Papers

This paper discusses transport barrier formation and layering as consequences of jam formation. Extensive use is made of analogies with the theory of traffic flow in one dimension. The relation of flux jamming to motility induced phase separation (MIPS) is explained. Two routes to heat flux jamming are identified. The first is due to a rollover in the heat flux-pulse size relation, i.e. $dQ_T(\delta T)/d\delta T<0$, and is similar to the condition of flux-gradient bistability. The second occurs when the delay time between pulse and heat flux exceeds a critical value. This does not require bistability and tends to occur near marginality. This analysis yields an estimate of the answer to the eternal question of 'how near is "near"?'. Staircase development is shown to follow jamiton train formation. The relation of jamming of avalanches to phase transitions in drift wave-zonal flow turbulence is elucidated. The formation of outward propagating blob trains and inward propagating void trains is demonstrated. The important role of turbulence spreading is identified.

Surface meltwater from glaciers and ice sheets contributes significantly to sea-level rise, yet the processes governing its transport and retention within cold firn remain poorly constrained, particularly in multiple dimensions. Here we present a multidimensional, vertically integrated modeling framework for aquifers in cold firn that incorporates phase change and residual trapping of liquid water. This mathematical framework, together with its numerical implementation, extends terrestrial groundwater models to describe aquifers expanding within otherwise cold firn, highlighting the analogous physics governing both systems. We derive semi-analytical solutions for finite-volume aquifers and validate them against numerical simulations and higher-fidelity model results. These solutions elucidate key features of meltwater dynamics and provide benchmarks for firn hydrologic models. We further demonstrate the three-dimensional expansion of an aquifer in cold, heterogeneous firn. Both the semi-analytical and numerical results show that lateral aquifer propagation slows at lower initial firn temperatures due to porosity reduction and associated loss of liquid water from freezing. Overall, this framework provides new insights into the formation and expansion of firn aquifers in percolation zones and helps clarify how subsurface meltwater storage modulates meltwater fluxes, surface mass loss, and contributes to global sea-level rise.

The dissipation mechanisms in weakly collisional plasmas have been a longstanding topic of investigation, where significant progress has been made in recent years. A recent promising development is the use of the "scale-filtered" Vlasov-Maxwell equations to fully quantify the scale-by-scale energy balance, a feature that was absent when using fluid models in kinetic plasmas. In particular, this method reveals that the energy transfer in kinetic scales is fully accounted for by the scale-filtered pressure-strain interaction. Despite this progress, the influence of ion-electron thermal disequilibrium on the kinetic-scale energy budget remains poorly understood. Using two-dimensional fully kinetic particle-in-cell simulations of decaying plasma turbulence, we systematically investigate the pressure-strain interaction and its components at sub-ion scales by varying electron-to-ion temperature ratios. Our analysis focuses on three key ingredients of the pressure-strain interaction: the normal and shear components of Pi-D and pressure dilatation. Our results demonstrate that the scale-filtered pressure-strain interaction is dominated by scale-filtered Pi-D across the kinetic range, with the shear component consistently providing the dominant contribution. We find that the scale-filtered normal and shear contributions of Pi-D exhibit persistent anticorrelation and opposite signs across all kinetic scales. We also discover that the amplitude of both anisotropic components for each species scales directly with their temperature and inversely with the temperature of the other species, while the scale-filtered pressure dilatation remains negligible compared to the Pi-D terms but shows enhanced compressibility effects as plasma temperatures decrease. We discuss the implications of these findings in thermally non-equilibrated plasmas, such as in the turbulent magnetosheath and solar wind.

We present an epi-illumination multi-camera array microscope (epi-MCAM) designed for wide-field reflective imaging of non-transparent samples. The epi-MCAM contains 24 tightly packed and synchronized epi-illumination microscope units, arranged in a $4 \times 6$ planar array at 18 mm spacing. Each unit contains a unique CMOS image sensor (13 megapixels each), an objective and tube lens pair, and a beamsplitter and epi-illumination light path. An epi-MCAM capture cycle produces a stitched image covering $72 \times 108~\mathrm{mm}^2$ at a micrometer scale resolution down to 2.46 $\mu$m. To image samples exceeding this native field of view, we translate the entire array across the sample surface to enable high-resolution coverage of large objects. We demonstrate the system's ability to image both flat and three-dimensionally structured reflective samples, such as semiconductor wafers and printed circuit boards, which highlight the epi-MCAM's strong potential within industrial inspection applications.

