Papers

Quantum gases of polar molecules have recently emerged as a powerful platform for exploring exotic many-body dynamics and correlated quantum behavior. To achieve the full potential of this platform, the production of deeply degenerate quantum gases of molecules in arbitrary confinement geometries is necessary. Here, we successfully evaporate fermionic KRb molecules in both 3D and quasi-2D geometries to well below their Fermi temperatures utilizing dipolar collisions. As we evaporate deeper into degeneracy in both geometries, we enter the Pauli-blocked regime with polar molecules, which we independently confirm for the first time by measuring the Pauli suppression of elastic collisions. Moreover, the Pauli suppression of collisions contributes to the limitation of our final molecular temperature to about 25% of the Fermi temperature in both geometries, particularly limiting quasi-2D evaporation where the Pauli blockade drastically reduces an otherwise large elastic to inelastic scattering ratio. This work demonstrates the production of degenerate Fermi gases of polar molecules both in a 3D harmonic trap and in mono- and bi-layer 2D configurations. Further, our work explores the fundamental limits on evaporation of molecular Fermi gases set by the Pauli-exclusion principle, which could be overcome in the future by introducing distinguishable scattering partners.

We study the collective dynamics of independent particles that diffuse outside a spherical surface, on which they are replicated with a prescribed catalytic rate. In spatial dimensions three and higher, the transient nature of diffusion creates a competition between autocatalytic branching and escape to infinity, leading to three distinct regimes: subcritical (finite limiting population), critical (algebraic growth), and supercritical (exponential growth). The rotational symmetry of the domain and an explicit form of the single-particle diffusion propagator allow us to fully investigate the time evolution of the population size, including the mean population size, variance, higher-order moments, as well as the whole distribution. In particular, we obtain an explicit form of the distribution at long times and describe a slow, power-law approach to this steady-state limit. We further extend the steady-state analysis to general exterior domains using the Dirichlet-to-Neumann operator, which determines the critical catalytic rate and reveals how boundary geometry affects the population-size distribution.

The gas-kinetic scheme (GKS) integrates the characteristics of flux difference scheme (FDS) and flux vector splitting (FVS) scheme, providing high accuracy in smooth regions and strong robustness near discontinuities across all Mach regimes. Leveraging these properties, an implicit discrete adjoint GKS is developed for aerodynamic shape optimization over a wide range of Mach numbers. The adjoint solver is constructed using the source-transformation-based algorithmic differentiation tool Tapenade. To enhance computational efficiency, both the flow and adjoint GKS equations are solved using an implicit time-marching strategy, also known as the Lower-Upper Symmetric Gauss-Seidel (LU-SGS) method. The effectiveness of the implicit formulation is demonstrated through comparisons with the explicit approach. To accurately impose solid wall boundary conditions, particularly in hypersonic regimes, kinetic boundary conditions and their adjoint counterparts are formulated for both adiabatic no-slip and isothermal walls. Four benchmark test cases covering subsonic, transonic, supersonic, and hypersonic flows are used to verify the effectiveness of the developed adjoint-based design optimization system.

Coarse-grained (CG) molecular simulations offer an efficient alternative to atomistic molecular dynamics to study large and complex biological systems. The accuracy of CG simulations has been increased dramatically by the introduction of machine-learned coarse-grained (MLCG) models. However, these models are typically designed to be used at a single thermodynamic point, lack temperature transferability, and can not be used to predict temperature dependent quantities like the heat capacity. Here we introduce a thermodynamically informed, temperature-transferable MLCG framework for proteins that explicitly decomposes the CG potential of mean force (PMF) into its energetic and entropic components. The model architecture enforces an exact thermodynamic relation between the energetic and entropic components of the PMF and guarantees physically consistent extrapolation and interpolation across temperature regimes. We validate this framework on an extensive dataset spanning a total of 250 $\mu$s of molecular dynamics simulations across five temperatures between 300 K and 400 K for the Chignolin protein, and demonstrate that it reproduces the temperature dependency of the reference atomistic free energy surfaces, correcting the temperature-unaware baselines. Furthermore, we show that it is possible to apply an inexpensive, post-hoc temperature-dependent correction that does not require retraining the MLCG potential, accurately recovering the atomistic heat capacity at different temperatures. Overall, this work provides a physically grounded pathway toward thermodynamically transferable MLCG simulations of complex biomolecular systems.

