Papers

Recently, the wave-particle turbulence simulation (WPTS) has been proposed as a novel framework for non-equilibrium turbulence modeling and simulation. In this work, for the first time the complete model equations of WPTS are explicitly derived from the perspective of wave-particle decomposition, and the physical mechanism of each term is clearly interpreted. To extend its applicability to wall-bounded flows, the WPTS coupled with wall model is developed, and the introduction of wall model substantially alleviates the near-wall grid-resolution constraint. In the bulk region, the wave component resolves the large-scale structures, whereas the particle component accounts for subgrid-scale modeling through the non-equilibrium transport mechanism. As a result, the coupled method enables accurate predictions of the flat-plate transition on coarse-grid. In particular, the computed skin-friction coefficient and mean velocity profiles in the fully turbulent region agree well with the reference data from direct numerical simulation, and the accuracy is markedly superior to that of the gas-kinetic scheme (GKS) under the identical grid. These findings underscore the considerable promise of the multi-scale WPTS method for transitional flow simulations.

Network immunization is a powerful tool for controlling contagion processes ranging from infectious diseases to misinformation diffusion. While prior works have focused on pairwise or static networks, immunization dynamics in temporal higher-order networks remain poorly understood. Here, we introduce immunization strategies and develop a theoretical framework tailored for such temporal systems. Firstly, we reveal bistability and discontinuous transitions in prevalence as the immunization fraction varies. This implies that immunization effectiveness depends on the initial prevalence, marking a fundamental departure from pairwise networks. Building on this prevalence-dependent behavior, we propose the High Infection Contribution (HIC) strategy, demonstrating its superior performance over all evaluated heuristic strategies. Furthermore, we introduce egocentric strategies by leveraging solely local observations. Notably, the optimal egocentric strategy shifts with the contagion prevalence. Our work advances the understanding of network immunization, paving the way for effective contagion control in temporal higher-order networks.

Surface tension is central to many two-phase flows, making accurate numerical schemes essential for predicting its effects. The integral formulation introduced by Popinet and Zaleski (1999) provides a natural discretisation that conserves momentum locally and globally and extends directly to variable surface tension, including Marangoni flows. However, to the authors' knowledge, only two-dimensional formulations have been reported, mainly because robust implementation in three dimensions is challenging for interfaces with complex geometries. This work presents the first three-dimensional integral surface tension scheme, implemented within a sharp front-tracking framework. The method is tested for static and translating spherical droplets, oscillating droplets, thermocapillary motion, and rising bubbles. Results are compared with analytical solutions, experimental data, and established approaches, including the continuous surface force (CSF) and smoothing-based methods. The proposed scheme produces spurious velocities comparable to CSF, while providing greater accuracy in all other tests. The largest improvements occur for droplets oscillating at low Ohnesorge numbers, variable-surface-tension flows, and strongly deforming rising bubbles. For a thermocapillary-driven droplet, terminal-velocity errors are reduced by up to five orders of magnitude relative to smoothing-based methods. The predicted steady-state shapes of rising bubbles also agree substantially better with experiments, particularly at low Morton numbers.

We examine anisotropy and spatial intermittency at small scales in drift-wave turbulence with zonal flows. We use a two-dimensional directional continuous wavelet transform, which allows simultaneous localization in scale, position, and direction. This wavelet analysis is applied to vorticity fields obtained from numerical simulations of the modified Hasegawa–Wakatani model, a reduced model of resistive drift-wave turbulence in magnetized plasmas with zonal flows. Directional wavelet statistics characterize the anisotropy of the turbulence. The second-order moment is enhanced around directions perpendicular to the zonal flow. Spatial intermittency, characterized by scale-dependent flatness, is more pronounced around directions along the zonal flow.