The spectral properties of feature embeddings offer critical insights into model generalization and representation quality. While deep learning models are widely used for molecular property prediction, kernel methods remain competitive in low-data regimes, yet their spectral behavior is largely unexplored. We present the first comprehensive spectral analysis of kernel ridge regression across diverse representations, including molecular fingerprints (ECFP), pretrained transformers, graph neural networks, and 3D descriptors, evaluated on QM9, MoleculeNet, and Therapeutics Data Commons benchmarks. Surprisingly, richer spectral features do not consistently yield better generalization performance, contradicting common representation heuristics used in self-supervised learning (SSL). Across four spectral metrics, only ECFP-based kernels show a strictly positive correlation with performance, particularly SSE, ID, and SR on MoleculeNet. Global 3D representations exhibit mixed behavior; transformer-based representations are consistently positive across all four metrics, whereas local 3D representations show a negative trend and no significant correlations. Truncation analysis further emphasizes this disparity: for local 3D representations on thermodynamic targets, fewer than 2% of eigenvalues, and occasionally as few as 0.02%, are needed to recover 95% of performance, whereas ECFP and transformer kernels require significantly more. By demonstrating a strong dependence on both task and representation, our results challenge the heuristic that richer spectra inherently improve generalization, providing new guidance for evaluating representations in SSL and in label-limited scientific tasks.

Halide perovskites have emerged as promising candidates for high-performance solar cells. This study investigates the temperature-dependent optoelectronic properties of mixed-cation mixed-halide perovskite solar cells using electroluminescence (EL) and photoluminescence (PL) hyperspectral imaging, along with current-voltage analysis. Luminescence images, which were converted to EL and PL external radiative efficiency (ERE) maps, revealed significant changes in the optoelectronic behavior of these devices at low temperatures. Specifically, we found that a significant source of heterogeneity in the low-temperature EL ERE maps below 240 K is related to local charge injection and extraction bottlenecks, whereas PL ERE maps show suppressed non-radiative recombination and significant improvements in efficiency throughout the investigated temperature range. The spatial distribution of ERE and its variation with applied current were analyzed, offering insights into charge-carrier dynamics and defect behavior. Our results reveal that while the perovskite layer exhibits enhanced ERE at low temperatures, charge injection barriers at the interfaces of the perovskite solar cells significantly suppress EL and degrade the fill factor below 240 K. These findings reveal that a deeper understanding of the performance of perovskite solar cells under low-temperature conditions is an essential step toward their potential application in space power systems and advanced semiconductor devices.

A novel matrix method of analyzing ion saturation current data from a general three-dimensional (3D) array of unmagnetized Mach probe tips is developed and used with data sets from two 3D Mach probes to make initial measurements of local plasma flow velocity in reversed-field pinch (RFP) experiments in the Madison Symmetric Torus (MST). The two 3D Mach probes are composed of regular polyhedral arrays of six and four tips, respectively, with the six-tip array composed of three orthogonal pairs of mutually opposite tips at the vertices of a regular octahedron and the four-tip array composed of non-opposite tips at the vertices of a regular tetrahedron, the analysis of which is specifically facilitated by the matrix method. Velocity measurement uncertainties for the Mach probes are derived based on uncertainties in probe machining and ion saturation current measurements, and typical relative uncertainties for the probes are estimated to be of order several percent, likely smaller than systematic uncertainties related to the Mach probe calibration constant and experimental uncertainties related to plasma and probe conditioning. Initial results for the octahedral probe show flow speeds of roughly the expected magnitudes based on previous MST measurements but with somes differences in flow direction, while those for the tetrahedron probe show similar flow directions to some previous measurements but also some larger than expected speeds. We consider possible causes for the unexpected results of these initial tests, with a focus on probe conditioning and fast electron issues.

