Papers

High-accuracy correlated four-component relativistic electronic structure methods are typically formulated in terms of integrals over molecular orbital (MO). Consequently, an efficient and scalable strategy is required to deal with the complexity of transforming relativistic two-electron integrals from the atomic orbital (AO) to the MO basis. The transformation bottleneck is particularly acute for approaches that include Breit interaction integrals, whose computational and memory demands further exacerbate the transformation cost. To overcome this challenge, we develop a quaternion-based, AO-driven direct integral transformation scheme. The method operates on scalar AO integrals and combines quaternion density-based contractions with direct Cauchy-Schwarz screening to systematically exploit integral locality. As a result, the proposed framework substantially lowers the practical computational scaling and provides an efficient, memory-conscious, and highly parallelizable pathway for the routine inclusion of relativistic Dirac-Coulomb-Breit integrals in large-scale four-component correlated calculations.

Optical detectors with single-photon sensitivity and large dynamic range would facilitate a variety of applications. Especially, the capability of extending operation wavelengths into the mid-infrared region is highly attractive. Here we implement a mid-infrared frequency upconversion detector for counting and resolving photons at 3 $\mu$m. Thanks to the spectro-temporal engineering of the involved optical fields, the mid-infrared photons could be spectrally translated into the visible band with a conversion efficiency of 80%. In combination with a silicon avalanche photodiode, we obtained unprecedented performances with a high overall detection efficiency of 37% and a low noise equivalent power of 1.8$\times$10$^{-17}$ W/Hz$^{1/2}$. Furthermore, photon-number-resolving detection at mid-infrared wavelengths was demonstrated, for the first time to our knowledge, with a multi-pixel photon counter. The implemented upconversion detector exhibited a maximal resolving photon number up to 9 with a noise probability per pulse of 0.14% at the peak detection efficiency. The achieved photon counting and resolving performance might open up new possibilities in trace molecule spectroscopy, sensitive biochemical sensing, and free-space communications, among others.

Electoral redistricting in Ireland's Proportional Representation Single Transferable Vote (PR-STV) system faces the challenge of selecting an optimally representative set of electoral boundaries from an enormous set of possible configurations, and where “representative” is a delicate balance of constitutional objectives that are often in tension with one another. We present the first computational framework for Irish electoral redistricting that systematically optimises across multiple constitutional requirements while making trade-offs explicit and quantifiable. The electoral redistricting problem is parsed using statistical physics, where constitutional objectives are considered as terms in a Potts Hamiltonian. Markov Chain Monte Carlo (MCMC) methods and simulated annealing are employed to minimise this objective function, systematically exploring this configuration space, with coupling constants as proxies for objective weightings. Multi Criterion Decision Analysis (MCDA) and Pareto Optimality is then utilised to remedy the ambiguity in choosing a certain objective weighting combination over others. With respect to proportional representation and compactness objectives evaluated in County Cork, COTHROM consistently improves on the existing legal constituency boundaries for a range of objective weightings.

Fixed convolutional representations can induce structured geometry in time-frequency data even without task-specific or source-domain training. We audit a previously reported morphology-space analysis of two granite acoustic-emission experiments. The original cumulative angular-path endpoint is reproduced to within 2.1e-7 in the Archive-B/Archive-A ratio. However, the same directional contrast is obtained with ImageNet features and with three randomly initialized EfficientNet-B0 encoders: the preserved Aether representation gives a ratio of 1.411, ImageNet gives 1.361, and the random encoders give 1.480–1.503. We therefore withdraw the attribution of this contrast to interferometric pretraining. We also correct a reversal between archived OG1/OG3 file identifiers and the physical experiments. Phase randomization and temporal shuffling alter the preserved trajectory, but these responses are reinterpreted as pipeline-dependent diagnostics rather than evidence of a learned universal elastic morphology. The corrected result is narrower: multiple fixed convolutional maps expose a reproducible contrast between two acoustic-emission datasets. The signal statistics and architectural biases responsible for that contrast remain open questions.

