Papers

Predicting the dynamic polarizability tensor of organic molecules is essential for simulating light-matter interactions in optoelectronic devices, yet conventional quantum-chemical methods such as time-dependent density functional theory (TD-DFT) are computationally expensive and limit large-scale screening. We present an equivariant graph neural network architecture that predicts frequency-dependent, complex-valued dynamic polarizability tensors directly from readily available molecular information, such as 3D geometry and UV-vis spectra. The model learns to approximate the shape and magnitude of the dynamic polarizability tensor, capturing both dispersive and absorptive features that define the molecular optical response. We evaluate the model on molecules from both the QM9 dataset and the Harvard organic photovoltaic dataset using various metrics, including the earth mover's distance, which is suitable for comparing spectral quantities. For both datasets, the model successfully reproduces the main features of the dynamic polarizability tensor and generalizes across diverse molecules. To assess the applicability, we further validated the model using a downstream workflow to calculate the optical properties of organic photovoltaic devices. We used the model-predicted polarizabilities to perform device-level optical simulations, yielding charge carrier generation rates in agreement with TD-DFT-based results ($R^2 = 0.94$). Our approach offers a promising complement, or even an alternative, to conventional methods such as TD-DFT, enabling faster and more efficient screening of photoactive organic molecules for optoelectronic applications.

Poly(1,3-dioxolane) (pDXL) is a chemically recyclable polyether that can be synthesized by living cationic ring-opening polymerization with precise control of chain length into the ultra-high-molecular-weight (UHMW) regime, where it acquires enhanced mechanical properties. Like most polymers, however, it has amorphous condensed-phase structure, and diffraction measurements alone cannot resolve the atomistic microstructure that underlies them. In this work, we predict the ensemble of the pDXL microstructures by applying a recently developed AI-accelerated ab initio bottom-up polymer structure prediction (AI$^{2}$-BPSP) framework that simulates the living polymerization of pDXL under experimental synthetic conditions with machine-learning force fields trained within van der Waals-corrected hybrid density functional theory. Partitioning the predicted X-ray and neutron diffraction signal between the growing chain and the surrounding monomer resolves its dominant feature into two counteracting modes: a monomer contribution near $q \approx 1.5$ Å$^{-1}$ that decays as monomer is consumed, and a polymer contribution near $q \approx 1.6$ Å$^{-1}$ that is absent in oligomers and grows with chain length. This work paves the way to direct accuracy validation of these statistically significant, first-principles, and chain-length resolved diffraction predictions against experiments.

In a standard two-conductor microwave transmission line, both the signal trace and the wide ground plane act as parasitic heat leakage paths along the propagation direction. To prevent this heat leakage, we devised a single-conductor microwave interconnect: the ground plane is removed entirely and the remaining signal trace is periodically fragmented into physically isolated metallic islands. We show that despite the galvanic isolation between adjacent metal islands, a surface confined mode of specific symmetry, the odd-symmetric spoof surface plasmon polariton, propagates through this periodic, single-conductor medium. This odd symmetry imposes an electromagnetic field null at the waveguide centerline, allowing complete removal of the metal at periodic intervals along the single-conductor waveguide without impacting EM propagation. Measurements on fabricated prototypes confirm the transmission response despite the physical fragmentation of the conductor, with de-embedded propagation loss of ~0.0031 dB/mm across a 5-6.5 GHz passband. Time-domain characterization verifies 1 Gbps amplitude-modulated data transmission with less than 145 ps group delay variation. Coupled electro-thermal simulation yields an effective thermal conductivity of 0.336 W/m*K, a 40x reduction relative to a continuous microstrip line of identical footprint.

Polarized neutron scattering has been widely used for studying magnetic orders in condensed matter. A typical setup for this technique is a triple-axis spectrometer with a ferromagnetic single-crystal monochromator and analyzer, which is suited for point-by-point measurements within the horizontal scattering plane. However, this setup cannot fully meet the demands of recent studies on complex magnetic orders with emergent cross-correlated phenomena. For instance, multiferroics, magnetic skyrmions, and other topologically nontrivial magnetic orders tend to have incommensurate magnetic modulation vectors running along various directions in reciprocal space, which cannot be captured by measurements restricted to the horizontal scattering plane. To overcome these limitations, we have developed an experimental setup combining a $^3$He neutron spin filter ($^3$He-NSF) and a two-dimensional (2D) detector, which enables us to analyze the polarization of neutrons scattered away from the horizontal plane. We also establish a data reduction procedure to convert the 2D intensity maps into three-dimensional intensity distributions in reciprocal space, correcting for the effect of imperfect $^3$He polarization. This method is applied to single-crystal diffraction measurements on the multiferroic material Mn$_3$WO$_6$, which exhibits complex incommensurate magnetic orders. We have successfully observed polarization-dependent intensities of out-of-plane incommensurate magnetic reflections, demonstrating the effectiveness of the present method for analyzing complex magnetic structures.