All-perovskite tandem solar cells with narrow and wide bandgap perovskite absorbers are promising candidates for low-cost and high efficiency photovoltaic applications. However, the open circuit voltage of typical tandem structures is generally smaller than the sum of the individual voltages in the single-junction form; a quantity we call the voltage gap. Subcell optimization can only begin once nonradiative losses associated with each absorber layer can be properly identified. To address this, we used absolute electroluminescence hyperspectral imaging to construct external radiative efficiency maps of each subcell within the tandem stack and compare these measurements with single junction devices. These measurements were then combined with additional electro-optical characterization and modeling to construct subcell current vs voltage curves. We find that the narrow band gap subcell contributes the most towards the voltage gap and therefore fabrication and processing efforts should focus on reducing nonradiative recombination losses within the narrow band gap absorber.

We introduce a modified corner cube reflector that encodes information from passive optical sensors in its retroreflected diffraction pattern, enabling remote sensor-state measurement over a single-ended optical link. The design interferes a reference path and a sensor-modulated path within the retroreflected beam to produce an interferometric signal suitable for reading out phase and amplitude variations with a squarelaw camera. This enables sensor-state determination in arbitrarily oriented passive nodes, extending coherent interferometric read out of chemical, biological, and physical sensors to scalable, robust, and inert field deployments.

Flying focus techniques produce laser pulses whose focal points travel at arbitrary, controllable velocities. While this flexibility can enhance a broad range of laser-based applications, existing techniques constrain the motion of the focal point to the propagation direction of the pulse. Here, we introduce a flying focus configuration that decouples the motion of the focus from the propagation direction. A chirped laser pulse focused and diffracted by a diffractive lens and grating creates a focal point that can move both along and transverse to the propagation direction. The focal length of the lens, grating period, and chirp can be tuned to control the direction and velocity of the focus. Simulations demonstrate this control for a holographic configuration suited to high-power pulses, in which two off-axis pump beams with different focal lengths encode the equivalent phase of a chromatic lens and grating in a gas or plasma. For low-power pulses, conventional solid-state or adaptive optics can be used instead. Multi-dimensional control over the focal trajectory enables new configurations for applications, including laser wakefield acceleration of ions, nonlinear Thomson scattering, and surface-plasmon emission of THz radiation.

We present a comprehensive computational framework for simulating nonadiabatic molecular dynamics with explicit inclusion of geometric phase (GP) effects. Our approach is based on a generalized two-level Hamiltonian model that can represent various electronic state crossings - conical intersections, avoided crossings, and elliptic intersections - through appropriate parameterization. We introduce a novel prelooping trajectory initialization scheme, allowing us to encode the memory as an initial phase accumulated due to the adiabatic evolution over the potential energy surface. This is a unified framework to handle different types of level crossings by incorporating Berry curvature-based force corrections to Ehrenfest dynamics, ensuring accurate representation of topological effects. For conical intersections, our method incorporates the theoretically expected phase pi, while for elliptic intersections, it yields a parametrically tunable but loop radius (energy) independent phase different from pi. We also include an eccentricity parameter (e) in the diabatic coupling to model more realistic molecular systems. Numerical simulations demonstrate the consistency of our approach with theoretical predictions for mixing of states and inhibition from mixing due to geometric phase effects. This framework provides a valuable tool for studying quantum-classical interactions in molecular systems where geometric phase effects play a significant role. The elliptical intersection and geometric phase effect opens avenue for the design and discovery of degenerate materials. It produces a fresh look to help develop a new kind of spectroscopy and potential qubit applications. This simple Hamiltonian reveals a pathological phase protection effect E = kr, where k is real, that has great utility in a new spectroscopy design.

The Australasian strewn field covers more than 15% of Earth's surface, consisting of tektites and microtektites. Australasian tektites from Southeast Asia and Australia, as well as microtektites recovered from deep sea sediments and Antarctica, are established to be derived from upper continental crust sediments. An expedition to retrieve remnants of bolide CNEOS 2014 January 8 (IM1), held in the Pacific Ocean, was in proximity to the known extent of the Australasian strewn field, and yielded "BeLaU"-spherules, whose compositions did not match most well-studied solar system material. We therefore report precise and comprehensive elemental data for Australasian tektites to compare their elemental abundances to those of microtektites from deep sea sediments, and BeLaU. Our findings corroborate previous studies that Australasian tektites and microtektites closely resemble the elemental abundance patterns of the upper continental crust. Meanwhile, the elemental patterns of the BeLaU-spherules are distinct from the Australasian tektite/microtektite compositions.