Waveform cross-correlation (WCC), when used for detection at seismic arrays, allows for a reduction in threshold by an order of magnitude compared to beamforming. An additional benefit of WCC detection is the significant suppression of signals unrelated to the studied area. This effect simplifies and improves the accuracy of local phase association with event hypotheses located near corresponding master events. All the advantages of WCC-based processing are best utilized in the detailed study of low-magnitude seismicity prior to mega-earthquakes. For the Sea of Okhotsk Mw8.3 earthquake that occurred on May 24, 2013, WCC processing detected the onset and subsequent evolution of intensive, low-magnitude seismicity during the five days leading up to the mainshock. The Kamchatka earthquake on July 29, 2025, is analyzed using a similar WCC-based processing extended by a detailed study of the recurrence curves of the cross-correlation bulletin (XSEL). The transition to the coseismic phase was marked by a progressive increase in the intensity of seismic activity with growing magnitude. These XSEL events fell below the detection level of the International Data Centre, which utilizes the same data stream from the International Monitoring System.

The detection of mass distributions and mass transport via gravity mapping is a key geophysical tool for understanding the structure and dynamics of the earth. Changes in mass distribution, driven by natural processes and human activity (e.g., extraction of oil, gas, and minerals), contribute to observable phenomena such as sea-level rise (3 mm per year), increased flooding, landslides, and ice mass loss (hundreds of giga-tons per year). These processes generate gravity variations detectable by gravimeters and gradiometers on ground and in space. Current instruments achieve sensitivities of 10-100 micro-GAL per square root of Hz and enable applications including hydrocarbon exploration, volcanic monitoring, and subsurface detection. They also measure Earth tides (100-300 micro-GAL amplitude), requiring long-term stability over days. However, existing systems are limited by size (more than 8 kg) and cost (more than 100,000 USD), restricting widespread deployment. Here we demonstrate a levitated mechanical sensor (LOMS) with a demonstrated sensitivity of 18 micro-GAL, a large dynamical range, and an integration time of 6 s, with an expected sensitivity of smaller than 200 nano-GAL per square root of Hz in a volume of only a few cubic cm. We resolve earth tide signals, demonstrating stability comparable to state-of-the-art instruments. Unlike conventional accelerometers (micro-g sensitivity, low stability), our device operates as a true gravimeter. Its compact size and low projected cost enable scalable deployment, including drone-based surveys (10-100 m altitude), distributed sensor networks, and multipixel gravity imaging arrays. This platform enables high-resolution, cost-effective gravity mapping with potential for large-scale geophysical monitoring.

Hairpin vortices are fundamental structures within turbulent boundary layers, playing a crucial role in energy dissipation, mixing, and momentum transport. However, accurately extracting these structures remains challenging due to their irregular shapes, varying scales, and entanglement with surrounding vortical structures. This paper presents a novel framework for the extraction of hairpin vortices from turbulent boundary layers. The method begins by identifying vortical regions and decomposing them into smaller segments using merge tree based segmentation. A novel bottom up rejoining approach is then introduced to group candidate segments according to the geometric and physical characteristics of hairpin vortices, resulting in regions that encompass complete hairpin vortex structures. These regions are subsequently refined and validated through skeleton analysis to detect the characteristic hairpin shape and are further confirmed using additional scalar based criteria. Finally, smooth enclosing surfaces are generated for effective visualization. To enable quantitative evaluation, reference hairpin vortices are extracted from several flow datasets and used as ground truth. Compared with existing approaches, the proposed method eliminates manual parameter tuning, reduces under and over segmentation, and significantly improves both accuracy and computational efficiency. Demonstrations on multiple turbulent flow cases show that the method is robust and effective for hairpin vortex extraction under varying boundary layer conditions.

