Papers

Accurate quantum number assignment for calculated molecular energy levels is a critical bottleneck in generating line broadening parameters for comprehensive line lists for radiative transfer applications. We present an automated pipeline for assigning Air Force Geophysics Laboratory (AFGL) quantum numbers to CO2 calculated rovibrational states lying below 15,000cm$^{-1}$ across all 12 stable isotopologues. A GraphSAGE graph neural network is trained transductively on empirical (MARVEL) energy levels, exploiting inter-isotopologue perturbation chains and intra-isotopologue rotational ladder edges to propagate assignment information to unlabelled calculated states. Physical uniqueness is enforced locally by a Hungarian algorithm solver operating within groups of states sharing the same polyad, rotational quantum number, and parity. A five-generation bootstrap loop iteratively promotes high-confidence predictions into the training set, expanding coverage without additional labelling effort. The pipeline assigns 224,650 previously unlabelled states over 12 isotopologues, accounting for 10.7$\%$ of all available states (including MARVEL-derived levels), with coverage now increased to 97.4$\%$ below 5000cm$^{-1}$. The pipeline includes a novel decision tree method for converting AFGL to Herzberg notation in asymmetric isotopologues, while the architecture and Hungarian uniqueness enforcement are applicable beyond CO2, any molecular system with a conserved polyad-like quantum number and a large body of unlabelled computed states is a natural target for this approach, suggesting a pathway toward automated quantum number annotation for the next generation of large-scale computed line lists.

Cohesive zone models are widely used to describe fracture and interfacial failure, yet most formulations prescribe problem-specific analytical traction-separation laws together with phenomenological rules for unloading and reloading, leading to specialized models for different cohesive behaviors. This work develops a unified learnable cohesive formulation within the generalized standard materials framework, in which the response is generated from learned constitutive functions while the underlying thermodynamic structure remains fixed. The surface free energy is decomposed into active and contact contributions, and irreversible damage evolution is governed by a learned mode-dependent damage resistance. The active energy is represented by an input-convex neural network, while the inverse damage resistance is represented by a monotone neural network. Convexity, monotonicity, normalization, and damage irreversibility are incorporated directly into the constitutive representation. Direct parameterization of the inverse resistance yields an explicit damage update and avoids local nonlinear inversion during constitutive evaluation. Material-point studies show that the formulation can represent qualitatively distinct cohesive responses, including plateaus, extended softening tails, irregular softening, nonlinear unloading, distinct Mode I and Mode II behaviors, and several classical mixed-mode cohesive laws. The framework therefore replaces law-specific model construction with a single thermodynamically structured representation capable of learning cohesive responses of broad functional complexity from data.

First-principles, electronic-structure simulations provide direct access to ground-state, global system properties. In the context of computational electrochemistry these are, e.g., bias-dependent energies, free energies, work functions, and capacitances. However, a comparably systematic local description of how these quantities respond to biasing via interfacial charging is still missing. Here, we introduce the screened electrochemical Born effective charge (SEBEC), a mixed total-energy derivative that measures the force response of an atomic degree of freedom to interfacial charging under electrochemical boundary conditions. SEBECs play the role for electrochemistry that Born effective charges play in the modern theory of polarization: they provide an unambiguous decomposition of global response and generate the same formal structures for structural relaxation, capacitance, and electrochemical Stark tuning that are familiar from solid-state response theory. The broader implication is a local response framework that brings electrochemical interfaces closer to the conceptual rigor long established for insulating solids.

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.

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.

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.

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.

Knot theory has provided a rich topological taxonomy for band structures, but its reach is fundamentally limited: knot invariants classify only 1D nodal lines at gap closure, and cannot encode the full dispersion or rich Fermi surface structure of realistic materials. Here we show that knotted graphs (knots that admit graph-like intersections in 3D space) - which have so far been elusive in condensed matter literature - provide a unified topological language for classifying the entire band dispersion, and even the eigenstate topology in some contexts. We propose a new framework beyond the existing Yamada polynomials that can topologically characterize the intricacies of realistic Fermi surfaces completely, crucially including how their multiple disconnected pieces are nested. This yields the Yamada set, a boundary-resolved extension which organizes the full topological evolution across energy into a Yamada sequence: a compact dispersion-level fingerprint directly tied to experimental signatures of Lifshitz transitions. Our framework is demonstrated with DFT-based band structures of real materials. Beyond dispersion-level classifications, this framework can be extended to non-Hermitian exceptional surfaces, where Berry-curvature flux further equips the knotted-graph skeleton with a directed Abelian edge flow that also captures the eigenstate topology.

