Papers

We investigate the structural information retained in the distribution of explosion ion trajectories from proteins subjected to strong ionization. Using molecular-dynamics simulations and machine-learning analysis benchmarked against an analytical approach, we show that low-resolution structural information can be retrieved from the explosion distributions alone. An ensemble of convolutional neural networks trained on simulated spherical ion maps recovers the radius of gyration and the three semi-axes of an ellipsoidal molecular envelope with prediction errors of 1.2 Å and 1.5 Å, respectively, while an analytical ellipsoid charge model returns similar estimates and performs best for globular structures. We further investigate how higher-level structural information and symmetries can be extrapolated from ion measurements. This study supports the viability for structural determination of single proteins without large-scale X-ray facilities.

Materials with full anisotropic thermal conductivity tensors are widely used in advanced applications, creating a need for dedicated characterization. Such tensors contain six independent components: three diagonal conductivities and three non-zero off-diagonal couplings. However, whether thermoreflectance measurements on a single crystalline orientation can uniquely determine an arbitrary tensor remains unclear. Here, we show that every fully anisotropic thermal-conductivity tensor admits a Schur-equivalent tensor with zero cross-plane couplings ($k_{xz}=k_{yz}=0$). The two tensors produce identical surface thermal responses, and this equivalence persists as frequency, delay time, beam geometry, and spatial offset vary. We further demonstrate that parameter-identifiability assessments based on heuristic interpretations of sensitivity trends or singular-value decomposition (SVD) of the sensitivity matrix cannot, by themselves, rule out globally equivalent solutions in fully anisotropic systems. Combining systematic analysis with numerical validation, we provide guidance on selecting signals and measurement orientations for full-tensor characterization. Specifically, measurements at three mutually orthogonal crystallographic orientations provide complementary information for full-tensor recovery. These findings clarify the limits of single-surface measurements and establish a systematic approach to characterizing fully anisotropic thermal-conductivity tensors.

NASICON materials are promising solid electrolytes for room-temperature sodium solid-state batteries and are typically synthesized via solid-state reaction. While sintering has been extensively studied, the effect of calcination on electrolyte properties remains poorly understood. In this work, the temperatures at which the NASICON phase forms in Na3Zr2Si2PO12 and Na3.4Zr2Si2.4P0.6O12 are identified. Calcination temperature is then varied between 900 {\deg}C and 1200 {\deg}C to obtain powders with different degrees of reaction prior to sintering. Under identical sintering conditions, higher NASICON phase content in the calcined powder is shown to yield denser electrolytes. Non-reactive sintering also improves grain boundary conductivity, increasing it by 130% for Na3.4Zr2Si2.4P0.6O12 and raising total conductivity from 1.60 to 2.95 mS/cm for powders calcined at 900 {\deg}C and 1200 {\deg}C, respectively. Finally, it is shown that phosphorus loss during processing compromises cycling stability against Na metal electrodes, and that adding off-stoichiometric phosphorus resolves this issue, while reaching a critical current density of 5.0 mA/cm^2 and a room-temperature conductivity of 3.82 mS/cm with over 400 hours of stable cycling. Overall, these findings directly relate synthesis and processing conditions to the final material properties and electrochemical performance of NASICON solid electrolytes.

Can attention move a shock across several cells in one update without breaking it? We develop a conservative, fixed grid finite-volume scheme in which a CFL-conditioned attention flux selects upstream information according to the transport required by the current time step. One-dimensional inviscid Burgers transport is used as the central mechanism test: the same learned flux remains reliable in the conventional small-step regime and, with a time step four times larger, preserves sharp shocks while using one stage per update. A standard fifth-order WENO scheme with third-order strong-stability-preserving Runge-Kutta time integration (WENO-5+SSP-RK3) is included alongside controlled Forward Euler comparisons to separate flux selection from time integration. The learned attention shifts upstream with the local transport reach and becomes more selective near shocks; inference-time interventions and retrained ablations show that transport-scale information and state-dependent selection contribute directly to performance. Directional two-dimensional scalar Burgers transport and the one-dimensional shallow-water system then test whether the conservation-scale-selection principle transfers beyond the original scalar setting. The results support attention as a learnable information stencil for conservative large-step shock transport, while identifying finite candidate reach and problem-dependent robustness as the present limits.