We propose a controlled momentum-space dual of the Fock–Lorentz (FL) transformations and use it to derive a deformed relativistic mass shell. Restricting the FL conformal factor to the cosmological-frame world line $\vx=0$, the invariant relation takes the form $(E^{2}-\vp^{2}c^{2})(1+ct/R)^{2}=m_{0}^{2}c^{4}$, which is equivalent to the standard special-relativistic dispersion law with a time-dependent apparent mass $\mapp(t)=m_{0}/(1+ct/R)$. Canonical quantization then yields Klein–Gordon (KG) and Dirac equations containing a slowly varying mass scale. We show explicitly that squaring the Dirac equation reproduces the KG operator, modulo first-order corrections proportional to $\dot\mapp$ that are suppressed by the ratio of the Compton wavelength to the FL scale. The construction is not presented as a unique covariant phase-space theory; rather, it is a world-line ansatz designed to isolate the spectral consequences of the FL conformal factor. As applications, we study the one-dimensional KG and Dirac oscillators. In the adiabatic regime, governed by the small parameter $\epsilon=c/(R\omega)\ll1$, closed-form instantaneous spectra are obtained. The Dirac-oscillator calculation is carried out in component form and then reduced to the physical spinor spectrum, thereby avoiding the double counting of the upper and lower component ladders. Dimensionless plots illustrate the apparent-mass drift, the induced spectral evolution, and the domain of adiabatic validity. For cosmological values of $R$, non-adiabatic corrections are entirely negligible; in the formal limit $t\to\infty$ the apparent mass tends to zero and, for fixed quantum number, the instantaneous levels collapse toward $E=0$.

Control of coherent waves is often restricted to phase-only actuation in multimode systems, yet the resulting physical limits remain poorly understood. Here, we show that restricting control to relative phases confines dynamics to a compact manifold whose geometry produces isolated stationary interference basins with robustness governed by local curvature. Imperfections act as smooth perturbations that soften basin structure without eliminating stationary states. This geometry imposes a universal scaling constraint: although the number of stationary states increases with system dimensionality, achievable localization contrast degrades through leakage into uncontrolled degrees of freedom. Experimentally, we demonstrate this in a telecom-wavelength multimode photonic lantern, where coarse phase-only scans directly map stable interference basins, reveal efficiency-stability trade-offs, and identify robust operating regimes without transmission-matrix reconstruction, adaptive optimization, or system inversion. The framework establishes a practical calibration-free approach for constrained multimode coherent control and applies broadly to optical, microwave, acoustic, and finite-dimensional quantum systems.

The $G3W2$ approximation – the second-order self-energy beyond $GW$ – is known to violate some fundamental analytic properties of the self-energy. In particular, its lack of positive semi-definiteness leads to unphysical features such as negative spectral functions. In this work, we reformulate the $G3W2$ approximation within the algebraic-diagrammatic construction (ADC) framework. The resulting ADC-$G3W2$ formalism enforces the same analytic form as the exact self-energy, namely a sum-over-state representation, and, consequently, guarantees positive semi-definiteness. Starting from the $GW$ self-energy, we construct a hierarchy of ADC-based approximations of increasing sophistication, including ADC-2SOSEX, ADC(3)-$G3W2$, and a full ADC-$G3W2$ scheme. These methods can be interpreted as nonperturbative resummations of vertex corrections to the self-energy, yielding Hermitian effective Hamiltonians whose diagonalization provides quasiparticle and satellite energies. This establishes a formal bridge between many-body perturbation theory formulated in terms of the screened interaction $W$ and conventional ADC schemes based on the bare Coulomb interaction. The performance of these ADC-based approximations is gauged for valence ionization potentials and benchmarked against their parent method.

Global infrasound monitoring provides a persistent means of detecting energetic bolide atmospheric entries, complementing optical observations and extending coverage over remote regions. We present a global assessment of the physical factors governing bolide infrasound detectability by correlating 623 bolide events reported by the Center for Near-Earth Object Studies between 2007 and 2025 with waveform data from the International Monitoring System. We identify 311 events with confirmed infrasound detections, corresponding to a detection rate of approximately 50%, substantially higher than inferred from earlier surveys, reflecting both the maturation of the global infrasound network and advances in automated, multi-frequency array processing. Analysis of flight parameters shows that infrasound detectability is selective rather than uniform across the bolide population. Detected events are preferentially associated with steeper entry angles and lower-altitude energy deposition, while shallow, high-altitude trajectories are less consistently observed. Very high-energy events remain detectable regardless of geometry, but for the more common lower-energy regime, observability depends on specific combinations of entry parameters and propagation conditions. This geometric dependence persists across comparable energy ranges and atmospheric conditions, indicating that entry angle exerts a primary control on detectability, with energy and propagation acting as secondary modulating factors. These results provide new physical constraints on bolide-atmosphere interactions and improve interpretation of global infrasound observations for planetary defense and atmospheric-entry studies.