Probing surfaces with optical beams is a powerful technique utilised for material characterisation, and more recently, measuring surface magnetisation with vortex beams has opened new avenues by utilising OAM beams for metrology. In this paper, we focus on extending the theoretical framework of unit order vortex beam reflection to spatially homogeneous magnetic surfaces, and derive expressions for the Goos-Hänchen (GH) and Imbert-Federov (IF) shifts. We calculate closed form expressions for the plane wave eigenpolarisations, polarisations which remain in the same vectorial direction after reflection, for both dielectric and homogeneous magnetic surfaces. These are found by extending the singularimetry formalism for dielectric surfaces, where the position of the phase vortex can be analytically approximated by taking into account the finite beam width, to homogeneous magnetic surfaces. From this we find the eigenpolarisations of the unit order vortex beam for reflection from both dielectric and homogeneous magnetic surfaces. We discuss with relation to the analytical forms of the Jones matrices for GH and IF shifts the additional reflection behaviour observed for homogeneous magnetic surfaces.

How a two-dimensional solid melts remains unsettled after 60 years of study, as theory, model systems, simulations, and atomic-resolution experiments continue to suggest conflicting scenarios. The same transition can appear continuous or abrupt depending on how it is observed, where this ambiguity is especially acute in confined water. Here we study bilayer water under nanoconfinement and ask not only where its phase boundaries lie, but how the system responds to the two fields that drive them: temperature and lateral pressure. Using Lee-Yang zeros together with enhanced sampling, we find that some phase boundaries are field-selective: the two responses can differ either in continuity itself, or in how strongly they are rounded in finite systems. This distinction changes the two-step melting picture. The solid–hexatic transition is field-selective first-order, with the density channel remaining unusually rounded, whereas the hexatic–liquid transition becomes a conventional first-order transition once larger cells reveal a hidden bimodal enthalpy distribution. This framework organizes the apparent disagreement among confined-water simulations, hard-disk models and AgI experiments by identifying which thermodynamic channel each probe sees.

We study theoretically near-symmetric resonant stratified media to show how a tiny broken spatial symmetry can effectively be probed by structured light with or without orbital angular momentum. This is achieved by examining both the in-plane and out of plane Goos-Hanchen and Imbert Fedorov shifts, respectively, in the reflected light, magnified by resonant enhancement and when needed with weak value amplification. We show that non-reciprocity in reflection for illumination from opposite ends can result in different shifts, even to the extent of shifts with opposite signs for tiny imbalance resulting from the broken symmetry. Our methodology is applicable to any general stratified medium with homogeneous and isotropic constituent layers being responsive to any entity that may cause such symmetry breaking - including temperature, refractive index, displacement etc. in the near symmetric case. This may have possible use for sensing applications.

In this work, we propose a Nonlinear Pluggable Optic (NLPO) transceiver that combines the low latency and low power consumption of Linear Pluggable Optics (LPO) with the range and robustness of digital signal processing (DSP)-based transceivers. The proposed NLPO uses an analog photonic Next-Generation Reservoir Computing (NGRC) architecture, constructed on a photonic integrated circuit (PIC), to compensate for electrical-domain distortions as well as optical-channel impairments from chromatic dispersion and Kerr nonlinearity. Focusing on a simulated 50 GBd PAM-4 link, we find that the NGRC-based NLPO not only extends the range of LPO, but actually outperforms DSP-based solutions as well. Our simulations reveal two key advantages compared to DSP-based Intensity Modulation/Direct Detection (IM/DD) links: (1) the NGRC can take advantage of the optical phase information without requiring a local oscillator and (2) the NGRC can optically sample the transmitted data well above the symbol rate without requiring high-bandwidth electronics. This work showcases the potential for photonic NGRCs to outperform state-of-the-art digital solutions in real-world applications and opens a path to low-latency, lower-power IM/DD links at ranges of 10s of km.

More than a century of physics is recorded in the American Physical Society (APS) archive, but the corpus cannot be analyzed as a single, time-resolved object because its subject metadata are fragmented across eras with no shared vocabulary. We close this gap by using a frontier large language model to retrospectively assign the modern Physics Subject Headings (PhySH) to the historical archive, yielding a unified subject map for every APS paper from 1893 to 2025. The resulting map not only reproduces century-scale disciplinary arcs but also resolves the fine-grained lifecycles of individual ideas, materials, techniques, and discoveries across a vocabulary of over 3,000 PhySH Concepts. The map turns a fragmented archive into a quantitative substrate for systematic search and for data-driven studies of how physics evolves.