Videos of the 2020 Beirut explosion offer a rare opportunity to see a shock wave. We summarize the non-linear theory of a weak shock, derive the Landau-Whitham formula for the thickness of the overpressure layer and, using frame-by-frame video analysis, we demonstrate agreement of data and theory.

The numerical solution of time-dependent PDEs posed over the combined space-time domain, instead of treating time sequentially, leads to space-time methods. Treating time as another spatial dimension allows parallelization across time and simultaneous adaptivity in spatial and temporal discretizations. However, space-time formulations suffer from the curse of dimensionality due to the (d+1)-dimensional domain. Time-series forecasting methods, such as dynamic mode decomposition (DMD), also suffer the same scaling for storing and processing measurement data. In this work, we investigate whether quantum-inspired tensor network methods can mitigate this curse for both solving PDEs and data-driven modeling. We formulate a space-time matrix product state (MPS) encoding to represent either the space-time solution or time-series data. Using this representation, we develop a unified framework that combines an MPS space-time PDE solver with an MPS-DMD algorithm. We benchmark our MPS space-time solver for linear and nonlinear PDEs in both (1+1) and (2+1) dimensions, including non-diffusive regimes such as shockwave formation and Allen-Cahn phase separation. We observe that the MPS ansatz efficiently captures the spatio-temporal correlations of the solutions, with a 99% reduction in parameters with respect to the grid size. We demonstrate the MPS-DMD algorithm in two cases. First, the MPS space-time solver generates the MPS encoding of the time-series data. We then apply MPS-DMD directly to predict the dynamics without solving PDEs. Second, for the general case where data is available as dense arrays, MPS-DMD needs an intermediate compression step. We implement a streaming-like compression algorithm and observe a 97% reduction in memory, with MPS-DMD predictions matching those of exact DMD.

We investigate the combined effects of carrier envelope phase and laser pulse shape on electron-positron pair production in the presence of an external asymmetric super-Gaussian electric field by solving the quantum Vlasov equation. By varying the field asymmetry, the pulse shape from Gaussian to super-Gaussian, and the carrier envelope phase, we show the momentum distribution and the number density of created pairs to exhibit extreme sensitivity to these field characteristics. The effects are also qualitatively explained by analyzing the turning-point structures within the WKB formalism. We observed that multiphoton pair production dominates in the case of long falling-pulse asymmetry. For a short falling pulse with a flat-top super-Gaussian laser profile, pair production is further facilitated. For certain field parameters, we demonstrate that the number density can be enhanced by two to three orders of magnitude.

Machine learning-based precipitation nowcasting relies on high-fidelity radar reflectivity sequences to model the short-term evolution of convective storms. However, the development of models capable of predicting extreme weather has been constrained by the coarse resolution (1-2 km) of existing public radar datasets, such as SEVIR, HKO-7, and GridRad-Severe, which smooth the fine-scale structures essential for accurate forecasting. To address this gap, we introduce Storm250-L2, a storm-centric radar dataset derived from NEXRAD Level-II and GridRad-Severe data. We algorithmically crop a fixed, high-resolution (250 m) window around GridRad-Severe storm tracks, preserve the native polar geometry, and provide temporally consistent sequences of both per-tilt sweeps and a pseudo-composite reflectivity product. The dataset comprises thousands of storm events across the continental United States, packaged in HDF5 tensors with rich context metadata and reproducible manifests.

Effective airfoil geometry optimization requires exploring a diverse range of designs using as few design variables as possible. This study introduces AirDbM, a Design-by-Morphing (DbM) approach specialized for airfoil optimization that systematically reduces design-space dimensionality. AirDbM selects an optimal set of 12 baseline airfoils from the UIUC airfoil database, which contains over 1,600 shapes, by sequentially adding the baseline that most increases the design capacity. With these baselines, AirDbM reconstructs 99 % of the database with a mean absolute error below 0.005, which matches the performance of a previous DbM approach that used more baselines. In multi-objective aerodynamic optimization, AirDbM demonstrates rapid convergence and achieves a Pareto front with a greater hypervolume than that of the previous larger-baseline study, where new Pareto-optimal solutions are discovered with enhanced lift-to-drag ratios at moderate stall tolerances. Furthermore, AirDbM demonstrates outstanding adaptability for reinforcement learning (RL) agents in generating airfoil geometry when compared to conventional airfoil parameterization methods, implying the broader potential of DbM in machine learning-driven design.