REBCO coated conductors have been used in ultrahigh field magnets for condensed matter physics research, nuclear fusion, particle accelerators, and NMR applications. It is well known, however, that they are not mechanically strong against delamination due to their intrinsic layered structures. As a result, the delamination issue has become one of the major design challenges of REBCO magnet coils. As a part of the development of the 40 T all-superconducting magnet at the National High Magnetic Field Laboratory, USA, large-scale coils wound by two-in-hand REBCO conductor with turn-to-turn insulation was designed and tested at 4.2 K in an 11.4 T background magnetic field. We found REBCO degradations within the windings and at the crossover joints during the tests. The postmortem inspection found that the degradation was caused by delamination. The delamination appears on the surface of the conductor as bubbles which were subsequently studied by microscopy. Additionally, a similar bubble was found on a short sample by torque magnetometry. This supports the inference that the delamination bubbles found in coils are due to electromagnetic stress. In this paper, we present these findings. The implication of these findings for future magnet designs and the method of mitigation will be discussed.

Fluvial corridors mediate dynamic river-groundwater exchanges and support major transportation infrastructure, yet continuous subsurface monitoring remains limited by sparse point sensors and infrequent time-lapse surveys. Addressing this observational gap is critical during extreme flood events, when transient saturation, pore-pressure buildup, and structural scour drive complex hydro-mechanical changes that threaten both riparian slope stability and bridge foundations. Here, we demonstrate that traffic-excited highway bridges act as persistent, spatially fixed seismic sources; their vibrations, recorded on roadside telecommunication fiber with Distributed Acoustic Sensing (DAS), enable time-lapse imaging of river corridors. We evaluate this approach using dark fiber recordings along South Korea's Gyeongbu Expressway during a record monsoon flood in July 2025, targeting a 430 m multi-span bridge crossing the Miho River. Vehicle impacts excite flexural resonances of the bridge deck, generating a discrete spectral comb of fundamental-mode Rayleigh waves in the surrounding ground; these 3-6 Hz waves propagate beyond 400 m across both riverbanks to image the alluvial section down to bedrock. Because bridge-structure geometry constrains the source location and dominant modal frequencies, virtual source gathers achieve rapid convergence within 5 min of ballistic wave stacking with high waveform repeatability over the recording period. Relative seismic velocity (dv/v) monitoring resolves velocity drops up to 1.0% during flood passage associated with heavy precipitation and river stage rise, consistent with hydrological softening of the riparian sediments. These results demonstrate that traffic-excited bridges provide persistent, repeatable seismic illumination, turning roadside telecommunication cables into dense arrays for both hydrogeophysical and civil infrastructure applications.

Common-image gathers (CIGs) contain physically meaningful information about velocity-model errors through reflector focusing and residual moveout, but in conventional imaging workflows they are typically used only as diagnostic tools. In this work, we propose a multiscale self-attention summary network that maps high-dimensional 3D CIG volumes into compact conditioning embeddings for probabilistic subsurface velocity inversion. These learned embeddings preserve offset-dependent kinematic structure and spatial coherence while reducing variability caused by background-velocity mismatch. Conditioned on these summary embeddings, a flow-matching model learns a transport from a Gaussian source distribution to the posterior distribution of plausible velocity fields. Numerical experiments show that, compared with direct conditioning on raw CIGs, the proposed summary network improves posterior velocity inference. In particular, the multiscale attention design provides greater robustness to background-model mismatch, yielding more accurate posterior reconstructions and lower predictive uncertainty.