Humans see the polarization of light through entoptic percepts arising from the macula's Henle fiber layer, where xanthophyll pigments absorb preferentially across the radiating fibers. From the layer's complex dielectric tensor alone, Maxwell's equations in Berreman $4\times4$ form yield its Mueller matrix, set by four scalars $\{A,B,C,D\}$. Its intensity channel depends only on the dichroic pair $A$ and $B$, which fix the percepts at a maximum contrast $|B|/A\approx0.05$. One relation generates the whole dichroism-mediated family: Haidinger's brushes under a uniform field, their dark arms perpendicular to the $\mathbf{E}$-vector; fractured brushes under spatially varying fields; and $N$-fold brushes under vector-vortex illumination.

Simulating the propagation of a large beam with high spatial resolution is a common challenge in optics due to both computational time and memory demands. While computational time sets a practical limit on what one can simulate, memory resources are an absolute hard limit. In this paper, we report a novel method to overcome a gap in spatial scale between the beam and the relatively small optical disturbance that needs to be modeled, such as those due to particulate contamination. The method splits the entire propagation chain into to two paths: a specular field propagation on coarse grids and scattered fields on fine grids. Especially for the latter, we dynamically apply high-resolution local patches for individual particles. Not limited to particulate contamination, this method is applicable to any tiny discontinuous structures on the beam propagation path without memory overflow triggered.

Post-perovskite is expected to dominate much of the solid mantles of rocky exoplanets, yet iron partitioning between post-perovskite and silicate melt, which controls the compositional evolution and buoyancy of crystallizing magma oceans, is unconstrained at these pressures. We use first-principles molecular dynamics and thermodynamic integration to compute the Fe–Mg distribution coefficient $K_D$ between post-perovskite and (Mg,Fe)SiO$_3$ liquid at 150–600~GPa and 6000–10000~K. Iron is strongly incompatible in post-perovskite and becomes increasingly so with pressure. Combining $K_D$ with equations of state, we find that iron enrichment of residual liquid reverses the solid–liquid density contrast, causing post-perovskite to become buoyant. Basal magma oceans are therefore gravitationally stable in super-Earth exoplanets up to 4 M$_{\oplus}$.

At low redshift, scatter in the Type Ia supernova (SN Ia) Hubble diagram is increased by peculiar velocities (PVs) and correlations between SN distances and their host-galaxy environments, limiting the precision of cosmological distance measurements. We investigate these effects using 1086 spectroscopically-confirmed SNe Ia from the Type Ia Supernova Trove from ATLAS in the Nearby Universe (TITAN) sample in the redshift range $0.01<z<0.05$. We identify 314 group-associated SNe, including 80 SN "cousins" occurring in different galaxies within 32 common groups. We first examine whether using the group-averaged redshifts of SNe in galaxy groups can mitigate PV-induced scatter; however, we find that the Hubble residual scatter increases slightly from $0.184^{+0.017}_{-0.009}$ mag using individual host-galaxy redshifts to $0.200^{+0.016}_{-0.015}$ mag using group-averaged redshifts. For the sample of SN cousins, we next analyze pairwise differences in their distances and compare their scatter to the Hubble residual scatter of the full SN sample. For analysis variants that limit line-of-sight differences in distance modulus between cousins, we find $\sim$30-40% lower scatter from SN cousins at significances of 2.3-2.8$\sigma$. We also find tentative evidence that these differences in scatter may be driven by the youngest groups, with a 2$\sigma$ reduction in scatter for groups with high fractions of spiral galaxies. These results suggest that SN cousins may provide a novel route for increasing SN Ia distance precision and understanding the role of host-galaxy and group properties on SN Ia distance measurements.

In molecular variational quantum eigensolver (VQE) calculations for a fixed electron-number sector, the projected-sector energy alone does not certify that a prepared state is physically valid. We show that a state can have zero projected-sector error while almost all of its probability mass lies outside the target particle-number sector. We therefore evaluate ansatz families using three quantities: projected-sector error, leakage outside the physical sector and parameter count. Our noiseless experiments show that particle-preserving UCC-style families eliminate leakage induced by ansatze, but do not guarantee ground-state accuracy. In the parameter-budget comparison experiments, the one-shot ranking rules select identical generator sequences and yield identical errors. The adaptive hybrid procedure achieves lower mean projected-sector errors at selected budgets. Using established Kraus invariant-subspace criteria, we also express channel-induced leakage through a positive operator and discuss global and local depolarizing noise analytically. This gives a practical diagnostic framework for comparing ansatze beyond energy alone.