This work investigates transfer learning strategies to accelerate deep reinforcement learning (DRL) for multifidelity control of chaotic fluid flows. Progressive neural networks (PNNs), a modular architecture designed to preserve and reuse knowledge across tasks, are employed for the first time in the context of DRL-based flow control. In addition, a comprehensive benchmarking of conventional fine-tuning strategies is conducted, evaluating their performance, convergence behavior, and ability to retain transferred knowledge. The Kuramoto-Sivashinsky (KS) system is employed as a benchmark to examine how knowledge encoded in control policies, trained in low-fidelity environments, can be effectively transferred to high-fidelity settings. Systematic evaluations show that while fine-tuning can accelerate convergence, it is highly sensitive to pretraining duration and prone to catastrophic forgetting. In contrast, PNNs enable stable and efficient transfer by preserving prior knowledge and providing consistent performance gains, and are notably robust to overfitting during the pretraining phase. Layer-wise sensitivity analysis further reveals how PNNs dynamically reuse intermediate representations from the source policy while progressively adapting deeper layers to the target task. Moreover, PNNs remain effective even when the source and target environments differ substantially, such as in cases with mismatched physical regimes or control objectives, where fine-tuning strategies often result in suboptimal adaptation or complete failure of knowledge transfer. The results highlight the potential of novel transfer learning frameworks for robust, scalable, and computationally efficient flow control that can potentially be applied to more complex flow configurations.

This paper presents a comprehensive review of the Social Laser Theory (SLT) as a natural extension of the broader framework of Quantum-Like Modeling (QLM). While QLM applies the mathematical formalism of quantum theory . such as Hilbert space representations, interference, and non-commutative observables - to model context-dependent and non-classical phenomena in cognition, decision-making, and social behavior, SLT advances this approach by integrating concepts from quantum field theory. The theory conceptualizes social systems as ensembles of social atoms capable of absorbing and emitting quantized units of social energy. Under conditions analogous to population inversion in physical lasers, external informational stimuli (e.g., media signals or mobilizing rhetoric) can trigger coherence across the population, resulting in large-scale, synchronized collective actions such as protests or ideological shifts. SLT thus provides a formal framework for understanding the amplification and coherence of social energy leading to abrupt phase-like transitions in collective behavior. Beyond its metaphorical appeal, the theory proposes measurable quantities and predictive parameters that may support empirical diagnostics of sociopolitical dynamics. By bridging micro-level psychological processes with macro-level sociological phenomena, SLT extends QLM into the domain of complex social systems, offering a mathematically grounded paradigm for interpreting rapid transformations in contemporary societies.

High-resolution spectroscopy allows one to probe weak interactions and to detect subtle phenomena. While such measurements are routinely performed on atoms and molecules in the gas phase, spectroscopy of adsorbed species on surfaces is faced with challenges. As a result, previous studies of surface-adsorbed molecules have fallen short of the ultimate resolution, where the transition linewidth is determined by the lifetime of the excited state. In this work, we conceive a new approach to surface deposition and report on Fourier-limited electronic transitions in single dibenzoterrylenes adsorbed onto the surface of an anthracene crystal. By performing spectroscopy and super-resolution microscopy at liquid helium temperature, we shed light on various properties of the adsorbed molecules. Our experimental results pave the way for a new class of experiments in surface science, where high spatial and spectral resolution can be combined.

Gyrochronology, a method for dating aged field stars ($\gtrsim$ a few Gyr) based on their rotation rate, has recently been shown to fail for many stars older than the sun. The explanation most often put forth is that a shutdown or mode change in the stellar dynamo leads to a sharp decrease in angular momentum loss in magnetized coronal winds. In this paper, we explore an alternate possibility, namely a collapse of the wind itself through a reduction of coronal heating. We show that in the low coronal temperature ($T_0$) limit, even at solar-like low rotation rates ($\Omega$) and coronal magnetic field strength ($B_{r0}$), magnetocentrifugal effects are important and preclude expression of the mass and angular momentum loss rates as power-laws of $T_0$ or $\Omega$ when $T_0$ drops below $\simeq 1.5\,$MK. Mass loss is found to scale linearly with power input into the wind at all coronal temperatures. Introducing an ad hoc power law relationship $T_0\propto B_{r0}^\sigma$ while retaining the “standard” dynamo relationship $B_{r0}\propto\Omega$, we show that reproducing the observed break in gyrochronology requires an exponent $\sigma\gtrsim 1.5$, with which is associated a drop by over 3 orders of magnitude in power input into the quiet corona. This appears physically unrealistic, given current observations of chromospheric and coronal non-thermal emission in aged solar-type stars.