In Bayesian inverse problems, posterior sampling requires generating samples that are consistent with given observations while capturing the range of plausible solutions. Direct conditional generative models introduce latent noise to model this ambiguity, but paired inverse-problem training can still encourage an almost deterministic map from the observation to the target. As a result, generated samples may be observation-consistent while under-representing posterior variability, especially when the posterior is multimodal, leading to undercoverage, mode distortion, or artificial transitions between distinct feasible solutions. We propose joint twist-flow, an augmented flow-matching formulation that learns a continuous transport from the augmented source state $(z_x, y)$ to the augmented terminal state $(x, z_y)$. Here x is the target variable, $y$ is the observation, $z_x$ is the Gaussian reference coordinate for posterior sampling, and $z_y$ is a Gaussian likelihood-side coordinate associated with the observation branch. Under a Gaussian observation model, $z_y$ is motivated by the normalized observation residual associated with observation compatibility. Its role is not to replace uncertainty in $x$, but to couple generated samples of x to observation consistency, helping reduce likelihood-inconsistent variation while preserving variability in weakly constrained directions. We validate the method on low-dimensional inverse problems with reference posterior samples, where joint twist-flow better preserves multimodal posterior support than a direct conditional-flow baseline. We further evaluate the method on image restoration and seismic subsurface velocity-model inversion, showing increased posterior variability while maintaining observation consistency.

We present full molecular dynamics simulations that explicitly incorporate the simultaneous interactions between a trapped ion and a bath of neutral atoms. In contrast to conventional molecular dynamics treatments, this framework enables a systematic assessment of how the atomic-gas density influences both the ion's cooling dynamics and its steady-state mean kinetic energy. Our results show that the gas density measurably modifies the ion's average kinetic energy, albeit only weakly, in qualitative disagreement with predictions obtained from standard molecular dynamics simulations. In addition, the calculations indicate that short-range features of the atom-ion interaction potential become increasingly consequential as the atomic density increases. Simulations including many atoms yield higher mean kinetic energies than standard molecular dynamics, a trend we attribute to the transient formation of molecular-ion complexes. Finally, we observe that for a fixed ion, lighter atomic baths lead to a smaller final ion average kinetic energy than heavier atomic baths.

Excited electronic states are difficult to accurately and tractably model, particularly those exhibiting multiexcitonic character. Scalable single-reference excited state methods typically fail to describe such states as they suffer from the defects of the mean-field approximation and/or neglect higher-rank excitation operators. To address this, we derive the [T] perturbative correction to the quantum self-consistent equation-of-motion unitary coupled cluster singles and doubles (q-sc-EOM-UCCSD) method. The method captures triple excitation effects through a classical step of second-order in many-body perturbation theory following a q-sc-EOM-UCCSD calculation on a quantum computer. The perturbative correction developed has a $\mathcal{O}(N^7)$ classical computational time scaling. We incorporate [T] into a hybrid compute strategy wherein eigenvectors of the effective Hamiltonian from the hybrid quantum-classical q-sc-EOM-UCCSD algorithm are relayed to a classical computer which performs the postprocessing required for the [T] correction. The benefits of [T] are quantified in a benchmark covering a variety of electronically excited states of the isoelectronic CH$^+$ and BH molecules. We find that the addition of the [T] correction to both trotterized and full operator variants of q-sc-EOM-CCSD can offer dramatic improvements over baseline q-sc-EOM-UCCSD. Our assessment clearly demonstrates the importance of considering higher-rank excitation operators in q-sc-EOM-UCCSD, quantifies the overall success of the [T] correction, and discusses some of its limitations.

Natural supercavitation behind conical cavitators generates a vapor-filled envelope that, when sustained over the full body of a high-speed underwater vehicles, reduces hydrodynamic skin-friction drag and enables performance levels unattainable in fully wetted flow. Accurate prediction of maximum cavity length and maximum cavity radius and local cavity profile as functions of cavitation number and cone half-angle is essential for both the static design and the real-time shape management of super-cavitating torpedo cavitators. Classical analytical models provide computationally inexpensive predictions but exhibit systematic disagreements in the practically important regime of moderate cavitation number and large angle, where slender-body assumptions begin to break down. Existing data-driven machine learning approaches offer higher accuracy but are constrained by the prohibitive cost of multiphase computational fluid dynamics (CFD) sweeps over the full design space. This study proposes a physics-guided multi-fidelity surrogate framework based on Extended Hierarchical Kriging (PG-EHK) that addresses this challenge by fusing the multiple competing analytical cavity formulas as non-level low-fidelity (LF) trend functions corrected by a sparse set of high-fidelity (HF) CFD data. It simultaneously incorporates the analytical models regardless of their local fidelity ordering, using Bayesian-estimated scaling factors to construct the trend function as physical baseline. The resulting surrogate maps operational condition inputs to cavity geometry responses using fewer than 20 high-fidelity CFD evaluations. Cross-validation on five independent hold-out CFD cases demonstrates that the proposed PG-EHK surrogate achieves 61% reduction in prediction error compared to the best-performing individual analytical approach.