Machine learning is beginning to influence rarefied-gas modeling at multiple levels, including equation-solving, operator learning, learned collision physics, moment closures, direct simulation Monte Carlo (DSMC) field surrogates, and gas–surface models. This Perspective argues that the central challenge is not demonstration-level success, but trustworthy use under realistic deployment conditions: multiregime Knudsen behavior, stochastic DSMC labels, sharp nonequilibrium structures, uncertain gas–surface interaction, and scarce direct experimental anchors. I classify the main method families by what is learned, distinguish soft physics penalties from structure-preserving designs, and propose evaluation standards based on extrapolation tests, noise-aware metrics, end-to-end cost accounting, and a three-level validation hierarchy. Most current evidence is solver-facing: it demonstrates surrogate fidelity to a teacher solver more often than direct physical fidelity to experiment. The aim is not to dismiss ML for rarefied and vacuum-related gas transport, but to separate what is already credible from what remains provisional, and to define a reporting standard that makes future claims auditable.

We demonstrate direct single-shot retrieval of the full in-plane vector field of broadband terahertz waves using electro-optic sampling. Implemented with a circularly polarized optical probe and polarization-sensitive camera, the method overcomes the projection constraints of conventional electro-optic sampling and enables direct time-domain reconstruction of transverse vector fields without Fourier-domain inversion or sequential polarization analysis. We experimentally validate the approach through spatiotemporal imaging of linear, circular, azimuthal and radial terahertz fields generated by polarization-engineered silicon metamaterials. The measurements reveal ultrafast time-resolved vector-field dynamics inaccessible to conventional scalar detection, including helicoidal field rotation within a single optical cycle and the spatiotemporal evolution of structured polarization topologies. Beyond structured-field imaging, we demonstrate single-scan vector-field spectroscopy through measurements of quartz birefringence and broadband terahertz waveplate performance. More broadly, this work transforms electro-optic sampling from a projection-based measurement into a vector-resolved imaging and spectroscopy platform for structured electromagnetic fields.

Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science. PXRD is an accessible and widely used characterization technique, yet recovering the atomic structure from diffraction data requires solving an underdetermined inverse problem due to the loss of phase information. Generative modeling can provide a prior over atomic structure and learn the mapping from PXRD patterns to crystal structures via simulated structure-spectrum pairs. We present XRDiff, a diffusion model that recovers crystal structures from PXRD given either the stoichiometry or, in a more challenging setting, the elemental constituents and total number of atoms in the unit cell. We evaluate on datasets where each stoichiometry has multiple polymorphs and all polymorphs of a given composition are held out together, ensuring that high performance reflects genuine use of the diffraction signal. XRDiff achieves strong structure recovery rates on simulated benchmarks, indicating that the model learns a spectrum-to-structure mapping precise enough to differentiate between polymorphs. To address generalization to experimental data, we compare a full-spectrum encoding against an encoding based on peak descriptors. The peak-based encoding generalizes substantially better, outperforming even a model trained on full spectra with augmentations fitted to the experimental noise distribution. These results demonstrate that representations robust to the noise and artifacts present in real-world PXRD offer a practical and scalable path toward closing the simulation-to-experiment gap, enabling zero-shot crystal structure solution from experimental PXRD with full or partial chemical composition input.

A pure stress-based finite difference formulation is introduced for modeling elastic wave propagation in linear elastic solids with spatial heterogeneity. The approach derives from the strong form of the elastodynamic equation of motion, in which stress is the only dependent variable. A standard second-order central difference scheme is applied to discretize the equation of motion, allowing the space-time-dependent evolution of stress components to be modeled. Numerical dispersion analysis is performed for homogeneous, elastically isotropic materials. Simulations are then carried out for a spatially heterogeneous case consisting of a bimaterial with stiffness heterogeneity. This bimaterial case allows comparison with known closed-form solutions for reflection and transmission coefficients and with an analogous displacement-based finite difference model. Simulations are executed on modern graphics processing unit architectures, enabling stress-based modeling of large-scale three-dimensional problems exceeding one billion degrees of freedom. The approach shows promise for ultrasonic simulations in materials with stiffness heterogeneity and uniform mass density, conditions common in polycrystalline metals used in engineering applications. The formulation offers a potential alternative means of modeling wave propagation and scattering in heterogeneous materials, with possible applications in nondestructive evaluation, materials characterization, biomedical ultrasound, and geosciences.

