocol provide a promising platform for probing previously unexplored parameter space in searches for light dark matter and high-frequency gravitational waves.

The linear and nonlinear motions of a damped rigid planar pendulum, driven by vibrating its pivot sinusoidally, are reexamined. The pendulum is known to exhibit periodic, quasiperiodic, and chaotic motions. Floquet analysis identifies regions of instability and stability within the driving parameter space. A new type of nonlinear oscillation may occur at driving parameters where Floquet analysis predicts a stable stationary state. Such non-Floquet oscillations always have periods longer than twice the period of the vibrating pivot. The possible periods of these oscillations may be four, six, eight, or twelve times the driving period. The power spectrum of the pendulum's angular velocity during these oscillations reveals a novel feature: the two dominant response frequencies sum to the driving frequency.

While the Dirac band structure of graphene has established it as a leading platform for ultrafast optoelectronics, its non-perturbative nonlinear response under intense excitation remains poorly understood. Here, we report ultrafast spectral modulation of nonlinear optical signals in graphene. By utilizing a robust suspended-graphene platform that allows for both wide-range electrostatic gating and high optical damage thresholds, we observe dramatic frequency shifts (up to 8 THz) in third-harmonic generation (THG) and sum-frequency generation (SFG) driven by pump-induced nonequilibrium carrier dynamics. The magnitude and even the direction of this spectral shift can be reversibly controlled by the Fermi level and excitation conditions. A quasiequilibrium theoretical framework based on hot-carrier dynamics quantitatively reproduces the measured spectral evolution, elucidating the critical interplay between carrier heating and the Fermi level. These findings establish a universal mechanism for carrier-mediated spectral control, providing a practical route toward high-speed, gatetunable nonlinear photonic architectures.

Understanding quantum system dynamics driven by nonclassical light pulses is challenging, particularly for general light states with large photon numbers. Here we introduce an efficient framework that makes this task tractable. By introducing a pulse-shaped P-representation, the exact quantum evolution is decomposed into a mixture of many independent quasi-classical branches, each governed by a standard master equation with a classical pulse which can be solved efficiently. As an illustration, for a two-level system interacting with an exponential pulse, we first find out the exact analytical solutions to the Bloch equations in each quasi-classical branch, and then by taking proper P-function average over all branches, the full system dynamics driven by nonclassical light pulses is analytically obtained. For the one-photon and two-photon cases, our method well reproduces the previous exact results either analytically or numerically. Crucially, our approach scales efficiently to more complex light states (Fock, thermal, squeezed vacuum states) with large photon numbers ($N\sim 100$). We further provide a clear physical interpretation how the system dynamics is influenced through the high-order optical coherence of the nonclassical pulses. This work provides a unified and computationally efficient route and a useful starting point to explore more complex quantum dynamics driven by nonclassical light in quantum optics and quantum information processing.

We demonstrate a suspended thin-film aluminum nitride (AlN) microbolometer for narrowband very long-wave infrared detection. The device uses a 100-nm-thick AlN membrane suspended above a Pt back reflector by a 1-um air gap. Resonant absorption is set by the AlN transverse optical phonon near 15.4 um and is strengthened by suspension above the reflector. A periodic perforation pattern reduces membrane thermal mass and enhances absorption without further thinning the film. DC resistance measurements under tunable infrared illumination verify bolometric operation, and the measured spectral response follows the absorption profile expected from spectroscopic measurement of passive devices. Narrowband response is observed in the 14–18 um range, with peak responsivity of 920.8 ppm/mW at 15.48 um. This platform can enable compact wavelength-selective thermal detectors for multispectral imaging, on-chip infrared spectroscopy, and chemical sensing.