The origins of consonance in human music has long been contested, and today there are three primary hypotheses: aversion to roughness, preference for harmonicity, and learned preferences from cultural exposure. While the evidence is currently insufficient to disentangle the contributions of these hypotheses, I propose several reasons why roughness is an especially promising area for future study. The aim of this review is to summarize and critically evaluate roughness theory and models, experimental data, to highlight areas that deserve further research. I identify 2 key areas: There are fundamental issues with the definition and interpretation of results due to tautology in the definition of roughness, and the lack of independence in empirical measurements. Despite extensive model development, there are many duplications and models have issues with data quality and overfitting. Future theory development should aim for model simplicity, and extra assumptions, features and parameters should be evaluated systematically. Model evaluation should aim to maximise the breadth of stimuli that are predicted.

The ability to control the spatial distribution of light, particularly in deep sub-wavelength areas, is important for a range of materials science, microscopy, and communications applications. Separately, materials science and communications rely on the ability to temporally shape the evolution of electromagnetic pulses. In this work we investigate theoretically the propagation of ultrafast pulses inside hyperbolic metamaterials-based photonic funnels, which have been recently used to achieve deep subwavelength (wavelength/30) concentration of monochromatic mid-infrared light. By analyzing the complex spatio-temporal dynamics of the pulse-funnel interaction, we show that photonic funnels, in general, broaden bandwidth-limited ultrafast Gaussian pulses. We demonstrate that this broadening can be mitigated by pre-chirping the incoming light, realizing simultaneous intensity enhancement and spatio-temporal compression of mid-wave IR light in the all-semiconductor "designer metal" funnel platform. Our analysis suggests that, in combination with linear chirp, designer-metal-based photonic funnels can be utilized with 100 fs bandwidth- and diffraction-limited pulses to produce wavelength/30-scale signals of ~200 fs duration, with intensity enhancement on the order of 5. Lowering material absorption can further enhance the peak intensity. The results presented can be used to assess the perspectives of ultrafast sub-diffraction light manipulation in other portions of the electromagnetic spectrum by adjusting the (meta)material composition of the funnels.

Metasurfaces composed of subwavelength nanostructures enable simultaneous control of polarization and wavefront, greatly enhancing holographic information capacity. Building on this capability, we extend holography into the quantum domain by experimentally realizing Bell-state holograms-distinct holographic images encoded in polarization-entangled Bell states of photon pairs. A polarization-multiplexed dielectric metasurface generates spatial modes conditioned on both input and output polarizations, entangling the holographic pattern with the two-photon state. To characterize these quantum holograms, we further develop quantum hologram tomography, reconstructing the full density matrix of the holographic state pixel by pixel. The reconstructed density-matrix hologram reveals tailor-made holographic symbols attached to individual Bell states through the metasurface, with contrast built up among the different Bell components as theory shows. This framework unifies metasurface photonics with quantum-state reconstruction and provides a scalable route toward high-dimensional quantum communication, encryption and information processing based on holographically encoded quantum light.

Shadow molecular dynamics provide an efficient and stable atomistic simulation framework for flexible charge models with long-range electrostatic interactions. While previous implementations have been limited to atomic monopole charge distributions, we extend this approach to flexible multipole models. We derive detailed expressions for the shadow energy functions, potentials, and force terms, explicitly incorporating monopole-monopole, dipole-monopole, and dipole-dipole interactions. In our formulation, both atomic monopoles and atomic dipoles are treated as extended dynamical variables alongside the propagation of the nuclear degrees of freedom. We demonstrate that introducing the additional dipole degrees of freedom preserves the stability and accuracy previously seen in monopole-only shadow molecular dynamics simulations. Additionally, we present a shadow molecular dynamics scheme where the monopole charges are held fixed while the dipoles remain flexible. Our extended shadow dynamics provide a framework for stable, computationally efficient, and versatile molecular dynamics simulations involving long-range interactions between flexible multipoles. This is of particular interest in combination with modern artificial intelligence and machine learning techniques, which are increasingly used to develop physics-informed and data-driven foundation models for atomistic simulations. These models aim to provide transferable, high-accuracy representations of atomic interactions that are applicable across diverse sets of molecular systems, which requires accurate treatment of long-range charge interactions.