Extreme salinity events in Florida Bay are a central concern of the Comprehensive Everglades Restoration Program (CERP). A large-scale seagrass die-off in the late 1980s, driven by hypersaline conditions following reduced freshwater delivery, made reliable salinity prediction essential to restoration planning. The Bay Assessment Model (BAM) is a hydrologic model that simulates salinity across 54 idealized basins representing Florida Bay. It is mass-conservative apart from where required for model stability. Interbasin fluxes are computed from hydraulic gradients across shoals, and each basin receives direct rainfall and evapotranspiration forcing. Along the shoreward boundary BAM is driven by observed water levels from the Everglades Depth Estimation Network (EDEN), or by output from upstream restoration planning models. Along the marine margins, tidal boundary conditions from NOAA subordinate station harmonic constituents are superimposed on a regional mean sea level anomaly. BAM is implemented entirely in Python and is open source. Over the extended 1999-2026 record the domain-wide mean water level bias is -0.024 m with a mean RMSE of 0.092 m, and the domain-wide mean salinity bias is 0.00 ppt with a mean RMSE of 6.21 ppt. Model bias varies systematically with hydrologic regime, increasing in magnitude from drought to wet conditions and reversing sign at one central bay basin, constraining the interpretation of anomaly-based metrics. Applying uniform offsets at the Everglades shoreward boundary yields salinity responses of 2-4 ppt in the northeastern bay, attenuating toward the marine margins; a fuller treatment of restoration and sea level scenarios is reported separately. BAM provides a transparent, efficient, and community-accessible tool for evaluating the salinity consequences of Everglades restoration actions in Florida Bay.

Ensuring the reliability of black-box machine learning models in safety-critical space missions remains a significant challenge, particularly when ground-truth is unavailable for validation. Although machine learning models offer a powerful means to augment standard pipelines by extracting transmission spectra from complex exoplanetary light curves, their susceptibility to unmodelled instrument anomalies, stellar activity, and domain shifts introduces unquantified risks. This study evaluates a modular safety cage architecture that operates as a parallel monitoring layer to assess the validity of a prediction without modifying the underlying estimator. By monitoring different runtime indicators, including uncertainty quantification, out-of-domain detection, and influence functions, the framework constrains the model's operational domain to a verified region. A controlled evaluation is conducted under both in-domain and cross-domain conditions, using datasets from the 2019 and 2021 editions of the Ariel Data Challenges. The results reveal that model failure is multifaceted and that no single indicator captures all failure modes, demonstrating the need for indicator fusion. The application of safety-driven rejection strategies shows that a modest 20% reduction in data coverage results in error reductions between 45% and 65% across different domains and evaluation metrics. Using a formalised coverage-risk framework, a systematic analysis of indicator combinations is performed to identify configurations that maximise risk-ranking accuracy and optimise the trade-off between data coverage and scientific performance. Safety cages provide a transparent mechanism for detecting unreliable predictions and represent a critical step towards the safe deployment of data-driven models in scientific applications, such as astrophysics, where ground truth is seldom available.

NASA's Habitable Worlds Observatory (HWO) will require ultraviolet, optical, and near-infrared instruments that are simultaneously compact, mechanically and thermally stable, high-throughput, and replicable at large channel counts. Space-qualified photonic integrated circuits (PICs) and optical fibres are key platforms that can deliver these properties: they manipulate light at the diffraction limit within micron-scale single-mode waveguide circuits. An astrophotonic instrument fully guides the starlight from focal plane to detector, eliminating scatter and ghosts and offering excellent stability with no moving parts. This paper examines how astrophotonics can address the principal science drivers of HWO through four topics: (I) the HWO observing modes best implemented with photonic instruments; (II) the waveguide and fibre materials that can span the demanding 100nm-2.5{\mu}m HWO wavelength range; (III) the potential of arrayed-waveguide-grating (AWG) spectrographs to reach the resolving powers and throughputs HWO science requires; and (IV) the steps needed to space-qualify PICs and fibres against radiation, thermal cycling, vacuum, and launch loads. For each topic, concrete technical requirements are extracted and the current Technology Readiness Level (TRL) is assessed. The main contribution of this paper is to unify these science drivers, candidate-aperture photon budgets, UV-to-near-infrared material platforms, AWG architectures, and space-qualification requirements within a single photon-budget framework that pairs each claimed benefit with a measurable requirement, a TRL, and an environmental test that can retire it. A phased roadmap advances the critical photonic components from their present TRL~2 to -5, depending on platform and application, to the TRL~6 needed at HWO's instrument-definition gate.