acousticHelmholtzFoam is an OpenFOAM application that computes time-harmonic acoustic pressure fields in heterogeneous gas-liquid media on unstructured polyhedral meshes. The complex pressure is stored as real and imaginary volume fields, the material coefficients are constructed from a volume-of-fluid phase fraction with geometric face-fraction averaging, and open boundaries are truncated by Cartesian perfectly matched layers (PMLs) whose damping tensor is generated by the companion utility setPMLFields. The two coupled real-valued equations are discretized with the cell-centred finite-volume operators of OpenFOAM and assembled explicitly, including processor couplings and boundary contributions, into a distributed PETSc matrix that is solved by default with the MUMPS sparse direct solver. The solver writes pressure amplitude, complex velocity, acoustic radiation pressure and time-averaged momentum flux for acoustic-force evaluation. This paper documents the implemented equations, the case interface, the assembly and solution algorithm, the build procedure and the test infrastructure. A layered gas-liquid-PML case with an analytical solution serves as an end-to-end regression: on 8000 cells the whole-domain relative complex-pressure error is $1.197\times10^{-3}$, and serial and MPI solutions on up to eight ranks agree to within $10^{-12}$ in relative $L_2$ norm. A single-axis acoustic-levitator height sweep with constant-volume spheroidal drops demonstrates the two-phase material input and the force output, and strong- and problem-size-scaling measurements characterize the direct-solver workflow. The numerical formulation and its verification are presented in a companion paper (arXiv:2610.01593).

We investigate intra-chain exciton energy transfer (EET) in a conjugated polymer using a reduced three-level donor-acceptor model that separates vertical excitations from vibrationally relaxed states. Population dynamics are obtained with the hierarchical equations of motion (HEOM), enabling a non-Markovian treatment of system-bath (S-B) interactions. Case studies on donor-acceptor (D-A) pairs with small and large energy gaps reveal competition among coherent hopping, vibrational relaxation, and Förster resonance energy transfer (FRET). Varying the hopping and the S-B relaxation rate highlights roles of exciton transfer, thermal relaxation, and dipole-mediated coupling. The results show that energy gap, hopping, and relaxation jointly control intrachain EET.

The existence of energy is the main criterion for (i) providing a common core to the materiality of all types of classical matter and (ii) denying that the gravitational field of General Relativity is a type of matter. Extending this criterion to quantum field theory, though formally straightforward, carries new conceptual consequences for the interpretation of gravity. Renormalization of the quantum matter stress-energy tensor forces a local, purely geometric term, built from the metric alone, into the conserved stress-energy, and this has two faces. It grants the metric a material significance alongside its spatio-temporal one (the egalitarian view); and it binds gravitational to non-gravitational energy so tightly that the two cannot be split apart without a choice of vacuum that a generic spacetime does not supply (the unificationist view). The latter poses a dilemma: either gravitational energy is meaningless, but then so is the energy of the non-gravitational quantum fields, and only classical fields remain matter; or both are meaningful, and the metric thereby acquires the material significance of a matter field. On neither horn does the classical divide between matter and spacetime survive quantization.

The Search for Extraterrestrial Intelligence programme aims to find evidence of extraterrestrial intelligence. SETI scientists pursue this objective by searching for technosignatures primarily in the radio or optical spectrum. The IAA SETI Committee has been developing protocols to offer guidelines in case of an actual extraterrestrial signal reception. These protocols establish procedures to confirm the authenticity of the signal and its actual extraterrestrial nature, as well as the committee's role in managing the discovery in relation to the scientific community and the general public, targeting scientific queries in relation to the management and development of equipment and methodologies employed in current SETI search. The project A Sign in Space part 2 aims to actively engage the global SETI community and the global telescope network, with scientific facilities responding and coordinating themselves to confirm the extraterrestrial nature of the received signal. The project develops some of the societal, scientific and technological methodologies already employed in the interdisciplinary work A Sign in Space. Here, we established a global collaboration between SETI scientists, radio observatories and the European Space Agency to test the communication and technological pipeline and protocols leading to the reception of a simulated extraterrestrial signal by a network of radio telescopes across the globe. We also employed an ESA spacecraft for system validation. A Sign in Space part 2 aims to further implement deep space technologies in SETI search through a structured and sustained programme, in collaboration with ESA, the SETI Institute and the Italian Institute for Astrophysics. The project will aim to stage and test the entire Post Detection procedure, implementing the protocol as envisioned by the IAA SETI committee.