In quantum technologies, continuous-variable systems offer advantages over their discrete counterparts. However, continuous-variable tomography suffers from exponentially growing sample complexity. We propose protocols using quantum mirrors to transfer the complete information of incident photonic states onto a control atomic system. This enables full photonic state characterization through measurements on the control atom alone, realized via kernel functions, direct wavefunction reconstruction, and pointwise Wigner function measurements. Our approach overcomes the limitations of conventional photon counting, statistical inference, and inverse transformation, providing a robust framework for benchmarking and verifying non-Gaussian states in continuous-variable quantum optics.

The spin dynamics of water ice in the presence of external magnetic fields are investigated. The employed model is based on the approach introduced by Buntkowsky et al. [Z. Phys. Chem. 222, 1049 (2008)], which considers two nearest-neighbor water molecules and yields a four-spin system, as the abundant oxygen isotope has zero nuclear spin. The model is extended to include coupling to external magnetic fields, allowing us to analyze the interplay between magnetic dipole-dipole interactions and magnetic field coupling. Two types of configurations are examined: (i) static, homogeneous fields, corresponding to a time-independent interaction, and (ii) spatially varying sinusoidal fields in relative motion with the molecules, leading to a time-dependent interaction. All computations are performed within the density operator formalism. The ortho/para populations and the total spin projections are evaluated during the first tens of milliseconds following the gas-to-solid phase transition. For static homogeneous fields, we show that increasing field strength suppresses dipolar-induced depolarization. Assuming that all molecules are initially in the para state, we show that static homogeneous fields can drive the ortho population up to approximately $50\%$, whereas suitably chosen sinusoidal-field configurations can increase it beyond $90\%$. These results are relevant for schemes aiming to preserve or manipulate nuclear-spin polarization during deposition.

State-of-the-art transistors have been successfully scaled the gate lengths and channel thicknesses down to 5 nm for high-performance and energy-efficient information processing. However, reducing channel width below 40-50 nm remains a bottleneck, as dangling bonds, edge disorder, and lateral depletion suppress drive current and degrade device performance. Here, we break this width-scaling wall using ultra-scaled two-dimensional semiconductor (2DSC) nanoribbon transistors down to 15 nm. In contrast to the conventional scaling rule of degradation of current density upon width scaling, our atomically-thin monolayer and bilayer molybdenum disulfide nanoribbon transistors exhibit enhancement of on-current density of up to 230% and 170%,respectively, followed by a saturation for the narrowest channels down to 15 nm. The ultra-narrow nanoribbon transistors maintain the highest on/off ratios reported so far for similar device dimensions, with improved mobility and threshold-voltage stability, indicating reduced edge scattering and depletion with a stronger electrostatic control. These findings lead to a breakthrough in width scaling rules using 2DSC nanoribbons with enhanced performance at narrower channel widths, which is promising for the ultimate scaling of transistors.

Accurate simulation of metal melting and solidification under localized energy deposition is crucial for the advancement of beam-based manufacturing technologies. This study presents an extended multiphysics model that addresses a critical limitation of prior approaches by incorporating volumetric changes from phase transitions and thermal expansion, in addition to capillary and thermocapillary effects. Validation against the benchmark problems – including a one-dimensional Stefan problem, two-dimensional solidification with free surface, and axisymmetric laser melting – demonstrates the high fidelity of the proposed model in describing melt-pool dynamics and free-surface evolution. The numerical implementation features a novel mass-correction algorithm that reduces the mass conservation error by several orders of magnitude, while a smoothed mushy-zone formulation in the enthalpy method mitigates the discretization artifacts in solid-liquid interface tracking. The results indicate that volumetric shrinkage plays an important role in surface topography formation during solidification.