For computing the ground state of the defocusing Gross–Pitaevskii energy, we propose and analyze two efficient schemes based on the Davis–Yin three-operator splitting, which treats the potential and interaction terms explicitly, and the kinetic energy by a resolvent. One iteration costs one inversion of $I-\gamma\Delta$ for the first scheme, and of $I-\gamma\Delta+\gamma V_1$ for the second, with $V_1$ denoting the separable part of the potential, and on structured meshes both operators can be inverted by simple fast GPU solvers. For monotone discrete Laplacians, including the second-order finite difference scheme and the lumped linear finite element method on simplicial meshes with suitable angle conditions, we prove global convergence to the unique positive discrete ground state, for every positive normalized initial vector, for any constant step size below an explicit threshold. In contrast, the methods previously proven to converge globally to the ground state all invert a more difficult elliptic operator that depends on the iteration variable. In three-dimensional tests with up to $999^3$ unknowns on one GPU, a simple variable step size rule makes the proposed splitting schemes efficient in practice, comparable in wall-clock time to Riemannian conjugate gradient methods that also invert only a shifted Laplacian operator, and much more robust with respect to the choice of the initial guess.

Quantum Amplitude Amplification (QAA), the generalization of Grover's algorithm, is well-positioned for combinatorial optimization and is particularly promising for Quadratic Unconstrained Binary Optimization (QUBO) problems. QAA is appealing due to its ability to drive the quantum system to a target state, yielding the globally optimal solution with probability over $90$+%. However, realizing QAA for application-scale optimization currently exceeds quantum hardware capacity, which is further compounded by unresolved algorithmic challenges regarding the choice of free parameters. In this study, we address these issues by utilizing a realistic traffic flow QUBO problem to investigate the implementation of QAA as a heuristic solver. Specifically, allowing the diffusion operator parameter to take non-$\pi$ values expands the capabilities of QAA. This unlocks a new multiple-shot, low-iteration strategy that aims for a high cumulative probability rather than maximizing the probability of a single basis state. Using $25$-qubit simulations, our results demonstrate that heuristic QAA addresses two of the main challenges cited in previous studies. Firstly, heuristic QAA works even at a single iteration, reducing circuit depth by orders of magnitude compared to standard QAA. And secondly, we show that the problem-dependent parameters necessary for reaching optimal algorithmic performance can be reliably approximated with minimal upfront classical computing overhead.

High-order entropy stable (ES) methods have attracted much interest in the computational fluid dynamics (CFD) research community over the past decade, as we seek to simulate practical engineering applications with high accuracy and robustness. As industry delves further into supersonic flight and space travel, we need schemes capable of modelling flows with multiple chemical species that interact and mix within the fluid. As such, there is a considerable amount of effort currently being focused on developing high-order ES schemes for multi-species (MS) flow. A key ingredient for these entropy stable schemes is the two-point flux, which determines the properties of the resulting high-order discretization. As such, the numerical scheme can possess important properties such as entropy conservation and kinetic energy preservation only if the two-point flux also mirrors these properties. At present, in the literature, the two-point fluxes used for high-order ES multi-species solvers are not kinetic energy preserving (KEP). In this short communication we develop a two-point flux that is both entropy conserving (EC) and kinetic energy preserving by adapting Ranocha's single-species methodology for multi-species. The newly proposed EC and KEP flux is tested against existing two-point fluxes in the literature using the density pulse advection and Taylor-Green vortex cases. It is shown that EC and KEP flux retains both properties while the other fluxes only retain one of the desired properties. Since the two-point flux dictates the properties of the resulting scheme, the proposed flux is the ideal option for simulating multi-species compressible flow.