We investigate cosmological constraints on a $(3+1)$ neutrino framework containing an additional partially thermalized, eV-scale sterile neutrino alongside the three active species. Rather than assuming full thermalization, we allow the sterile abundance to vary independently through its physical mass $m_s$ and contribution $\Delta N_{\rm eff}$ to the effective number of relativistic species. The sterile state is modeled as a thermally distributed non-cold relic with a temperature below that of the active-neutrino background. We consider flat and non-flat $\Lambda$CDM and $w_0w_a$CDM cosmologies, all including the same $(3+1)\nu$ sector. The models are constrained using Planck 2018 CMB data, DESI DR2 BAO, Pantheon+ Type Ia supernovae, a conservative compilation of $f\sigma_8$ measurements, and the recent H0DN. Oscillation-informed priors are imposed on the active-neutrino mass splittings, while the eV scale sterile-mass range is motivated by short-baseline oscillation searches and global $(3+1)$ analyses. We find that allowing additional freedom in the late-time cosmological background substantially changes the inferred sterile-neutrino sector. The effective sterile mass increases from $m_{s,\rm eff}\sim0.11\pm0.05~{\rm eV}$ in flat $\Lambda$CDM$+(3+1)\nu$ to $\sim0.35\pm0.12~{\rm eV}$ in the non-flat $w_0w_a$CDM$+(3+1)\nu$ model. The corresponding sterile abundance shifts from $\Delta N_{\rm eff}\lesssim0.1$ to $\Delta N_{\rm eff}\simeq0.35\pm0.15$, while remaining well below the fully thermalized limit, $\Delta N_{\rm eff}\simeq1$. The non-flat $w_0w_a$CDM models also show $\sim 2.8\sigma$ preference for open spatial geometry, $\Omega_k\simeq0.0045\pm0.0016$, and yield $H_0\simeq70.9\pm0.7~{\rm km\,s^{-1}\,Mpc^{-1}}$ and $S_8\simeq0.785\pm0.015$. These models improve the minimum $\chi^2$ by approximately $21$-$25$ relative to flat $\Lambda$CDM$+(3+1)\nu$.

The problem of incompressible fluid mixing is long-studied, yet many questions remain open. This paper aims to address the question “what do efficient flow fields for mixing look like, and how do they behave?” Inspired by the dynamic and geometric approach to optimal mass transport, we formulate a version of the fluid mixing problem as an optimal control problem for transport of a passive scalar, in which the dynamics are given by the continuity equation together with an incompressibility constraint. We then develop a geometric framework around this problem which parallels that of classical optimal transport: the optimal control problem induces a metric on the set of reachable states (analogous to the Wasserstein metric), and this metric carries a formal Riemannian structure when the optimal control objective is induced by an inner product. Using this structure, we decompose our problem into a series of more fundamental geometric problems and derive (formal) necessary conditions for optimality, geodesic equations, and gradient flows for mixing. Specializing the control objective to the kinetic energy recovers the incompressible Euler equation, connecting our framework to the geometric hydrodynamics of Arnold. The resulting problem appears to be ill-posed, however, suggesting that the kinetic energy is an unnatural measure of mixing effort. Specializing instead to the enstrophy, which we argue is better suited to mixing, yields the Euler-Poincaré (EPDiff) geodesic equation for the right-invariant homogeneous $\dot{H}^1$ metric on the group of volume-preserving diffeomorphisms, and we present numerical simulations of both geodesics and gradient flows in this case. Along the way, we generalize the classical Helmholtz decomposition of vector fields, rederive the Clebsch representation of incompressible flows, and develop a parallel to the Otto calculus of optimal transport.

Uncertainty quantification in machine learning models is essential for nuclear energy applications. While machine learning models can make both uncertainty and point predictions, these models are often significantly more difficult to train jointly than models that make point predictions alone. Split conformal prediction is a family of methods for computing distribution-free prediction intervals by calculating nonconformity scores corresponding to a target quantile in a calibration dataset. Although conformal prediction only guarantees marginal coverage, normalizing nonconformity scores enables predictive interval widths to be calculated on a per-sample basis, providing an approximation to conditional coverage. In this work, we connect uncertainty predicting neural networks to pre-trained networks that estimate shielding factors in multigroup neutron cross sections. We train the uncertainty models on the same OpenMC data used by the pre-trained models, whose weights and biases remain fixed. The outputs of the uncertainty models are the standard deviations of the residual, separated into contributions from the model and tally uncertainty. Their corresponding variances are combined in quadrature to provide a normalizing factor for conformal prediction, providing interval widths that are adaptive on a per-sample basis. Baselines for comparison are established using Mondrian conformal prediction, and by using the ground truth OpenMC uncertainties as normalizers in the conformal framework. We use predicted uncertainty interval widths and the severity of predictions that overshoot the interval widths as bases for comparing our normalizer with the baselines. We find that our method dramatically reduces both interval widths and the degree to which predictions fall outside these widths.