Dark modes arise when destructive interference prevents selected bosonic degrees of freedom from coupling to environmental channels. We formulate a Hamiltonian criterion for identifying such modes in multimode bosonic systems by separating two requirements: the candidate mode must be invisible to the direct system–environment coupling, and its generated operator space must remain invariant under the intrinsic system dynamics. For linear environment coupling and quadratic system Hamiltonians, the criterion is reduced to the the familiar null-space and invariant-subspace conditions of passive linear dark-mode theory. We then extend the analysis to nonlinear scenarios. For a two-photon conversion channel coupled to an auxiliary environmental mode, interference among nonlinear conversion pathways can reduce the environment coupling to a single collective two-photon channel, leaving a complementary bosonic mode decoupled from the environment. We show that preserving this mode under nonlinear intrinsic dynamics generally requires more than conventional Kerr-type quartic interactions: correlated four-boson conversion processes are needed to cancel mixed nonlinear conversion between the dark and environment-coupled collective modes. Finally, we show that the Bogoliubov dark mode of a parametrically driven optomechanical satisfies the same Hamiltonian criterion through an active canonical transformation. These results provide a unified Hamiltonian framework for identifying and engineering dark modes in linear, nonlinear, and driven bosonic systems.

We propose a mechanism for the generation of intense ultrafast mid-infrared radiation from heavily doped semiconductors. An ultrafast optical pulse transfers a finite impulse to the free carriers, displacing the screening clouds surrounding ionized dopants and inducing coherent plasma-frequency oscillations of the resulting polarization. We derive an exact analytical solution for the corresponding nonequilibrium dynamics and the resulting far-field radiation emitted by a thin semiconductor film. For realistic parameters of heavily doped GaAs, the emitted mid-infrared pulses can reach electric-field amplitudes on the order of $10^7\,{\rm V/m}$ directly in the far field, without relying on optical focusing.

Modelling rarefied gas flow using the Boltzmann equation is vital in many areas. Because of the high dimensionality and coexistence of multiple characteristic scales, conventional solution strategies for this equation incur prohibitively high computational costs and are inadequate for rapid response in engineering design simulations. Based on proper generalised decomposition (PGD), we propose an a priori, asymptotic-preserving reduced-order method for the high-dimensional parameterised Shakhov kinetic model equation. Separated representations reduce the kinetic problem to a set of low-dimensional problems. We incorporate synthetic equations into the PGD iteration so that, in the hydrodynamic limit, the iteration reduces to solving the Navier-Stokes equations, whose solution naturally exhibits a low-rank structure. Furthermore, treating the rarefaction parameter as an additional coordinate yields a solution over the whole range of rarefaction in one offline computation. It enables fast online queries to any point in the parameter space. Numerical examples using this method show reasonable precision and a significant reduction in computational cost.

Stochastic simulations can be generated from deterministic reaction-diffusion equations by discretising in space and time and interpreting coefficients in the resulting system of discretised equations as probabilities governing movement and reaction events. In this paper, we present a novel variant of this approach for nonlinear reaction-diffusion equations that employs an exponential integrator when discretising in time. The proposed method yields valid probabilities, defined by the entries of appropriate matrix functions, without the strict conditions on the time step required by a commonly-employed time discretisation scheme. Simulation results presented for one and two dimensional Porous-Fisher type models demonstrate the veracity of the method across several test problems.

Power-law spectra are a central prediction of shock acceleration and are commonly associated with asymptotic scale invariance under diffusive transport. In finite relativistic shocks, strong anisotropy and limited residence times may restrict the number of effective shock crossings before the many-cycle diffusive limit is established. This work develops a reduced finite-cycle framework in which particle energization is described by discrete shock-crossing mappings, while downstream transport is encoded through an energy-dependent return probability. In this formulation, the local spectrum is controlled by the competition between the mean energy gain per cycle and the probability of surviving to the next cycle. A systematic decrease of the return probability with energy then produces intrinsic spectral curvature as a consequence of transport-limited cycle survival. The energy dependence of the return probability is estimated from the competition between magnetic deflection, downstream advection, and finite shock lifetime, yielding a characteristic steepening scale determined by macroscopic source parameters. For fiducial parameters relevant to compact blazar emission regions, the steepening scale lies below the ultimate acceleration cutoff, so that curvature can appear before the terminal maximum energy is reached. These results point to a pre-asymptotic finite-cycle limit of relativistic shock transport in which non-power-law spectra can arise from the limited survival of repeated shock-crossing cycles.