We derive a unified set of scaling equations for Bose-Einstein condensates in time-dependent harmonic traps, connecting the weakly interacting Gaussian regime to the strongly interacting Thomas-Fermi regime. The exact Gross-Pitaevskii ground-state density is taken as a fixed profile in rescaled coordinates, with its dynamics described by three scaling factors corresponding to compression or expansion along the three spatial directions. The resulting equations contain no adjustable parameter, since their coefficients are determined once from the initial state. They recover analytically the Gaussian variational and Thomas-Fermi scaling equations in their respective limits and satisfy an axis-resolved virial theorem. The approach also yields the global phase and the low-lying collective-mode frequencies of the condensate. We benchmark the model against three-dimensional Gross-Pitaevskii simulations and against measured expansion energies. The model remains accurate across all interaction regimes, in strongly anisotropic traps, and along time-dependent sequences including a relatively fast quench, up to the point where the underlying frozen-profile hypothesis, shared by all three scaling approaches, breaks down.

The minimum loss required to reproduce non-Hermitian dynamics depends on how many physical modes each reservoir can address coherently. Established positive-matrix criteria bound this cost. We construct a phase-controlled cubic-root Su-Schrieffer-Heeger chain in which three-mode reservoirs attain the unrestricted passivity threshold throughout its phase diagram and halve the minimum pair-supported loss at a tuned point. The bound constrains exact conditional trajectories, including time-dependent Markov controls, rather than preparation of one final state. A connected-chain protocol tests effective reservoir support through calibrated finite-time emission. Full system-auxiliary propagation then bounds conditional fidelity and target yield for every one-particle input. A coherent controller that accurately prepares the selected endpoint fails this propagator test. A singular-value bound excludes every unitary realization of the same conditional map. These results connect local reservoir access to a measurable loss cost for prescribed dynamics.

In situ monitoring of reactions with chemical specificity and nanometer-scale spatial resolution under aqueous conditions remains a long-standing experimental challenge. Here, we use thin SiC membrane-enabled liquid-phase infrared nanoimaging and nanospectroscopy to monitor Ca$^{2+}$/Na$^{+}$ ion exchange in a mixture of disodium terephthalate and CaCl$_2$ solution. Beneath the membrane, we follow the formation of calcium terephthalate (CaTP), a metal-organic framework (MOF)-type coordination phase in which terephthalate ligands link Ca$^{2+}$ centers. The symmetric and asymmetric carboxylate stretching modes serve as chemically specific near-field reporters of Ca$^{2+}$ coordination to the terephthalate linkers. Nano-FTIR spectra reveal pronounced peak shifts and linewidth changes in these vibrational modes, indicating modification of the carboxylate coordination environment during CaTP formation. Complementary near-field nanoimaging resolves the emergence of nanoscale precipitates beneath the SiC membrane following CaCl$_2$ injection, providing spatially resolved evidence of the formation of the CaTP coordination framework. Solid-phase nanoimaging and nanospectroscopy identify well-defined CaTP particles, providing further evidence for crystalline CaTP formation. We demonstrate that the chemistry of metal-ligand interactions can be monitored in situ via liquid-phase near-field nanoscopy. This capability could enable the study of catalytic, electrochemical, and biological processes under native aqueous conditions.

Transverse light localization of Anderson type through disorders has made intriguing fundamental topics. Toward this, we found that in the disordered porous Si (PSi) a nearly "zero reflection" (from 450 to 900 nm) can be observed. The localization length versus disorder parameter is experimentally visualized and calculated based on a self consistent theory and solution of stochastic Helmholtz equation for electric fields traveling in a transverse disordered media with the results determined to be in a reasonable agreement with experiments. Based on these calculations for different pore diameters in a constant order parameter, diffusion length can vary from 260 nm to 2.4\mu m, accounting as controllability of such structure for further applications in photonic devices. Reflection from ordered PSi structure are evaluated that shows no similar responses to those seen in disordered PSi.