Uncertainty quantification in thermoreflectance measurements of multilayer structures is challenging because thermophysical-property extraction involves nonlinear inverse problems with strongly coupled parameters. Covariance-based analytical propagation is efficient but assumes local linearity and approximately Gaussian fitted variables, whereas Monte Carlo (MC) propagation captures non-Gaussian behavior but is computationally expensive and can be unreliable when simultaneously fitting parameters spanning very different orders of magnitude. Here, we address these issues by combining normalized MC sampling, used to obtain reliable benchmark distributions, with physics-based variable transformations for analytical uncertainty propagation. From the multilayer heat-diffusion equation, we identify natural coordinates: the interfacial thermal resistance 1/G, the substrate combinations sqrt(k_z k_r) and sqrt(k_z C), and the thin-film combinations k_r/C, hC, and Ch^2/k_z. Across several representative multilayer systems and thermoreflectance modalities, these coordinates are approximately Gaussian even when the original physical parameters are skewed. Performing covariance-based propagation in the transformed space and mapping the results back to physical parameters yields asymmetric confidence intervals that closely match normalized MC benchmarks. The approach preserves the non-Gaussian uncertainty structure of the recovered parameters while avoiding repeated nonlinear refitting, providing a fast and physically interpretable methodology for thermoreflectance uncertainty quantification in multilayer systems governed by diffusive heat conduction.

Plasma edge simulations with codes like EMC3-EIRENE are widely used to study fusion magnetic confinement devices. However, numerical errors can significantly impact the simulation solution and are often insufficiently characterized. These errors consist of the statistical error, bias, and time integration error, resulting from the finite sampling and tracking of Monte Carlo particles, and the discretization error, resulting from a finite grid resolution. This work presents the first comprehensive numerical error analysis of EMC3-EIRENE simulations in stellarator geometries, focusing on edge plasmas in the Helically Symmetric eXperiment (HSX). Fundamental plasma and neutral quantities are analyzed, and error reduction rates are compared against theoretical expectations. The statistical error, bias, and time integration error are fully characterized; however, quantification of the discretization error remains challenging because of the complex grid geometries used in EMC3-EIRENE. The commonly used volume averaged relative change metric is shown to systematically underestimate the statistical error, highlighting a critical flaw in EMC3-EIRENE analysis, where convergence is claimed despite significant noise remaining in the simulation solution. Dominant contributions of the time integration error are shown to differ in stellarator simulations compared to tokamak simulations, related to the relative importance of cross-field transport between the two devices. Based on these results and existing literature, we establish best practices towards improved accuracy and efficiency in general stellarator modeling with EMC3-EIRENE.

The optical theorem dictates that the wave scattering of an object in the forward direction is proportional to the total power extinguished from the excitation. For passive objects, this relationship is a by-product of the intuitive notion that scattering and absorption necessarily create shadows, and that zero forward scattering, i.e., no shadow, can only be obtained if the object is fully transparent, i.e., it neither scatters nor absorbs. Materials with gain can be in principle leveraged to suppress forward scattering while preserving a large scattering signature in other directions. However, sufficient optical gain is challenging to realize, and it inherently leads to instabilities and nonlinearities. Here, we experimentally demonstrate shadow-free \ scattering from a passive high-index dielectric sphere by exciting it with a complex frequency signal tailored to mimic the presence of material gain. We achieve as much as 26dB suppression of the forward scattering when compared with optimal monochromatic excitations, while preserving strong scattering in other directions. More broadly, our results demonstrate how complex frequencies offer a natural pathway to enhance control over the scattering signature of passive objects, with implications for a range of photonic applications.

Atmospheric warming raises evaporative demand, but actual evapotranspiration (ETa) may track demand or decouple under water limitation. Here, we quantified demand-tracking ability from regression slopes relating deseasonalized ETa and reference evapotranspiration (ETo) anomalies across California during 2008-2024. Forests and croplands retained similar positive tracking under severe drought, whereas grasslands and shrublands showed weak or negative responses. Despite similar demand tracking, forest responses were consistent with subsurface-storage access, whereas cropland responses reflected managed water access and crop-system adjustment. Positive ETa-ETo coupling on active fields coexisted with a higher statewide crop-attributed fallow share of managed cropland than under no drought, showing that active-field estimates alone did not capture whole-crop-system adjustment. Late-period demand-tracking ability was lower at 54.9% of grid cells, with larger regional declines accompanying greater groundwater-storage losses. Our results indicate that similar ETa responses mask contrasting modes of water access, requiring joint consideration of hydrology and management when interpreting drought responses.