We develop an entropy-based formulation of gravitational focusing in Lorentzian geometry by associating a scalar entropy functional to timelike geodesic congruences. Rewriting the Raychaudhuri equation as an entropy production law, we show that entropy accumulation governs the evolution of the expansion scalar and drives finite-time focusing. We prove that when the integrated entropy exceeds a critical threshold set by the initial expansion, geodesic incompleteness necessarily follows. This result provides a quantitative refinement of the classical singularity theorems, replacing qualitative geometric conditions with an explicit entropy criterion. Our approach offers a thermodynamic interpretation of gravitational collapse and suggests that singularities arise as endpoints of irreversible entropy growth in spacetime.

A general expectation in quantum gravity is that quantum properties of spacetime manifest themselves as fuzziness, causing an irreducible uncertainty in the measurement of spacetime-related observables. In this work, we derive the time-of-flight fuzziness for free particles within a noncommutative spacetime model with $\kappa$-Poincaré deformed relativistic symmetries. To retain the full quantum structure of the theory we work in the corresponding noncommutative space of worldlines. This provides a convenient framework for constructing quantum states and deriving the probability distributions for the parameters characterizing particle trajectories, allowing us to obtain the probability distribution of particle times of flight. Our results reproduce the well-known systematic time-of-flight correction, leading to energy-dependent departures from the special-relativistic expectation, and simultaneously predict a novel stochastic contribution to the time of flight. While the systematic contribution scales with the ratio between the particle energy and the quantum-gravity energy scale and is amplified by the propagation distance, the stochastic contribution scales as the square root of the product of the quantum-gravity length scale and the travel time, multiplied by a prefactor which depends on the particle velocity.

The current tension between early and late-Universe determinations of the Hubble constant motivates the development of independent high-redshift distance tracers. Quasars, as luminous active galactic nuclei - AGNs, provide a promising route through the empirical relation between their X-ray and ultraviolet luminosities.We construct a joint Type Ia supernova-quasar Hubble diagram and investigate whether a statistically selected quasar subsample can define a preliminary golden sample for future AGN standardization. We combine calibrated Type Ia supernovae with SDSS DR14 quasars and infer the cosmological parameters from the posterior distribution of a joint model. Posterior medians and credible intervals are obtained from the marginalized distributions. We further define redshift-dependent quasar pools from SDSS queries and select the most representative sources in each bin using a multivariate statistical-distance criterion based on redshift, optical magnitudes, and SDSS colors. The posterior analysis yields $H_0 = 71.76^{+1.67}_{-1.31}\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$ and $\Omega_m = 0.294^{+0.014}_{-0.012}$. The statistically selected quasar candidates reproduce the reference Hubble diagram with small residual dispersion across multiple redshift intervals and remain broadly distributed across the SDSS angular footprint without evidence of strong artificial clustering induced by the selection procedure.