We construct a three-layer reaction-diffusion model of an autocatalytic chemical system in which raw molecules ($a_i$), catalytic proteins ($p_l$) and large RNA/protein “genes” ($W_p^{(k)}$) interact through a mass-action stoichiometry tensor $\mathrm{Coef}_{ijk}$ whose magnitude is modulated by the fold-stable activity of the largest polymers.Mass-action is broken by an $\epsilon$-noise term so that the system is nonequilibrium. We compute the total entropy production $\sigma(t)$, the genetic Shannon entropy $S_\mathrm{gene}$ and the thermodynamic uncertainty relation (TUR) and thermodynamic speed limit (TSL) bounds on growth and evolution rates. The hierarchical model exhibits the expected co-occurrence of $\sigma_\mathrm{env}\!\uparrow$ and $S_\mathrm{gene}\!\downarrow$ predicted by Schrodinger's negentropy argument and reformulated as maximum-entropy-production-principle (MEPP)-driven adaptation. In contrast to a single kinetic-proofreading-like cycle, whose TUR products of $\sim 5$, matching the experimentally reported regime of the ribosome.The hierarchical model's TUR product sits $10^4$-$10^5$ above the universal bound of 2, and the TSL ratio sits $10^6$-$10^8$ above its bound of 1. And scaling number of molucules leaves the looseness intact for the hierarchical model but tightens it monotonically with particle number for the minimal model. We close by drawing an explicit correspondence between the autocatalytic system and diffusion-model training: $a_\mathrm{ext} \to a$ flux $ \Leftrightarrow $ data-information flow, $ \tanh(\beta Wp - \theta) $ $\Leftrightarrow$ score network, replication noise $\Leftrightarrow$ forward-diffusion noise, $ S_\mathrm{gene} \searrow $ $\Leftrightarrow$ $ H[q(\theta|\mathcal{D})] \searrow $. All code and figures are available https://github.com/xiangze/DiverseCells/Hier_Autocatalysis

This work presents an experimental setup for implementing two-qubit operations on neutral atoms ($^{87}$Rb) with the possibility of using two different Rydberg excitation schemes. One of them uses 5P$_{1/2}$ as the intermediate level and applies the second-stage beam locally to the addressed atoms. The second scheme uses the 6P$_{3/2}$ level; in this scheme, the particles to be entangled are moved to a separate zone through which both Rydberg beams pass. The advantages and limitations of both schemes are analyzed. Based on numerical modeling performed with a Julia package developed by the authors, it is demonstrated that the spatial configuration has a greater effect on quantum-operation fidelity than the choice of intermediate level. An experimental implementation of the scheme using the 6P$_{3/2}$ level is demonstrated, making it possible to achieve a two-qubit operation fidelity of 94%.

Numerical simulations are performed for an FFA-W3 wind-turbine airfoil corresponding to a section of the DTU 10-MW Reference Wind Turbine. Wall-resolved large-eddy simulations (LES) are carried out with Nek5000 and EllipSys at chord Reynolds number $Re_c=1\times10^5$ and effective angle of attack $AoA=3.1^\circ-3.3^\circ$. A spanwise domain width of 10 percent of the chord is sufficient to reproduce the time-averaged flow and the evolution of the main disturbances. EllipSys is validated against Nek5000 for LES, showing close agreement for the mean flow and most amplified perturbations. EllipSys underpredicts the amplitude of Tollmien-Schlichting waves in the attached boundary layer, owing to higher numerical dissipation, but closely predicts the evolution of the Kelvin-Helmholtz (KH) mode in the laminar separation bubble, in agreement with parabolized stability equation (PSE) results. The mode shape is extracted using spectral proper orthogonal decomposition (SPOD), revealing the KH wavepacket forming in the separation bubble. Long-time EllipSys simulations show a slow modulation of the normal-force coefficient, with amplitude 10.5 percent and period 48 flow-through times, corresponding to $f=f^*c/U_\infty=0.021$ and $St=f\sin(AoA)=0.0012$. This frequency is associated with low-frequency oscillations reported in airfoil studies, although the Strouhal number is lower than previously observed and occurs at smaller angle of attack. For the DTU 10-MW turbine, the oscillation period corresponds to 7.7 blade rotations. Periodic stalling and reattachment may trigger the oscillation, while sufficiently high reverse flow on both sides of the airfoil may permit absolute instability and periodic bubble bursting.