The rapid growth of low-cost and open-source spectrophotometers has created a need for systematic methodologies to evaluate and compare alternative instrument architectures. This study presents a comparative assessment of three representative open-source spectrophotometric platforms, encompassing discrete multispectral sensing and continuous-spectrum imaging approaches. Their performance was evaluated against a commercial Thermo Scientific Genesys 30 spectrophotometer in terms of spectral agreement, portability, implementation complexity, and cost. The results demonstrate that no single architecture is universally optimal; instead, each presents distinct trade-offs that determine its suitability for specific applications. Multispectral sensor-based systems offer compact, low-power solutions for field deployment, whereas imaging-based platforms provide spectral performance closer to laboratory-grade instrumentation. All hardware designs, firmware, and software developed in this work are released as open-source resources to support reproducibility and further development. The study highlights the importance of matching instrument architecture to application requirements and proposes a practical framework for the evaluation and selection of low-cost spectrophotometers, facilitating the adoption of open-source analytical tools for water quality monitoring and related applications.

Pentamode lattices are architected materials characterized by an elastic response dominated by a single deformation mode. The low stiffness of the other deformation modes, however, may result in limited stability under compressive prestress, thereby limiting their mechanical performance. In this work, we develop a general analytical framework to determine the buckling limits of prestressed 2D and 3D isotropic pentamode lattices with generic link geometries. Two distinct instability mechanisms are considered: a global instability associated with the loss of stability of the effective medium, and a local instability associated with finite-wavelength modes at the scale of the lattice cell. Closed-form expressions for the corresponding critical pressures are derived, and the buckling limit is obtained as the lower of the two. The analytical predictions are validated through numerical simulations of different PM lattices, showing good agreement over the configurations considered. The results reveal a trade-off between approaching the pentamode limit and maintaining sufficient buckling resistance, but also show that appropriate link designs can substantially increase the buckling limit while retaining the desired pentamode behavior.

In this work, we present an amplitude extrapolation ansatz for geometry optimization that uses calculations from previous steps to construct an initial guess for the quasi-Newton solver at each new geometry, significantly accelerating convergence of the coupled-cluster and Lambda solver alike. The ansatz incorporates a necessary amplitude transformation to mitigate discontinuities associated with orbital energy crossings at the Hartree-Fock level of theory, while remaining computational and memory efficient. The method is tested for a diverse set of medium-sized molecules to demonstrate a systematic and significant reduction in the number of coupled-cluster iterations compared to the conventional method.

Nuclear spin relaxation has been widely used to probe molecular reorientation in solids, yet its interpretation has always required assuming both the relaxation mechanism and the form of the underlying correlation function. In rotator-phase solids more unknowns enter the picture: in ammonium iodide, the proton T$_1$ passes through a maximum and decreases with temperature, a behavior attributed to spin-rotation coupling. Here we model both the dipolar and the spin-rotation contributions to $^1$H relaxation in solid NH$_4$I from first principles, by combining molecular dynamics with machine-learned force fields trained on density functionals and ab initio spin-rotation coupling tensors. The computed rates reproduce the measured values above the order-disorder transition, together with their temperature dependence and frequency independence, and show that fluctuations of the NH$_4^+$ angular momentum dominate relaxation throughout the rotator phase. The orientational and angular-velocity correlation functions obtained directly from the simulations further share an oscillatory component at lattice-phonon frequencies, revealing a coupling between lattice vibrations and cation reorientation that contributes measurably to relaxation. Separating this component yields a reorientational barrier in agreement with neutron scattering. The approach makes nuclear spin relaxation a quantitative benchmark for simulated dynamics in molecular solids and provides direct access to angular-momentum correlations in the solid state.