How much should an LLM agent remember, and how should multi-agent systems be connected when trying to reach consensus? We show these two design choices interact in a way that flips the sign of memory's effect on coordination. Across 432 simulation runs of a networked Naming Game on eight fixed 16-agent topologies, we vary memory depth and network structure. Longer memory slows the time to reach steady state in decentralized networks but accelerates it in centralized ones; the same parameter pushes the system in opposite directions depending on topology. Critically, "faster settling" in centralized networks means locking in to a fragmented plateau more quickly, not reaching system-wide consensus, which can be used to generate diverging opinions. We further document a memory-mediated speed-unity trade-off: centralized networks consistently preserve more competing conventions than decentralized networks, but their settling speed depends sharply on memory. At the agent level, within-network analyses show that high-betweenness bridges suffer a brokerage penalty while agents in locally clustered neighborhoods achieve higher coordination success. Finally, in search of analytically tractable generative mechanisms, we find that agents' choices are well captured by Fictitious Play, indicating belief-based rather than reward-based adaptation. The practical implication: memory depth and communication topology should be co-designed, not optimized in isolation.

Computed Axial Lithography is a volumetric additive manufacturing method that selectively cures photosensitive resin through the 3D superposition of patterns of light, offering advantages over layer-based processes including rapid print times, reduced layer artifacts, and compatibility with high-viscosity materials. However, diffusive effects, primarily those of free-radical quenchers such as oxygen, blur the boundary between cured and uncured regions, limiting resolution and preventing the reproduction of sharp, high-spatial-frequency features. By comparing micro-CT data to computational dose models convolved with kernels across a range of diffusivities, we establish a framework for extracting a single diffusion kernel from any standard uncorrected print to account for all observed deviations from the target. In this work, we correct diffusion-induced blurring by co-optimizing for its effects alongside the inherent blur of the computed tomography reconstruction, demonstrating improved fidelity over previous approaches of pre-compensating the target geometry via deconvolution.

High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require ${\cal O}(100)$ function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-to-end generation. We introduce a unified framework that improves the balance between speed, shower quality, and physics fidelity. The method combines: (i) an average velocity field integrator that enables sampling in one or a few evaluations; (ii) a learned generative prior in shower space, constructed from data rather than random noise; and (iii) physics-guided loss terms that impose inductive biases on key observables during training. These elements are training time regularizers, preserving end-to-end inference with no additional cost. With only one or a few evaluation steps, the model achieves shower quality competitive with state-of-the-art flow and diffusion approaches, tested on several public high granularity calorimeter datasets. The results demonstrate inter-layer shower structure consistent with the underlying physics, providing a strong candidate for future fast simulation workflows.

Two-dimensional (2D) axisymmetric simulations are conducted to investigate convection in a rotating cylindrical annulus with localized heating at the outer bottom edge and uniform cooling at the inner cylindrical wall. The resulting radial and vertical temperature gradients generate buoyancy-driven motion and produce a stratification pattern relevant to atmospheric circulation. The effects of aspect ratio (\(\Gamma\)), Rayleigh number (\(Ra = 2.4 \times 10^{7}\)–\(1.2 \times 10^{9}\)), and Taylor number (\(Ta = 1.6 \times 10^{7}\)–\(1.2 \times 10^{9}\)), including the non-rotating limit (\(Ta=0\)), are examined. Convection is largely confined to thin boundary layers, while the fluid interior remains diffusion dominated. Without rotation, the temperature field exhibits nearly horizontal isotherms. Rotation establishes quasi-hydrostatic and geostrophic balances that redistribute heat and promote deeper penetration of isotherms into the interior. Heat transfer, quantified by the Nusselt number (\(Nu\)), depends strongly on \(Ra\), \(Ta\), and \(\Gamma\). For moderate and high \(Ra\), \(Nu\) follows the scaling \(Nu \sim Ra^{1/4}\) and is only weakly influenced by rotation. At low \(Ra\) and high \(Ta\), rotational suppression of buoyancy reduces \(Nu\) significantly. Increasing \(\Gamma\) enhances heat transfer, although the growth rate diminishes for \(\Gamma > 1\). The relative thermal and Ekman boundary-layer thicknesses govern the sensitivity of heat transfer to rotation.