Silicon Photomultipliers (SiPM) will be used extensively in the upgraded CMS detector at the Large Hadron Collider. SiPMs have already been implemented into the barrel and endcap hadron calorimeters (HCAL) as part of the Phase I upgrade, and hundreds of thousands of SiPMs will be used for two new Phase II subdetectors, the Barrel Timing Layer (BTL) and the endcap high granularity hadronic calorimeter (HGCAL). We discuss the motivation for SiPMs as the photodetectors of choice in CMS, the evolution of the SiPMs from Phase I to Phase II, and the particular challenges faced and overcome for each of the three subdetectors.

This paper studies an inverse problem in nonlinear optical encryption. We examine chosen-plaintext attacks (CPA) on a nonlinear optical encryption strategy that integrates double random phase encryption (DRPE) into a nonlinear optical propagation model to enhance the security of the combined system. We first demonstrate that the system's phase information can be decoded from carefully designed differential CPA data. We then demonstrate that the strength of the optical device's nonlinearity can also be recovered from CPA data, indicating that including this parameter as an additional security key does not enhance protection against CPA attacks, although numerical simulations show that strong nonlinearity still poses significant challenges for CPA attacks. Finally, we provide a stability analysis to demonstrate that small errors in decoded security keys result in only small errors in the decrypted text, even though the encryption process is nonlinear.

Energetic particle (EP) transport stiffness plays a crucial role in determining EP confinement in tokamak plasmas. This work investigates the impact of nonlinear thermal plasma dynamics on EP transport using global gyrokinetic simulations with the TRIMEG code. Simulations are performed for the ITPA toroidal Alfv'en eigenmode (TAE) benchmark, and extended to beta-induced (BAE) and reversed shear Alfven eigenmodes (RSAE). Two models are compared: a fully nonlinear treatment of all species and a reduced model with only nonlinear EP. For TAE simulations, while linear properties of the instability are identical in both cases, significant differences arise in the nonlinear regime. Including thermal plasma nonlinearity reduces the saturation level following an overshoot phase and modifies the radial mode structure, including mode broadening and poloidal harmonic splitting. These changes significantly affect EP transport. Specifically, the dependence of EP flux on the EP drive becomes weaker when nonlinear thermal plasma dynamics are included. The saturation level changes from an approximately quadratic scaling to a weaker, nearly linear dependence, reducing the scaling of EP flux with the EP gradient from quartic to quadratic. Zonal flow generation is observed but plays a minor role in regulating the instability. Without accounting for thermal nonlinearity, the EP flux can be overestimated by an order of magnitude. Simulations of BAEs and RSAEs demonstrate similar effects of thermal plasma nonlinearity on mitigating the saturation level. These results demonstrate that nonlinear thermal plasma effects provide an important feedback mechanism that reduces EP transport stiffness and regulates Alfvenic mode saturation, which is essential for predictive modeling of EP confinement in burning plasmas such as ITER.

The Fermi paradox is sharpened, not weakened, by plausible extrapolations of artificial intelligence, autonomous robotics, in-situ resource utilization, orbital manufacturing, space-based computing, and uncrewed interstellar probes. Once a civilization can design, launch, and maintain autonomous industrial systems beyond its home planet, interstellar expansion no longer requires biological starships or a human-like empire. It can proceed through low-mass probes, robotic seed factories, archival payloads, biological repositories, local computation, and slow replication across nearby stellar systems. This paper proposes the quiet expansion filter: old, stable civilizations that reached autonomous AI-cosmoindustry probably did not arise in the part of the Galaxy capable of reaching the Solar System, because after that threshold interstellar expansion becomes too useful, inexpensive, and rational for all civilizations to refuse; however, successful expansion would be machine-mediated, distributed, low-noise, and partly biological rather than Kardashev-like or imperial. Order-of-magnitude estimates indicate that a single post-threshold civilization could saturate its reachable stellar neighborhood within ~10^7 yr – less than 0.1% of Galactic age – at modest energy cost per probe. The novelty of the proposal lies not in any new mechanism but in extending the AI-filter literature toward post-threshold observability predictions. The hypothesis predicts that successful advanced expansion, if present, is more likely to appear as weak artifacts, local probes, small-scale resource processing, exoplanetary anomaly clusters, or techno-biological preservation systems than as galaxy-scale energy harvesting.