Time crystals are a unique and recently discovered state of matter in which time-translational symmetry is spontaneously broken. Although this state has been observed across a variety of quantum systems, understanding the nature between driving, dissipation, and noise and the effects of these forces on the state lifetime remains elusive. Here, we study phase diffusion and entrainment of the time crystalline oscillatory state of a thermal-vapor Rydberg atom ensemble. We apply a pulsed rf heterodyne driving field which can entrain the oscillation frequency during the on driving period. During the off period, the entrained ensemble phase diffuses on a 1/$e$ timescale up to 2.3 ms – significantly longer than the Rydberg state lifetime. We additionally study how the entrainment and phase diffusion timescales are affected by the strength and detuning of the rf driving field, and develop a classical Fokker-Planck model with an Adler-based driving term that reproduces the observed experimental findings. These results demonstrate the creation of a long-time synchronized time crystal Rydberg atom ensemble state, which could be utilized in applications requiring frequency and phase stability.

Many machine learning algorithms rely on the Fourier transform, which is also the inner core of many analog computing devices and, specifically, of optical computing. The square of the modulus of the Fourier spectrum naturally maps onto pairwise interactions, as in the renowned Ising model, making it applicable to combinatorial optimization. Here we show how the nonlinear Fourier transform, related to integrable nonlinear partial differential equations, significantly widens the computational capability and also enables a wide variety of computing gates. We study specifically the case of the nonlinear Schrödinger equation; we introduce an input composed by “tokens”, i.e., batches of information encoded in a multi-step potential, and we show how the nonlinear spectral computing arising in the wave propagation not only generalizes combinatorial optimization to higher order problems, but also realizes boolean gates. In the weakly nonlinear regime, nonlinearity generates higher-order interactions as perturbative corrections to the pairwise terms. In the solitonic regime, the discrete nonlinear spectrum performs digital computation through soliton generation. By using four input tokens, we demonstrate the programmability of all 256 Boolean functions of three variables, including 3-SAT instances. Our results show that integrable nonlinear propagation provides an unprecedented single physical platform for higher-order and logical computation, opening the way to a rich variety of new algorithms for analog computing, machine learning, and classical and quantum information processing.

A combination of remote sensing techniques, open space data and field testing was developed to derive astronomical and meteorological parameters and assess Dark Sky brightness of the hitherto unstudied Schist Villages region in the mountainous Interior Center of Portugal. This study covered the period 2012-2017 and established the methodology for regular light pollution and sky brightness studies in the region using satellite imagery to reduce cost and time and increase accuracy of the final analysis. Long-term Artificial Light At Night (ALAN) data from VIIRS instrument aboard the Suomi-NPP satellite along with cloud cover inferred from MODUS/AQUA satellite data and orographic data, were analyzed in a Geographical Information Systems (GIS) environment. The derived ALAN values were consistent with a preliminary site survey selection, confirming the accuracy of the identified dark sky spots in the region. The analyzed region area spans 1624 Km$^2$ and the darkest site within it showed a sky brightness of 21.4 mag/arcsec$^2$. Ground-based SQM photometer observations of the Night Sky Brightness (NSB) at the zenith validated the satellite derived ALAN data. The devised field testing is itself a low cost solution that can be used by teams in developing countries to easily assess dark sky potential prior to the development of local observatories and astro-tourism activities. This study supported the initial certification of this region as a Dark Sky Starlight Tourist Destination since 2018, an important milestone for dark sky protection in central Portugal.

Martian dust particles are a few micrometers in diameter, comparable to the mean free path of the gas in the hypersonic shock layer. Their Knudsen number ranges from approximately 0.2 to 100, placing them in the transitional and free-molecular regimes, whereas the flow around the vehicle is a continuum. The two-way flow-particle coupling previously developed by the authors for a two-temperature thermochemical nonequilibrium flow is extended to a receding and pyrolyzing heat shield surface. The gas-surface interaction and the in-depth material response are solved on separate time scales. The surface balances are exchanged at every flow step, while the in-depth response is coupled implicitly over physical time windows. The flow-material coupling is assessed against graphite arc-jet measurements. The framework is applied to the Schiaparelli trajectory from 50 to 30 km. Three surface chemistry models (equilibrium, Park-5, and Zhluktov-Abe) are compared for fixed geometry, char-induced recession, and combined char ablation and particle erosion. At the final trajectory point, the stagnation point heat flux differs by 30-75% among the chemistry models. For the equilibrium model, surface recession changes this heat flux by less than 1% but lowers wall temperature and increases pyrolysis gas injection. Particle erosion contributes 1.73 mm of stagnation point recession, exceeding the char-induced recession, and more than doubles the total recession for all three models. To the authors' knowledge, this study provides the first quantitative comparison of particle erosion between rarefied and continuum drag correlations, and the continuum correlation underestimates the stagnation point erosion rate by 31-35%.