Multireference electronic-structure calculations remain difficult to automate because critical workflow decisions, including active-space selection, state averaging, convergence recovery, and state identification, traditionally rely on expert judgment. Here, we investigate whether an autonomous large language model (LLM) agent can perform these tasks without human intervention. The agent selects active spaces using literature-grounded analogies or explicitly documented chemical reasoning, generates and submits ORCA calculations, analyzes outputs, and records all decisions in an auditable reasoning log. Benchmarking against 558 vertical transition energies (VTEs) from QUESTDB shows that an unguided baseline agent achieves 24.9% coverage with a mean absolute error (MAE) of 0.373 eV. Introducing a structured decision ladder increases coverage to 44.1% while reducing the MAE to 0.339 eV. The largest gains are observed for double and Rydberg excitations, demonstrating that expert-informed procedural guidance substantially improves active-space construction and state identification. When provided with complete workflow information, the agent successfully reproduces published QUEST calculations with an MAE of only 23 meV, resolving 75% of target configurations within seven attempts. These results demonstrate that contemporary LLM agents can autonomously execute and reproduce complex multireference quantum-chemical workflows, while highlighting the importance of structured reasoning frameworks for achieving reliable high-throughput and high-fidelity electronic-structure calculations.

Kohn-Sham density functional theory (DFT) remains the workhorse of ab initio materials simulation, yet cubic computational and quadratic memory scaling have confined calculations to a few hundred to thousands of atoms, spanning only nanometers, far below experimentally relevant length scales. We introduce XLSDFT, a linear-scaling DFT framework based on divide-and-conquer decomposition of the one-particle density matrix and Chebyshev-filtered subspace iteration, achieving linear computational and memory scaling while retaining DFT accuracy. Deployed on the LineShine exascale supercomputer, XLSDFT reduces computational complexity by orders of magnitude, enabling unprecedented DFT scale: a 200-million-atom silicon crystal, twentyfold beyond the prior record. Our implementation achieves 96.6% weak-scaling efficiency and sustained 157.9 Pflop/s (FP64) for a 100-million-atom scaling study. We further simulate an 11-million-atom all-solid-state battery interface of unprecedented complexity, 1,000 times beyond prior DFT for such systems, revealing how lithium metal reacts with the solid electrolyte at atomic resolution, in quantitative agreement with spectroscopy experiments.

The 3D digital reconstruction of multi-scale, heterogeneous porous media is traditionally limited by the resolution boundaries and artifacts of X-ray computed tomography (XCT). This paper introduces a physically consistent 2D to 3D pore space synthesis framework that supersedes conventional geometric shuffling with dynamic Newtonian gravitational deposition using a high-performance rigid-body mechanics engine. To honor strict physical contact mechanics, statistical 2D targets, and morphological constraints, we develop a two-stage dynamic packing pipeline that combines realistic 3D grain shapes with a virtual sub-particle insertion engine. The framework is validated against synthetic and natural core benchmarks. First, we show that our stereological integral equation systematically converts size distributions; bypassing this inversion step induces a systematic 50% deviation in permeability predictions despite identical total porosities, independently altering flow channel topology. Second, temporary virtual spheres serve as architectural placeholders to stabilize large non-equilibrium dissolution voids, ensuring the reconstructed model satisfies target flow metrics within the prescribed tolerance. Finally, when applied to a fine-grained, weakly consolidated sandstone, the workflow exposes and suppresses systematic XCT artifacts, including phantom internal porosity. Ensembles of two-point correlation functions demonstrate that the hydrodynamically and structurally synthesized digital twins quantitatively exhibit closer structural proximity to the uncorrupted rock geometry than the reference XCT dataset itself. This methodology establishes a scalable foundation for high-fidelity multi-phase rock reconstruction, with potential extensions toward complex amorphous cement phases.