Complex neural quantum states are difficult to optimize when their wavefunction phase carries gauge, chiral, fermionic, or topological structure. We show that the major failure mode is not only ansatz expressivity, but the Monte Carlo estimator used to learn this phase. For separated amplitude-phase states, differentiating the local energy at fixed samples gives a different unbiased estimator of the same variational Monte Carlo phase force, without changing the objective. We further extend the construction to coupled two-head networks by keeping the amplitude-gradient contribution and applying the direct derivative only to the phase path. An adaptive minimum-variance mixture interpolates between standard and direct estimators during training. Across flux ladders, chiral chains, two-dimensional flux cylinders, an interacting fermion ladder, shared-network controls, and a fractional quantum Hall benchmark, the resulting estimators reduce phase-gradient variance, suppress seed failures, and often move multi-percent standard-gradient plateaus to sub-percent accuracy.

Sensors with fast timing capabilities (on the order of tens of picoseconds) are a critical component of future HEP tracking detectors, providing the ability to disentangle high multiplicity events. Silicon 3D sensors are capable of delivering this temporal resolution and additionally display an excellent radiation hardness, suitable for the harsh environments expected in future hadron colliders (e.g. FCC-hh). In the course of the RD50 project "3D detectors optimized for timing applications", double-sided 3D sensors with two different column layouts, hexagonal and orthogonal, and different column counts were designed and produced by CNM. In this study, results of IV, top-TCT and source timing measurements of unirradiated as well as proton irradiated sensor structures are presented. The IV characterisation shows a strong decrease of the overall high leakage current of the sensors after irradiation. Top-TCT measurements with both an infrared and red laser reveal a mostly homogenous charge collection in the sensor bulk, while higher signals are observed in the outermost columns for surface charge injection. The time resolution of sensors irradiated up to a $1 \, \mathrm{MeV}$ neutron equivalent fluence of $3\times10^{15} \, \mathrm{cm^{-2}}$ reaches down to ca. $65\,\mathrm{ps}$, showing only a slight degradation compared to unirradiated sensors.

We develop a geometric model for the spreading of shallow, viscous puddles of arbitrary shape, building on the recent 'capillary current' model for axisymmetric droplets. In short, we assume that a spreading puddle remains close to instantaneous mechanical equilibrium as it spreads, with hydrostatic pressure balanced by surface curvature. In turn, its contact line advances so as to maximize the rate of energy loss subject to viscous dissipation. The resulting system yields a natural geometric evolution equation for both the two-dimensional footprint and the three-dimensional depth profile of a spreading puddle. In appropriate limits, it recovers the classical spreading laws for axisymmetric droplets, a local version of the Hoffman-Voinov-Tanner law for small non-axisymmetric puddles, and a nonlocal Hele-Shaw-like description for large, relatively regular puddles. We show that the model rationalizes new observations of silicone oil spreading over smooth borosilicate glass.

Fluidic networks may contain hierarchical porous structures in which transport across macroscopic interfaces is controlled by geometrical features at multiple nested length scales. Direct resolution of all these scales rapidly becomes computationally prohibitive for network-scale studies, while reduced descriptions require a priori effective hydraulic properties. Here, we develop a nested homogenization framework for rigid porous interfaces under single-phase viscous flow. The pore-scale structure is first replaced by an effective stress-jump interface law, which is then embedded as an internal interface condition within a second characteristic problem at the intermediate scale. This successive upscaling propagates the pore-scale geometry through effective tensors to obtain a closure for flow across the whole porous structure. The approach is validated against fully resolved simulations at the mesoscopic and macroscopic scales, for two xylem-inspired channel configurations, and accurately captures pressure drops and flow redistribution while strongly reducing computational cost. The framework provides a tractable bottom-up description of a network composed of nested porous membranes and a basis for extensions to deformable structures and multiphase flows.

In this work, we derive the working equations for the transition density matrices within the equation-of-motion frozen-pair coupled-cluster framework. We focus specifically on the recently developed EOM-fpCCSD and EOM-ptCCSD models and present the corresponding transition dipole moments and oscillator strengths obtained via approximate expressions. This formulation avoids the computational overhead of solving the coupled-cluster $\Lambda$ equations. Furthermore, we employ a matrix inverse approximation to eliminate the need for explicit calculation of the left EOM eigenvectors. The accuracy of the resulting EOM-fpCCD and EOM-ptCCSD excited-state properties is benchmarked against the linear-response (LR)-CCSD method. Our results demonstrate that the description of excited state properties is improved when using EOM-fpCCSD and EOM-ptCCSD models compared to the standard EOM-CCSD variant.