Time-dependent mantle-dynamics inversion must address the high dimensionality of the initial state, nonlinear rheology, and gradient propagation through long-term thermo-mechanical evolution. We develop ADEPTS, a two-dimensional staggered-grid finite-difference framework for mantle-dynamics inversion based on automatic differentiation. The forward model solves incompressible Stokes flow, temperature advection-diffusion, and compositional advection with temperature- and strain-rate-dependent viscosity and plastic yielding. For the nonlinear Stokes system, we compare two gradient strategies: unrolled differentiation through a fixed number of Picard iterations and implicit differentiation of the converged discrete residual equations. Numerical experiments show that unrolled differentiation remains stable even when the nonlinear solve is not fully converged, whereas implicit differentiation requires sufficiently accurate nonlinear solutions; otherwise gradient consistency and optimization convergence deteriorate. With sufficiently converged solves, implicit differentiation recovers accurate gradients and reconstruction quality comparable to unrolled differentiation. Joint thermo-chemical twin experiments show that ADEPTS can simultaneously recover a high-dimensional initial temperature field and low-dimensional physical parameters, including compositional density, reference viscosity, and stress exponent, while fitting final-time temperature, surface horizontal velocity, and surface normal stress. These results demonstrate the feasibility of differentiable time-dependent mantle-dynamics inversion and clarify the different convergence requirements of unrolled and implicit differentiation.

We study the far-from-equilibrium dynamics of domain-wall initial states in the integrable lattice Landau–Lifshitz spin chain. Building on the linear spin-wave description of domain-wall spreading, we derive the long-wavelength magnon dispersion directly from the microscopic integrable Hamiltonian and use it to characterize the propagation and broadening of the resulting wavefronts. The dispersion interpolates between a quadratic form at the isotropic point and a linear form in the easy-plane regime. In the isotropic limit, the quadratic dispersion leads to diffusive broadening, whereas in the easy-plane regime the domain-wall front propagates ballistically. Keeping the leading nonlinear correction to the linear dispersion gives a cubic dispersive term, which produces a characteristic $t^{1/3}$ broadening of the ballistic front. We further show that the sign of this cubic correction changes at a critical anisotropy, leading to a reversal of the dispersive wake from trailing to advancing. The resulting critical point occurs at $\Delta_c=1/7$, corresponding to $\gamma_c\simeq 1.047$. Numerical simulations of the full nonlinear spin dynamics confirm the analytical scaling and demonstrate the coexistence of the amplitude-independent spin-wave front with amplitude-dependent nonlinear soliton propagation.

We present a Godunov-type mesh-free hydrodynamics scheme for the numerical simulation of weakly compressible single- and multiphase flows. The method employs a consistent second-order spatial reconstruction of the primitive variables at the inter-particle interface, applies slope limiting, and solves the moving Riemann problem using a subsonic HLLC approximate Riemann solver. Particles are advected with a quasi-Lagrangian material velocity incorporating an inherent self-relaxation mechanism that suppresses the formation of anisotropic particle distributions. Near material interfaces, one-sided renormalized gradients are used to handle density discontinuities and slope limiting is applied for each phase independently. The Lagrangian character of material interfaces is preserved by constraining the relaxation mechanism to act tangentially to the interface. For same-phase particle interactions, the Riemann problem is centered at the arithmetic mean of the two material velocities. For cross-phase interactions, it is centered at the contact-wave velocity. This choice naturally eliminates spurious mass fluxes across phase boundaries. The methodology is validated on a set of challenging benchmarks, including the inviscid Taylor-Green vortex, material interface advection, surface gravity waves near hydrostatic equilibrium, the Kelvin-Helmholtz instability, and the Rayleigh-Taylor instability under gravitational stratification.

Cells in connective tissues reside within the extracellular matrix (ECM), which consists of a fibrous mesh that exhibits non-linear strain-stiffening behavior, driven by a transition from bending-to-stretching-dominated deformation. While bulk rheology captures macroscopic mechanical properties, cells actively sense and respond to local microscale heterogeneities and stiffness anisotropy in their environment. Characterizing ECM micromechanics is therefore essential for understanding the mechanical cues experienced by cells. This study quantifies local stiffness anisotropy in stretched fibrous gels by combining experimental and numerical approaches. Experimentally, we utilized optical tweezers microrheology to measure local stiffness in fibrin gels subjected to uniaxial stretch. The gels demonstrated gradual local stiffening along both the tensile and perpendicular axes, with a more profound increase along the tensile axis, resulting in local anisotropy. To investigate the physical parameters driving this phenomenon, we developed a 3D finite element model of a discrete random fiber network, successfully replicating the experimental local stiffening and anisotropy. Numerical analysis further revealed that within the sub-isostatic region, both fiber thickness and network connectivity strongly influence local anisotropy: slender fibers and higher connectivity amplify the anisotropy by up to an order of magnitude. This contributes to the formation of a highly anisotropic local environment, thereby playing a significant role in directing mechanically driven biological processes, such as cell migration and durotaxis. Our simulations also indicate that local micromechanical responses may differ from the material's global stiffening behaviors, highlighting the need for characterization at the microscopic scale.