Hydrogen cyanide (HCN) is a key molecule in prebiotic chemistry, and its availability in water is limited by hydrolysis. In this work, we estimate the hydrolysis rates of \ce{HCN}, particularly the errors associated with the rates as a function of temperature, pH and in the presence of salts containing sulfite, sulfide and phosphate. For pure water, we find an acid-catalyzed hydrolysis rate constant at ${\rm 0 ^{\circ}C}$ of $\ln (k^+_{273}/1\,{\rm M^{-1} s^{-1}}) = -12.7 \pm 1.12$ with an activation energy of $67.1 \, {\rm kJ \, mol^{-1}}$. We find a base-catalyzed rate constant of $\ln (k_{273}^-/1\,{\rm M^{-1} s^{-1}}) = -9.0 \pm 1.27$ at ${\rm 0^{\circ}C}$ with an activation energy of $89.5 \, {\rm kJ \, mol^{-1}}$. These values are consistent with estimates from the literature within our uncertainties at ${\rm pH} > 8$ but diverge from literature values at lower pH. In the presence of salts, hydrolysis is accelerated under acidic conditions to 6–14$\times$ the acid-catalyzed rate without salts. However, at ${\rm pH \gtrsim 8}$ hydrolysis rates become $2.5\times$ times slower with sulfite and sulfide. These results demonstrate the importance of estimating uncertainties associated with rates when constraining the maximum concentrations of prebiotic molecules attainable in natural waters on the Earth and other planets.

We draw a structural analogy between quantum error correction (QEC) and error handling in neural circuits with respect to their redundant encodings and constraint-based inferences. In QEC, logical information is embedded in a protected codespace within a larger Hilbert space. A set of commuting checks (e.g. stabilizer constraints) is repeatedly evaluated to produce an error syndrome that identifies which constraints were violated without directly revealing the logical state. A decoder then maps the syndrome to a recovery operation that returns the system to the codespace and suppresses logical failure below a threshold. Neural circuits exhibit error-control strategies that can be viewed through a related biological error correction (BEC) pattern: information is distributed across multiple neurons (redundant encoding), yielding reliable collective activity from error-prone unit operations of individual neurons. The structural analogy with QEC raises the question whether collective activity may be constrained on lower-dimensional manifolds (a biological codespace), allowing recurrent circuit dynamics and mismatch signals to function as syndrome-like indicators of constraint violations, driving fast corrective dynamics and slower adaptive updates. Our structural analogy also suggests that new insights into brain-inspired algorithms for collective information processing may inform novel QEC approaches. We perform numerical experiments using simplified models of qubit and neuron dynamics to illustrate the analogy.

We have experimentally realized a parabolic potential barrier for surface gravity water waves. The analogy between the resulting wave equation and the Schrodinger equation for the inverted harmonic oscillator (IHO) enables us to study the propagation of quantum-mechanical wave packets with different average energies in this iconic scattering model. We observe a clear boundary in the phase-space dynamics, namely the separatrix, which distinguishes wave packets with energies below the maximum of the IHO potential from those with energies above it. In the former case, the wave packet is blocked, whereas in the latter case, it is transmitted. We also measure the corresponding variation in momentum during this process.

Particle charts are useful teaching tools, but the usual quark-model multiplet diagrams emphasize symmetry labels more than mass ordering. This paper presents a compact “scarab chart” for light hadrons that keeps the familiar isospin and hypercharge information while adding a mass-sensitive ordering. The chart is intended for modern physics or introductory particle physics teaching. Each tile represents a light meson or baryon, with columns labelled by isospin projection, rows ordered by an integer pion-anchored mass count, tile colour identifying hypercharge, and border colour corresponding to the pseudoscalar nonet, vector-meson nonet, baryon octet, or baryon decuplet. The chart is proposed as a pedagogical visualization. The result gives students a single visual diagram in which quark content, multiplet, strangeness, and approximate mass scale can be compared.

Atom interferometry is a sensitive tool for measuring relativistic effects, but there are no known non-trivial exact solutions for relativistic atom interferometer phase shifts. Here we derive relativistically exact expressions within the usual semiclassical approximation for a wide range of experimentally interesting atom interferometer pulse sequences in flat spacetime, including Mach-Zehnder, resonant, and large momentum transfer interferometer geometries. As an example, the leading order phase shift $\omega_a g T^2/c$ for a Mach-Zehnder clock atom interferometer is found to become $\omega_a \left(1 + \frac{\omega_a}{2m}\right)(e^{-gT/c}-1)^2 c/g$ when all relativistic kinematics are included. We calculate exact phase shifts for both clock (single-photon) interferometers and Raman or Bragg (two-photon) interferometers.