The composition of partially ionised plasmas is investigated for densities and temperatures at which the free electrons are degenerate. Based on a quantum statistical approach, the effect of Pauli blocking is addressed. Specifically, one- and two-electron ions are studied. Approximations for deriving an in-medium Schrödinger equation for the ionization potential are indicated. New results regarding the degree of ionisation and the Mott effect are presented. Standard codes for plasma properties do not take Pauli blocking effects into account and are therefore unable to explain the experiments in the high-density regime, where the electrons are degenerate.

Reducible oxides exhibit a rich interplay of electronic, structural, and chemical properties that underpins applications in catalysis, photovoltaics, batteries, and energy storage. This interplay is strongly shaped by excess electrons, often introduced by oxygen vacancies, that localize as small polarons and influence charge transport and surface chemistry. At surfaces, these polarons play a central role in charge localization, mobility, and reactivity, yet their finite-temperature dynamics remain difficult to access from first principles due to the long time scales needed to adequately sample polaron's hopping. To overcome this limitation, we extend machine-learning-assisted polaron dynamics to redox-active oxide surfaces, using oxygen-deficient rutile TiO$_2$(110) as a paradigmatic case. By accessing several nanoseconds of dynamics over a range of temperatures, we show that small-polaron mobility at the reduced rutile TiO$_2$(110) surface is suppressed by several orders of magnitude relative to the corresponding bulk material, providing a microscopic interpretation of the lower electron mobilities observed in porous rutile TiO$_2$ compared with single-crystal samples. This suppressed mobility arises from the loss of favourable hopping pathways: surface polaron motion is largely confined to planar inter-row trajectories within the second topmost layers, with only rare interlayer hopping events. These results establish a transferable machine-learning strategy for investigating polaron dynamics in reducible oxides.

We can do more than defend science from a flood of AI-assisted papers. Used well, AI offers a historic opportunity to correct distortions in the publication system, help us publish fewer and better papers, and give scientists back the time to do their best work.

The response of particles to low-Reynolds flow can be compactly predicted with resistance or mobility tensors. However, the difficulty of obtaining accurate values for the elements of these tensors for specific geometries has held back work on particles with complex shapes. Here we show how Stokesian dynamics can be adapted to efficiently compute the resistance and mobility tensors of rigid and flexible aggregates, including confinement by walls. We introduce SHAPES, an implementation of the method, and demonstrate its capabilities for complex geometries including curved fibres, chiral dipoles, interacting aggregates, and active swimmers. Aggregates are represented by assemblies of beads designed to reproduce the geometry and motion of rigid or flexible particles. This coarse-grained description preserves the essential hydrodynamic interactions while substantially reducing computational cost. The method accurately reproduces known exact and approximate solutions, as well as experimental observations. The ability to compute the complete resistance and mobility tensors provides new insight into how aggregate shape controls translation, rotation, and coupling to fluid-velocity gradients. Previous descriptions often relied on simplified models retaining only a few symmetry-allowed couplings. While useful, such reduced descriptions are not always structurally stable under small perturbations of particle shape. Computing the full tensors makes it possible to draw robust conclusions and relate them to shape symmetry and hydrodynamic interactions. In particular, the method allows systematic analysis of non-Jeffery couplings to the strain rate that arise for helicoidal aggregates. SHAPES therefore provides a versatile framework for studying rigid and flexible aggregates in microfluidic, biological, and environmental flows.

NASA has five robotic space probes on escape trajectories from the Solar System, and the Interstellar Probe concept was considered in the recent U.S. Solar & Space Physics Decadal Survey. While none of these robotic probes will be operational when they reach another star, it is natural to ask whether another civilization also might have sent out interstellar probes. Serious consideration of interstellar probes dates at least to R. Bracewell in the early 1960s, and the discovery of three interstellar objects has rekindled some of that interest. I consider current limits on signatures of extraterrestrial technology in the Solar System, both objects on various orbits ("probes") and surface artifacts, using data from planetary exploration and astronomical sky surveys. Perhaps not surprisingly, the completeness to which the Solar System has been searched varies as a function of distance from the Sun. I conclude that only extremely crude upper limits can be placed on the existence of technosignatures in the Solar System and that, in some cases, relatively large probes or surface artifacts would have escaped detection. I also highlight areas that might be profitable for improving these limits considerably.