A liquid-nitrogen-cooled cryogenic gas target system has been developed and installed for radioactive ion beam (RIB) production at the Radioactive Ion Beam Line in Lanzhou (RIBLL). Light-element gases ($\mathrm{H}_2$, $\mathrm{D}_2$, and $^4\mathrm{He}$) filled in the target cell were cooled to cryogenic temperatures, with the gas-cell outlet temperature typically monitored at 82–86 K during beam irradiation and operating pressures up to 1000 mbar. The system was used to produce $^{7}\mathrm{Be}$, $^{16}\mathrm{N}$, and $^{15}\mathrm{O}$ RIBs via the $^{1}\mathrm{H}(^{7}\mathrm{Li}, ^{7}\mathrm{Be})n$, $^{2}\mathrm{H}(^{15}\mathrm{N}, ^{16}\mathrm{N})p$, and $^{1}\mathrm{H}(^{15}\mathrm{N}, ^{15}\mathrm{O})n$ inverse kinematics reactions, yielding purities of 85%, 99%, and 95%, with intensities of $1.02\times10^{6}$, $2.7\times10^{5}$, and $1.0\times10^{5}$ pps, respectively. A $^{93m}\mathrm{Mo}$ isomer beam was also produced via the $\mathrm{^4He(^{94}Zr,} 5n)^{93m}\mathrm{Mo}$ reaction, achieving an intensity of $5.38\times10^{3}$ pps and a purity of 20% (which can be further improved to $\sim$50% with offline time-of-flight gating). By delivering a broader range of high-intensity secondary RIBs, this setup establishes a robust platform at RIBLL for low- and medium-energy nuclear astrophysics and reaction studies.

The coordinated interactions between organisms enhance collective functionality, a feature that artificial systems such as enzymatic nanomotors seek to replicate. A key objective, yet still a major challenge, is to achieve chemical communication among nanomotors. Progress has been limited by the difficulties in verifying effective signaling processes, including chemical signal propagation and the response of receiving nanomotors. Here, we address this challenge using an enzymatic nanomotor system that demonstrates communication between two populations through generically non-reciprocal phoretic response. A primary swarm of glucose-responsive nanomotors migrates toward a glucose gradient while producing H2O2 as a diffusible communication signal. This self-generated chemical gradient then acts as a chemoattractant for a secondary swarm of catalase-powered nanomotors. Through carefully designed experiments, we visualize the propagating H2O2 gradient and quantify the spatiotemporal response of the receiver nanomotors to the chemical front. Combined experimental and theoretical analysis has revealed that the synergy between different combinations of chemo-attractive and chemo-repulsive mobilities and catalytic rates of consumption and production of substrates and products gives rise to a wealth of different collective responses in the system. This work represents a step toward programmable synthetic systems at the collective level, broadening the functionality of chemical nanomotors and opening opportunities for future hybrid living-synthetic systems.

The Poisson Nernst Planck PNP system constitutes a canonical stiff coupled PDE problem where the charge density prefactor produces extreme coefficient ratios and the electric double layer imposes sharp boundary layers. Physics informed neural networks PINNs are appealing here because they require no mesh and differentiate through the physics automatically. Spectral bias and multi task loss imbalance however have limited their accuracy on stiff PNP systems. We present the first systematic data free benchmark of eleven PINN configurations organised into four strategy groups on a physically parametrised one dimensional PNP model for a lithium symmetric cell implemented within NVIDIA PhysicsNeMo Sym and validated against a finite volume method FVM reference. Root mean square errors RMSE span across architectures. The balanced residual decay rate BRDR scheme matches Neural Tangent Kernel NTK performance for concentration fields while reducing mean wall clock time making it the preferable strategy under compute constraints. Loss landscape geometry corroborates the RMSE ranking. We release an open source PhysicsNeMo Sym implementation for reuse on stiff coupled PDE problems in computational mechanics.