Machine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing. As ML systems grow increasingly autonomous, ensuring their reliability for discovery claims becomes critical. This review synthesizes the VERaiPHY (Validation & Evaluation for Robust AI in PHYsics) initiative's frameworks for rigorous ML assessment across particle physics, astrophysics, and cosmology. We establish when verification is essential by contextualizing ML within the statistical discovery workflow. We emphasize fundamental limitations: inductive bias is unavoidable, sample complexity bounds learning, and experimental constraints limit discovery. We reflect on physicists' evolving role as both experimental designers and evaluators whose judgments encode scientific rigor into AI systems. Responsible integration requires understanding ML's transformative potential alongside its intrinsic boundaries.

Identifying the causes of Earth's extremes is challenging because counterfactual experiments are not possible in the observed world. Data-driven causal discovery complements computationally expensive and potentially biased numerical experiments, but existing methods can struggle with undersampled, high-dimensional data and fail to recover multi-timestep, multivariate pathways leading to specific events. We introduce Tracer of Causal Evolutions in Space and Time (TraCE-ST), a probabilistic Lagrangian approach that produces event-conditioned causal trajectories in multivariate gridded data. TraCE-ST recovers known causal drivers and estimates their relative contributions in synthetic experiments and real-world extremes, including the 1991 Mount Pinatubo eruption. TraCE-ST also highlights less-studied drivers, such as orography-driven vorticity for Tropical Storm Debby (2006) and anomalous ocean-surface fluxes for the 2021 Pacific Northwest heatwave. Here, we propose causal tracking as an efficient data-driven framework for synthesizing causal evidence and generating testable hypotheses, complementing association analyses and numerical modeling while accelerating the study of high-impact events.

This paper presents a maximally flat-top half-band FIR optical filter on a silicon nitride (SiN) platform at 193 THz. Using a cascaded Mach-Zehnder Interferometer (MZI) topology simulated in Lumerical INTERCONNECT, MODE, and FDTD, the filter achieves insertion losses of 0.1349 dB and 0.1761 dB with extinction ratios of 18.317 dB and 23.002 dB for Channel-A and Channel-B, respectively, under realistic S-parameter conditions.

Turbulent bluff-body wakes exemplify the coexistence of large-scale coherent structures and fine-scale turbulence – two ends of a wide dynamical range of scales connected through the turbulent cascade. In this work, we study the multiscale dynamics in the high-Reynolds-number wake behind a circular disk. One-point and two-point statistics are first examined, including the budget and spectra of the turbulent kinetic energy (TKE). Streamwise advection is found to contribute the most to the TKE balance, while the dissipation rate does not follow the classical equilibrium scaling ($\varepsilon \nsim \mathcal{U}^3/\mathcal{L}$). The largest scales are represented by the three-dimensional coherent modes extracted using spectral proper orthogonal decomposition, whereas the TKE and Reynolds shear stress spectra exhibit inertial-range scalings. A filtering-based triple decomposition further separates the fluctuations into large- and small-scale components and partitions the kinetic energy, with respective spatial transports at each scale and an inter-scale transfer in between. The inter-scale fluxes indicate a statistical forward cascade and follow the classical $\mathcal{U}^3/\mathcal{L}$ scaling, while their radial profiles become self-similar. The disequilibrium between inter-scale flux and dissipation is shown to arise from non-negligible streamwise advection at the sub-filter scale. Finally, the observed anti-correlation between the dissipation coefficient and the local Taylor Reynolds number, $C_\varepsilon = \varepsilon \mathcal{L}/\mathcal{U}^3 \sim Re_\lambda^{-1}$, is shown to originate from a similar correlation in the coarse-grained, locally averaged statistics. The results suggest that the instantaneous cascade-dissipation disequilibrium is intrinsic to turbulence and becomes apparent when large-scale unsteadiness and length-scale growth prevent statistical equilibrium.