Transient glacier simulations with higher-order ice flow require the repeated solution of a nonlinear problem as the geometry evolves. In the online mode of the Instructed Glacier Model, the velocity field is represented by a neural network whose weights are warm-started from the previous time step and updated with a few optimizer iterations. We show that supplying the network with inexpensive input fields derived from low-order ice-flow balances improves this online solver. Unlike residual-based physics-informed neural networks, which incorporate physics through governing-equation penalties in the loss, our approach leaves the governing energy objective unchanged, adding physical structure through the network inputs. Across three real-world glacier configurations, the enriched solver is markedly more robust to solver settings. On the two alpine cases, it also improves the tuned accuracy–runtime trade-off, reducing surface-velocity errors by factors of two to four at fixed runtime and reaching few-percent relative errors with only $10^4$–$10^5$ trainable parameters, far fewer than comparable raw-input baselines. A 300-year Aletsch simulation then completes in under one minute, and the larger Valais domain in about two minutes, on a single GPU—a budget once reserved for much simpler shallow-ice models. Gains are smaller for the fast marine-terminating glacier, where nonlocal stress coupling favors larger or spectral networks. More broadly, the results suggest that enriching a neural solver's inputs with reduced-order physics can make repeated higher-order solves much cheaper, with no training data and no offline training.

Time evolution of tensor-network states is a central tool for studying nonequilibrium dynamics in strongly correlated quantum systems. In the past decade, using the time-dependent variational principle (TDVP) discretized by projector-splitting has become the gold standard for evolving matrix product states (MPS) with long-range Hamiltonians. More recently, a conceptually different class of discretizations of the TDVP, the class of Basis Update and Galerkin (BUG) integrators, has been developed within the framework of dynamical low-rank approximation, offering an alternative, more flexible approach. In this work, we formulate two BUG integrators explicitly for MPS. The BUG integrators result in "single-site" MPS integrators that are robust with respect to small singular values, avoid backward-in-time substeps, and naturally allow for the adaptive choice of bond dimensions. We consider the augmented BUG integrator of first order and the midpoint BUG integrator of second order and clarify their relation to the projector-splitting discretization of the TDVP. Using representative models of strongly correlated quantum systems, like the XY spin model with long-range interaction and two-dimensional lattice models of fermions and spins of various lengths, we perform a systematic numerical comparison between BUG and projector-splitting time discretizations of the continuous-time TDVP, focusing on scalability, numerical complexity, and accuracy. We find that BUG integrators, due to their reduced numerical cost and the flexibility of the concept, provide a competitive and, in certain regimes, advantageous alternative to the well-established time-stepping by projector-splitting in TDVP, especially in models with large local dimensions, and in cases where observables are extracted with high temporal resolution.

Terahertz (THz) imaging has emerged as a promising non-ionizing modality for breast cancer assessment owing to its intrinsic sensitivity to tissue hydration. However, existing dielectric descriptions of biological tissue, largely restricted to single- or double-Debye models, fail to capture the multiscale relaxation dynamics governing broadband THz dispersion and absorption, thereby limiting the quantitative interpretation of reflected pulse signatures.Here, a physics based triple Debye dielectric framework is developed to quantitatively predict broadband THz-tissue interactions. The proposed model explicitly incorporates three physically distinct relaxation processes associated with free-water rotational dynamics, bound-water relaxation ({\tau}_2), and ultrafast interfacial/macromolecular polarization ({\tau}_3). Model parameters are obtained by nonlinear least-squares fitting to experimentally measured refractive-index data digitized from published THz time-domain spectroscopy measurements of ex vivo human breast tissue. Compared with conventional Debye formulations, the proposed model substantially improves the fitting accuracy, reducing the root-mean-square error from 0.7773 (single Debye) and 0.2976 (double Debye) to only 0.0199 for the triple-Debye model. Fullwave FDTD ADE simulations of realistic multilayer breast structures further demonstrate that reflected THz pulses encode tissue hydration through reproducible temporal signatures, including increased reflection amplitude, delayed pulse arrival, and enhanced waveform broadening in malignant tissue. Polarization and angle resolved Fresnel analysis further indicates that hydration-dependent pseudo-Brewster minima provide an additional contrast mechanism by selectively suppressing reflections from low hydration normal and adipose tissues.