We develop a theory of ridges in compact stellarators with quasiaxisymmetry (QA). The equilibrium with finite plasma currents and pressure is modeled by ideal magnetohydrostatics (MHS). Field lines are collimated near sharp ridges, much like X-points, making ridges attractive to divertor designs without the requirement of a rational rotational transform at the divertor. However, unlike X-points, which must cover the entire torus an integer number of times, sharp ridges are typically localized in certain parts of the flux surfaces. Motivated by recent work (Henneberg and Plunk, Phys. Rev. Research 6, L022052) on compact hybrid devices, we develop a perturbative treatment of nearly axisymmetric quasisymmetric devices by expanding in the deviation from perfect axisymmetry. As a result, we can analytically describe the key features of compact QA devices, such as the tendency for ridges to be localized on the inboard side, where the Gaussian curvature is typically negative, and the field strength is maximum. We provide comprehensive numerical evidence in support of our analytical theory.

Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data. We introduce Stein kernelized molecular dynamics (SKMD), an enhanced sampling method that uses interacting particle dynamics to acquire informative training configurations for the active learning and fine-tuning of MLIPs. SKMD corresponds to a stochastic variant of Stein variational gradient descent that is adapted for molecular dynamics by incorporating asynchronous particle updates and a kernel of global atomic descriptors, which provides a symmetry-aware measure of configurational similarity. Unlike other enhanced samplers used in molecular dynamics, SKMD preserves the Boltzmann distribution as the asymptotic distribution of the dynamics. This property enforces a balance between the exploration of diverse configurations and attraction toward high-probability regions of the energy landscape. We further propose an approach to efficient online data acquisition using an adaptive stopping criterion that selects non-redundant training data over the course of simulation. We demonstrate SKMD for the active learning of a neural network model of the Müller-Brown potential and the fine-tuning of a MACE interatomic potential for alanine dipeptide. Compared to active learning baselines, our method achieves higher model accuracy in fewer training iterations with the same number of acquired training samples.

In numerical experiments simulating Turbulent Radiative Mixing Layers (TRMLs) it is observed that as the cooling time in the mixed gas, $t_{\rm cool}$, becomes very short compared to the dynamical time of the turbulence, $t_{\rm eddy}/t_{\rm cool} \gg 1$, there is a change in the scaling behavior of the total energy radiated in the TRML as a function of this ratio, also known as the Damköhler number, ${\rm Da} \equiv t_{\rm eddy}/t_{\rm cool}$, from $\dot{E}_{\rm cool} \propto {\rm Da}^{1/2}$ to $\dot{E}_{\rm cool} \propto {\rm Da}^{1/4}$. The latter, so-called "fast-cooling," regime is of particular interest as many astrophysical mixing layers lie in this regime. We demonstrate that the origin of this change is the suppression of turbulent folding of the surface by the ram-pressure of the inflowing gas, which becomes much greater than the turbulent pressure in this regime. We present an argument that reproduces the $\dot{E}_{\rm cool} \propto {\rm Da}^{1/4}$ behavior by appealing to the suppression of the fractal structure of the interface by the ram-pressure of the inflowing gas.

Turbulent Radiative Mixing Layers (TRMLs) are of fundamental importance to the transport of energy and momentum in multi-phase, astrophysical fluids. We use measurements of the "micro" and "macro" properties of these layers in high-resolution AthenaK simulations to investigate when their properties can be considered well-resolved. In particular, we demonstrate that the previously noticed resolution independence of total cooling, $\dot{E}_{\rm cool}$, in these simulations is due to a remarkable, and perhaps fortuitous, cancellation of the countervailing effects of numerical dissipation and numerical viscosity. This calls into question the degree to which we can trust the results of these experiments, as there is no physical picture that explains this cancellation. We also demonstrate that in order to correctly resolve the phase structure in these layers, important for accurate predictions of their observable properties, one must resolve the scale on which turbulent diffusion acts on time-scales comparable to the cooling time. This "turbulent Field length", $\lambda_{\rm F,turb}$, is where the eddy turnover time is equal to the cooling time ($t_{\rm eddy}(\lambda_{\rm F,turb}) = t_{\rm cool}$). We demonstrate that resolving this scale results in converged phase-structure and spatially resolved transitions in the gas phases.