Photonic-crystal microresonators (PhCRs) are a powerful platform for generating Kerr frequency combs. Because Kerr-soliton dynamics in PhCRs are largely decoupled from the operating wavelength, the comb output can be engineered through customization of the device layer. Here, we demonstrate a tantalum pentoxide (tantala) PhCR platform that supports 1310 nm and 1550 nm band operation, and we explore high-efficiency O-band soliton microcombs with all-semiconductor laser pumps. We engineer the PhCRs with silicon dioxide cladding and normal dispersion with intrinsic quality factors exceeding $7\times10^{6}$. By pumping bandgap modes, we obtain robust and efficient soliton comb formation at a 200 GHz mode spacing. Our PhCRs enable systematic tuning from narrowband to broadband comb states within a single device geometry. The combs exhibit low relative intensity noise approaching the shot-noise limit, indicating stable phase-matching in the PhCR. Using a second resonator coupler, we amplify the comb output off-chip, demonstrating a pathway to high-power O-band sources. These results establish PhCR engineering in the tantala platform as a scalable approach to wavelength-agile, low-noise microcombs for applications in communications, sensing, and signaling.

We investigate the magnetic-field response of interstitial anionic electrons (IAEs) in two-dimensional electrides, using monolayer Ca$_2$N as a prototypical system. By computing the Landau-level (LL) spectrum of the electride bands forming the Fermi surface, we find a linear LL evolution with magnetic field that closely resembles the behavior of a nearly-free 2D electron gas (2DEG). The extracted cyclotron effective mass and Landé g-factor deviate moderately from their free-electron values, indicating that the IAEs retain a remarkably free-electron-like character. Furthermore, the energy dispersion of the electride bands remains insensitive to the choice of exchange-correlation functional (LDA vs.~PBEsol), indicating that local exchange and correlation effects have minimal influence on the IAEs. Overall, our findings provide fundamental insight into the quantum nature of electrides and open new avenues for exploring magnetic confinement, correlation effects, and emergent quantum phenomena in low-dimensional interstitial electronic systems.

Purpose: Fully optical receive coils can potentially allow dense receiver arrays with a large channel count, reduced channel crosstalk, and less cable clutter. The power requirements of conventional low-noise amplifiers (LNAs) are prohibitive for simultaneously driving many coils through optical means, as opto-electric power conversion efficiencies can only reach about 50%. The goal is to develop low-power LNAs (LPLNA) with substantially lower power consumption without compromising noise figure (NF) and gain. Methods: A LPLNA was designed as a two-stage cascaded amplifier using an MR-compatible E-pHEMT (Enhancement-mode Pseudomorphic High Electron Mobility Transistor) transistor. The design was implemented on a single-sided printed circuit board (PCB), and its performance was compared with a commercial LNA. A four-channel shielded loop resonator array was constructed, and the signal-to-noise ratio (SNR), noise covariance, and preamplifier decoupling performance were evaluated. Results: The LPLNA had a five-fold lower electrical power consumption (40 mW) than the commercial LNA and provided comparable SNR in phantom measurements. In vivo experiments further confirmed that the LPLNA operates reliably under realistic MRI conditions. Additionally, four-channel receiver array measurements demonstrated comparable SNR within 2% of the commercial LNA and lower inter-channel noise correlation with 0.26 vs 0.3 on average. Conclusion: This study demonstrates the feasibility of LPLNAs for optically-powered RF receiver coil arrays. The LPLNA could also be applied in power-constrained or remote MRI environments.