The inverse design of crystalline materials ultimately seeks structures with desired physical properties. However, for many functional responses, a favorable numerical value is meaningful only when supported by the symmetry of the underlying crystal. Without the appropriate crystallographic constraints, an apparent response may be ill defined, accidental, or not symmetry protected. Here we introduce SPARC, a symmetry- and property-aware reinforcement learning framework that optimizes physical objectives while preserving the structural conditions required for their realization. We demonstrate SPARC on two complementary tasks. The first targets strong uniaxial dielectric anisotropy, a tensorial response that is well defined only within appropriate crystal classes. The second maximizes the spectroscopic limited maximum efficiency, a scalar device-level objective without a prescribed symmetry class, allowing the framework to identify favorable crystallographic motifs. These results show that symmetry is not merely an additional design constraint, but a physical foundation for generating candidates with meaningful, robust, and realizable functional properties.

Laser Powder-blown Directed Energy Deposition (LP-DED) offers flexibility for process and materials and high productivity (~5 kg/h), but process-induced pores often compromise mechanical properties. This study utilizes in-situ X-ray synchrotron imaging to compare pore formation mechanisms in Ti-6Al-4V (Ti64) and stainless steel 316L (SS316L), focusing on the interplay between particle dynamics and thermophysical properties. Four distinct pore formation mechanisms were identified, with most large pores originating from the closure of cavities formed behind incident particles impinging on the melt pool. High Weber number (We >> 1) governs this behavior, indicating that particle inertia, rather than thermophysical property differences, is the primary driver of large pore formation. The study demonstrates that increased energy density leads to larger melt pool volumes, facilitating deeper particle penetration. This greater penetration depth directly correlates with increased pore diameters. While thermophysical properties secondarily influence pore-formation frequency and cavity symmetry, particle inertia remains the dominant factor. These findings provide a physically grounded basis for understanding and controlling porosity in powder-blown DED processes.

Refractory body-centered cubic (BCC) alloys are attractive candidates for structural applications at extreme temperatures, yet combining room-temperature ductility with high-temperature strength remains the unsolved challenge. Ductility in crystals requires that dislocations emit from a sharp crack tip before brittle cleavage, but continuum theories that treat disordered alloys as chemically homogeneous incorrectly predict brittleness for many experimentally ductile alloys. Here we show that atomic-scale stress fluctuations in disordered alloys create an additional local stress intensity at the crack tip, enabling dislocation loop nucleation below the cleavage threshold. Accounting for these fluctuations yields a local ductility criterion where alloys deemed brittle by conventional fracture mechanics can be intrinsically ductile. Atomistic simulations with machine-learned interatomic potentials and an analytic fracture mechanics model correctly predict composition-driven brittle-to-ductile transitions in binary and ternary Mo-Nb-Ti alloys, in Nb-Ti alloys at 4 K, and in several commercial BCC alloys at room temperature. Guided by this criterion, we predict, fabricate, and test the Hf$_{15}$Mo$_{15}$Nb$_{32}$Ti$_{38}$ alloy, confirming its room-temperature ductility. This fluctuation-driven mechanism provides a quantitative basis for designing ductile multicomponent BCC alloys.

Inverse design is a powerful computational tool for generating nanophotonic devices that should outperform their traditionally designed counterparts. However, translating these theoretical gains into physical devices is challenging because the irregular and non-intuitive features that make inversely designed structures so effective also make them exceptionally difficult to fabricate, typically necessitating substantial process optimization unique to each device design. We describe a versatile two-step nanofabrication process that achieves pattern transfer fidelity in excess of 90% for each of three distinct inverse device designs with no device-specific process optimization.