Microtubules capture chromosomes during mitosis by stochastically switching between growth and shrinkage at catastrophe events. They display strikingly rich biochemistry and dynamics, regulated by a stabilizing cap with distinct conformational states. Microtubule lengths at catastrophe are observed to follow a peaked distribution, while their growth "stutters" briefly before catastrophe. Such complexity makes it hard to capture all these observations without a large number of tunable parameters. Here, we introduce a topological model of the microtubule cap that reproduces the features above through dynamical edge states, that provides a minimal description with just two free parameters. Our approach further provides an analytical description of catastrophes and allows the same features to persist over a wide range of tubulin concentration, consistent with experimental observations.

Public discourse emerges from the interplay between individuals' willingness to voice their opinions and the structural features of the social networks in which they are embedded. In this work we investigate how choice homophily and triadic closure shape the emergence of the spiral of silence, the phenomenon whereby minority views are progressively silenced due to fear of isolation. We advance the state of the art in three ways. First, we integrate a realistic network formation model, where homophily and triadic closure co-evolve, with a mean-field model of opinion expression. Second, we perform a bifurcation analysis of the associated Q-learning dynamics, revealing conditions for hysteresis and path dependence in collective expression. Third, we validate our theoretical predictions through Monte Carlo simulations, which highlight the role of finite-size effects and structural noise. Our results show that moderate triadic closure can foster minority expression by reinforcing local cohesion, whereas excessive closure amplifies asymmetries and entrenches majority dominance. These findings provide new insights into how algorithmic reinforcement of clustering in online platforms can either sustain diversity of opinion or accelerate its suppression.

Low power consumption is critical for smart windows for temperature control and privacy. The recently discovered ferroelectric nematic liquid crystals exhibit strong coupling of the ferroelectric polarization with electric fields, making them promising candidates for energy-efficient electrochromic devices. Here we investigate the electrochromic properties of a room temperature chiral ferroelectric nematic liquid crystal in films with in-plane electrodes, where the electric field is perpendicular to the helical axis. We demonstrate that smart windows based on this material can regulate interior temperatures within a 10 Celsius range using only 50 milliwatt per square meter specific power, achieving fifty percent larger temperature modulation and 50-100 times lower power consumption than polymer dispersed and polymer stabilized liquid crystal windows. These findings suggest that chiral ferroelectric nematic liquid crystals offer a highly efficient approach for smart window applications, potentially surpassing existing electrochromic technologies in energy efficiency and thermal regulation.

We discuss the most general form of the Lorentz transformation in 1+1 dimensional spacetime, focusing mainly on its superluminal branch. For this purpose, we introduce the 2-velocity of a reference frame and the clockwork postulate. Basic special relativity effects are discussed in the proposed framework. Different forms of the superluminal Lorentz transformation, which were studied in the literature, are critically examined from the perspective of our formalism. Counterintuitive features of the superluminal Lorentz transformation are identified both in our approach and in earlier studies.

FeynCraft is a browser-based game that is designed to teach players the particle interactions of the Standard Model of particle physics, and how to link these interactions together to produce valid Feynman diagrams. It is primarily targeted at undergraduates and lecturers in introductory courses in particle physics, but we anticipate that it should also be useful for school pupils and teachers studying the basics of particle physics, and perhaps also current researchers. Users may draw particle lines and link them together to form vertices and complete diagrams, and FeynCraft determines invalid vertices using a sequence of simple rules, showing users which vertices are invalid and why. Diagrams may be drawn that involve both fundamental Standard Model particles and hadrons (where hadrons are represented by their constituent quark content). Users can also be presented with a process for which they must draw valid Feynman diagrams – FeynCraft is able to generate such 'problems' at random, but there is also the facility to create, share, import and solve curated sets of problems. Alternatively, one is able to specify the process, and ask FeynCraft itself to generate the Feynman diagrams. Finally, we include several overlay options that give more information on a Feynman diagram (e.g. QCD colour flow, interaction strengths), and the option to export a drawn diagram as LaTeX code.