The interplay between nonlinear optical transitions and topological band structure offers a route to control photocurrents. We reveal a fourth-order optoelectronic response that emerges due to an interlink between the circular photogalvanic effect (CPGE) and the Berry curvature dipole (BCD) in noncentrosymmetric 2D materials. Using monolayer $\Td$-WTe$_2$ as a prototype, we predict that circularly polarized mid-infrared light produces a steady dc injection current that induces an internal electric field, which in turn drives a transverse nonlinear Hall response through BCD. The resulting cascaded photovoltage scales as the fourth power of the optical field $E_0^4$. By mapping the full injection current tensor, we show that this cascaded voltage is strongly tunable by the optical geometry: normal incidence drives an in-plane resonance $\mathrm{Im}(\eta_{yxy})$, whereas oblique illumination ($\theta = 45^{\circ}$) recruits a dominant out-of-plane component $\mathrm{Im}(\eta_{yyz})$ and amplifies the signal by more than two orders of magnitude (${\sim}10^2~\mu$V). While the massive linear Drude background typically screens nonlinear responses in semimetals, we argue that the amplitude modulation of the optical pump allows lock-in detection to cleanly isolate the frequency-doubled cascaded response. The proposed mechanism converts mid-infrared light into a gate-tunable transverse signal, providing a route for probing quantum geometry and realizing topological photodetectors and frequency doublers.

Non-fullerene acceptors such as Y6 have enabled high-efficiency organic photovoltaic devices and motivated the development of single-component architectures; however, the microscopic mechanisms governing exciton transport and charge dissociation remain under active investigation. In particular, the interplay between Frenkel-charge-transfer excitations and their coupling to environmental fluctuations complicates the description of light absorption and subsequent exciton dynamics. Here, ultrafast transient absorption spectroscopy is used to probe exciton quenching dynamics in Y6 films interfaced with hole-transport layers. To interpret these measurements, we develop an analytical model based on hybrid Frenkel-charge-transfer states that enables direct extraction of intermolecular electronic couplings, charge-transfer character, and system-bath interaction strengths from experimental data. The analysis reveals a substantial charge-transfer admixture of 20-40% in the exciton states and identifies a transport regime characterized by delocalization-mediated exciton motion rather than purely diffusive hopping. Consistent with this interpretation, the corresponding quenching dynamics occur on a ~1 ps timescale within ~4 nm of the interface, suggesting a short-range injection mechanism facilitated by exciton delocalization. In addition to providing physical parameters for Y6, these results establish a quantitative framework that connects spectroscopic observables to microscopic transport mechanisms and can be generalized to other non-fullerene acceptors.

Ground and excited electronic states in highly symmetric systems typically possess high degrees of spatial degeneracy as a consequence of point-group symmetry. However, many current quantum-chemical methods struggle to accurately describe the strong correlation effects inherently present in these states, thereby precluding the ability to obtain meaningful insights into the electronic structure of the underlying systems. Consequently, many of their important chemical and spectroscopic properties cannot be reliably computed and predicted. In this article, a new theoretical framework is described that unifies the symbolic treatment of non-Abelian symmetry in QSym$^2$ and the recently developed state-specific multi-reference coupled cluster theory termed Generalised Normal Ordered Coupled Cluster (GNOCC) to describe such difficult ground and excited states in a balanced and targeted manner. This is ensured by the ability of QSym$^2$ to exploit symmetry orbits to restore any broken spatial symmetries and generate symmetry-adapted multi-determinantal wavefunctions, as well as the ability of GNOCC to dynamically correlate arbitrary spin eigenfunctions in a size-extensive and spin-free manner. To illustrate the capabilities of this framework, several ground and excited states in three model systems are examined in detail: (i) octahedral $(\textrm{H}_6)^{2+}$, (ii) octahedral $\textrm{H}_6$, and (iii) tetrahedral $\textrm{Li}_4$. The results demonstrate that the proposed method can target both degenerate and non-degenerate states, while delivering improved numerical performance relative to conventional single-reference coupled-cluster approaches.