Non-Markovian quantum-state diffusion (QSD) provides a microscopic trajectory description of open-system dynamics, but trajectories associated with a chosen initial state are not directly reusable as quantum channels. We develop a constructive framework that converts the QSD propagator into an input-independent Kraus channel for a fixed bath preparation and a selected evolution time. The construction incorporates reservoir memory through a finite-mode approximation of the bath covariance and yields a deterministic channel representation that can be compressed for approximate implementation with a system-ancilla circuit. For the qubit systems considered here, the construction closes exactly in vacuum and extends systematically to finite temperatures. Two-qubit examples with structured reservoirs validate the vacuum channel construction, illustrate its finite-temperature extension for a chosen initial state, and show that the complexity of the reservoir description can differ substantially from the ancilla space required for quantum realization. The framework therefore establishes a direct bridge from microscopic reservoir memory to reusable non-Markovian quantum simulation and provides a foundation for optimized hardware implementations and extensions to larger systems and multitime dynamics.

We present the stellar parameters for 59,266 high-quality spectra of 24,130 unique stars from the MaNGA Stellar Library (MaStar) in the Sloan Digital Sky Survey (SDSS) Data Release 17 (DR17). The median signal-to noise ratio per pixel of the spectra is 96. We derive four stellar parameters, effective temperature (Teff), surface gravity (log g), metallicity ([M/H]), and {$\alpha$}-enhancement ratio ($\rm [\alpha/M]$), by comparing the data with BOSZ (ATLAS-9 based) and MARCS theoretical atmospheric models. We adopt a Bayesian method and use color and absolute magnitude derived from Gaia to select a subset of theoretical models for each star. We then perform full-spectrum fitting to estimate the likelihood of each model in the subset and then compute their likelihood-weighted mean parameters as the final parameters. We set stellar-parameter quality flags to facilitate the use of the derived stellar parameters. The MaStar stellar parameters derived herein span an effective temperature range of $\rm 2,600 \leq T_{eff} \leq 29,861 K$, a surface gravity range of $0 \leq \log g \leq 5.5$, a metallicity range of $-4.9 \leq \rm[M/H] \leq 1.0$, and an {$\alpha$}-abundance range of $\rm -0.98 \leq [\alpha/M] \leq 1.0$. We compare these parameters with those from APOGEE and Gaia for stars in common, finding general consistency within the uncertainties. However, some artifacts and systematic differences are present, and we discuss their potential causes. These new stellar parameters are available through the MaStar SDSS-IV DR17 value-added catalog (https://www.sdss4.org/dr17/mastar/mastar-stellar-parameters/).

Integrated modelling of the Mega Ampere Spherical Tokamak Upgrade (MAST-U) indicates that turbulent electron heat transport can dominate over ion transport across a range of operating regimes. Only a limited set of instabilities are able to produce this behaviour, with the primary candidates being microtearing modes (MTMs) and electron temperature gradient (ETG) driven modes. This work investigates electron-scale core transport in two L-mode plasmas and one H-mode plasma on MAST-U using local gyrokinetic analysis. The linear and nonlinear sensitivities of ETG modes are examined, with particular focus on their dependence on electron temperature gradients and ExB shearing rates. In L-mode discharges, ETG modes are found to be linearly unstable over a broad radial region and can drive experimentally relevant levels of electron heat transport, particularly towards the outer core. The transport is strongly sensitive to both the electron temperature gradient and the ExB shear, with nonlinear simulations showing stiff transport and good agreement with experimental estimates within uncertainty. Towards the core, however, ETG-driven transport is reduced and is insufficient to fully explain the observed anomalous heat flux. In contrast, in the H-mode plasma ETG modes are found to be stable or only weakly unstable in the core, resulting in negligible electron-scale transport. This is attributed to higher equilibrium pressure gradients and reduced electron temperature gradients, both of which stabilise ETG turbulence. Following the collapse of the core rotation profile, ETG modes can become unstable towards the outer core, but typically remain insufficient to account for the full level of transport. Overall, ETG turbulence can play a significant role in setting electron heat transport in L-mode plasmas, particularly at larger radii, but is unlikely to dominate in high-performance H-mode conditions.