Quantum computing platforms based on arrays of tweezer-confined neutral atoms have recently emerged as a competitive modality thanks to a direct path toward high qubit count, rapidly advancing operation fidelities, and their ability to execute circuits with arbitrary qubit connectivity. These features will enable the use of efficient error correction schemes with high encoding-rates, time-efficient decoding, and resource-efficient architectures based on transversal gates. With these goals in mind, recent state of the art neutral atom demonstrations focus on the transition from the use of physical qubits to error-corrected logical qubits, but to date there has been no demonstration of repeated error correction scalable to arbitrary depth. Here, we demonstrate many cycles of syndrome extraction in a toric quantum error correcting code, using mid-circuit measurement and replacement of lost qubits, including reloading of a qubit reservoir for indefinite coherent operation. We characterize the logical error rate after up to 90 cycles, showing that logical information can be preserved through multiple rounds of qubit reloading. Comparing two distances of the code up to 8 rounds of syndrome extraction shows a lower absolute logical error rate for the larger distance code.

Charge transfer (CT) is governed by complex multiscale dynamics sensitive to environmental factors. In molecules, charge state and vibrational effects shape energy levels, charge distribution, and reactivity, thereby controlling CT efficiency. Quantifying these contributions in CT is experimentally challenging, as vibrational effects remain difficult to isolate due to limits in precision, sensitivity, and stability. In such context, the original Marcus theory, often lacks the refinement required to accurately capture CT rates in complex environments, necessitating new approaches that incorporate vibronic effects. Here, we examine a non-covalent H2TPP dyad on a semi-insulating CaF2/Si(100) surface. Using a low-temperature (9 K) scanning tunneling microscope (STM), we generate tunneling electrons that trigger CT events by creating locally cationic states while activating transient vibronic modes at specific molecular sites in one fragment. This initiates a meso N tautomerization serving as a CT signature in the second fragment. By tuning the tunnel electron energy, CT rates measurements reveal a periodic modulation. Quantitative analysis with the Marcus-Levich-Jortner model identifies key reorganization energies and resonant vibronic modes, while DFT provides complementary conformational and vibrational insights. Extending the Marcus-Antoniewicz models to explicit reaction coordinates reveals that CT is governed by the interplay between electronic and vibronic contributions, establishing surface-supported systems as a model framework.

We explore an extension to Newtonian gravity through a generalised Lagrangian function with the introduction of a second dynamical scalar field. Building on previous research into gravity with variable gravitational coupling, the work derives the complete field equations and applies a weak-field approximation. This leads to an effective post-Newtonian gravitational potential that includes key aspects of relativistic theories. The resulting N-body equations of motion highlight differences among inertial and gravitational masses, which can constrain the theory's free parameter through data from the Nordtvedt effect. By employing the method of osculating orbits for a two-body system, the study calculates the secular variation of the orbital pericenter and aligns this with the latest data on Mercury's perihelion shift, for another observational constraint on the model. Furthermore, a few examples of theories are discussed.

This study presents a unified nonlinear dynamical framework for understanding, modelling, and teaching competitive judo. The Tori Uke Dyad is formalised as a constrained multibody system whose behaviour emerges from symmetry breaking, coupling dynamics, and transitions between attractor basins. Two fundamental instability archetypes rotational collapse Uchi mata type and gravitational lever collapse Seoi otoshi, suwari version are identified as the core pathways through which all throwing techniques evolve. A Functional Instability Index It is introduced as a dimensionless order parameter. Derived from local bifurcation analysis, it integrates geometric, dynamic, and coupling related variables through a multiplicative nonlinear structure, enabling the quantification of critical transitions such as Kuzushi, Tsukuri, and Kake. Fractional Brownian Motion models the global displacement of the Dyad, where the local Hölder exponent encodes the informational structure of the interaction. An AI based pipeline extracts instability signatures from high frequency competition video, providing objective measures such as finite time Lyapunov exponents, attractor topology, and coupling stiffness. Building on these principles, a three level teaching framework is proposed, shifting judo pedagogy from a technique centred to an instability centred approach.This study establishes the first theoretical foundations for a predictive science of judo performance and outlines future directions for empirical validation, athlete monitoring, injury risk modelling,cross sport applications and judo as neurological rehabilitative tool for Parkinson diseases.