We present a fully differentiable far-field transform in cylindrical coordinates for full-area point spread function (PSF) evaluation and optimization of large axisymmetric metalenses. The method computes wave-optical responses of apertures spanning thousands to tens of thousands of wavelengths in diameter (millimeter scales in the visible, centimeter scales in the infrared) in seconds, achieving three to four orders of magnitude speedup over Green's function integration while avoiding the prohibitive memory of two-dimensional FFTs. The approach decomposes vectorial near fields into parallel angular-momentum channels, applies FFTLog-accelerated Hankel transforms, and uses Graf's addition theorem to recenter focal fields under oblique illumination. Analytic adjoint gradients enable optimization with only ~65% overhead relative to a forward simulation. For a 4 mm-diameter aperture (~8000 wavelengths, ~12,600 azimuthal modes) at 30-degree incidence, a forward-adjoint iteration requires only ~12 s on a 350-thread CPU, making oblique optimization practical without ray-tracing approximations. Applied to polychromatic RGB (446/530/650 nm) metalens design at normal incidence, full-area PSF evaluation exposes efficiency limits hidden by conventional cropped-focal-spot analysis: a mono-pillar metalens that appears diffraction-limited achieves only ~6% average absolute focusing efficiency, while direct far-field optimization raises this to 37% (locally periodic approximation) and 51% (zoned discrete axisymmetry). A cascaded double-metasurface design reaches 63%, while a four-metasurface architecture attains 96% average relative efficiency. We also demonstrate millimeter-scale, oblique-incidence optimization of single-surface and doublet architectures; cascaded doublets enable partial coma correction inaccessible to a single rotationally symmetric surface.

Here, we present a platform built on our inverted Graph Transformer Network, IMPRESSION-G2, which can accurately and rapidly reconstruct molecular bonding directly from experimental nuclear magnetic resonance (NMR) spectroscopic information. It comprises three interconnected stages: a one-shot model that predicts bond connectivity between atoms; a structure-correction stage that corrects the predicted structures by removing uncertain bonds and iteratively reassigning them; noise-augmented multi-shot prediction, generating an ensemble of candidate structures, which are ranked to identify the best-fit structure. By integrating a range of $^{1}$H and $^{13}$C NMR data, including two-dimensional (2D) experiments such as COSY, HSQC, and HMBC, the inverse-IMPRESSION platform correctly identifies the structures of 77.8% of molecules with up to 30 heavy atoms (H, C, N, O and F) using simulated NMR data, and 10 of 19 (53%) molecules using experimental NMR data. The experimental structures solved have molecular weights of up to 480 Da and are representative of the complex structures in synthetic and natural products that routinely challenge chemists. The inverse-IMPRESSION framework thus provides the first effective approach for automated molecular structure elucidation using graph-based machine learning on experimental data.

Cardiac amyloidosis (CA) is increasingly recognized but remains substantially underdiagnosed because its clinical and imaging phenotype overlaps with more common cardiomyopathies. Definitive subtype assignment and management further require integration of multimodal evidence to distinguish transthyretin from light-chain disease. Machine learning and deep learning have been applied across the diagnostic and management pathway. These applications span electrocardiography (ECG), echocardiography, and health record-based case finding, as well as cardiac magnetic resonance (CMR) and nuclear interpretation, including single-photon emission computed tomography/computed tomography (SPECT/CT) biomarker quantification, prognostic modeling, and treatment response assessment. This narrative review synthesizes these studies by clinical tasks, namely screening, detection, quantification, prognosis, and longitudinal assessment after treatment initiation, rather than by input modality. This task-based organization clarifies why apparently similar AI models require different cohorts, reference standards, evaluation metrics, and implementation thresholds. The evidence reveals a maturity gradient. Binary detection and AI-assisted interpretation of cardiac scintigraphy with bone-avid tracers and SPECT/CT currently represent one of the more mature AI applications in cardiac amyloidosis, supporting standardized image interpretation and quantitative biomarker extraction. However, patient-level diagnosis still requires integration with monoclonal protein testing, SPECT/CT localization, clinical context, and biopsy or tissue typing when indicated.