Probabilistic bits (p-bits) provide a physical and algorithmic primitive for stochastic inference, but highly parallel updates can alter their collective dynamics. We study the decoding of random-regular (3, 6) low-density parity-check (LDPC) codes using Jacobi-type p-bit annealing with stochastic partial activation. An additive response-path rule stores each bit's saturated response and reuses it before stochastic readout. High-statistics simulations with independent parameter optimization for each method show pooled bit-error-rate reductions of 33.5%, 74.8%, and 81.8% relative to memoryless probabilistic simulated annealing (pSA) for representative codes of block lengths 96, 192, and 288, respectively. Static gain, normalized averaging, response shuffling, and same-bit binary-state feedback with only its coefficient tuned under the same nonmemory parameters do not reproduce the full saturated-response benefit. Trajectory analysis links the improvement to acquisition of the channel-consistent valid-codeword basin and enhanced post-acquisition stability; the acquisition advantage persists from random and channel-hard-decision starts under the tested conditions. Across all 30 independent code realizations, fixed additive parameter-and-readout packages achieve lower bit- and frame-error rates than separately optimized pSA-specific packages, with neither package retuned for individual codes. These results show that the computational effect of temporal state depends on the retained quantity and its reinjection into stochastic dynamics.

In this study, a physics-enhanced neural operator framework is proposed to enhance the generalization prediction ability of the cooling layout of a turbine endwall with variable number of film holes. Specifically, inspired by the film cooling superposition principle, we propose a film cooling prediction model, namely superposition-based deep neural operator (SDNO), that divides the endwall temperature field prediction into two stages. In the first stage, the cooling layout of a turbine endwall is divided into several sub-parts with randomly assigned film holes, and a Transformer-based neural operator network, namely Calculate Net, is designed to predict the temperature field of each sub-part. Then, in the second stage, another neural operator network, i.e., Super Net, is trained to combine the temperature fields predicted by Calculate Net for each sub-part and obtain the superposed temperature field of the full cooling layout. Additionally, instead of directly taking the film cooling contours as pixel plots, a signed distance function (SDF) which is sensitive to the variable locations of cooling holes, is designed to encode the location information of cooling holes. Furthermore, the proposed endwall film cooling prediction model is trained with the samples that changing the number of film holes from 1-5 with variable locations. Then, the trained prediction shows excellent generalization prediction ability, which can accurately predict the film effectiveness of the cooling layout with 10-20 film cooling holes that are unseen in the training samples. The proposed SDNO also improves prediction accuracy relative to the fully supervised baseline. With the above, the effectiveness of our proposed prediction model has been well demonstrated.

Amid increasing global demand for clean, sustainable energy, the search for novel electrode materials has emerged as a crucial link to advancing future energy storage technologies. Here, we explored the potential of N-functionalized 2D MBene V2BN2 as anode materials for Li- and Na-ion batteries using first-principles calculations. Phonon dispersion and ab initio molecular dynamics calculations were employed to assess the dynamic and thermal stability of the material. The intrinsic metallic properties of V2BN2 were revealed through electronic band structures and density of states analyses. Importantly, Bader charge analysis demonstrates substantial charge redistribution upon Li/Na adsorption, leading to stronger ion-substrate interactions compared to the pristine counterpart. This redistribution plays a decisive role in enhancing Li/Na ion adsorption and stabilizing ion accommodation. Furthermore, owing to favorable multilayer adsorption of Li and Na ions, V2BN2 exhibited high theoretical specific capacities of 1524 and 762 mAh/g, as well as low open circuit voltages of 0.73 and 0.23 V for Li and Na, respectively. In addition, the energy barriers were calculated to be 0.49 and 0.29 eV for Li- and Na-ion transport, respectively, indicating rapid ion transport and excellent rate capability. These results indicate that V2BN2 holds significant potential as an anode material for next-generation rechargeable ion batteries.

We present Tomography-NMR, an open-source Python package that reconstructs quantum density matrices from spectroscopic measurement data. The package implements a complete analysis pipeline for two-qubit quantum state tomography based on the product operator formalism: raw time-domain signals are Fourier-transformed into frequency-domain spectra, spectral peak intensities are mapped to expansion coefficients of the density matrix, and the full quantum state is reconstructed. Three integration methods are provided for different use cases: direct peak height measurement and fixed-parameter numerical integration require no theoretical reference and are suited to unknown states, achieving fidelities of approximately 98% on a benchmark set of known states, while a systematic parameter optimization against a known target state achieves reconstruction fidelities exceeding 99% for the same benchmark states. While the detailed theoretical framework for quantum state tomography is well established, the practical procedures for extracting density matrices from experimental spectra are often inadequately documented in the literature and obscured within proprietary software. This package addresses that gap by providing a fully transparent, reproducible implementation of every analysis step, from spectral preprocessing to density matrix visualization. The software has been validated on experimentally prepared two-qubit states measured via nuclear magnetic resonance (NMR) spectroscopy of coupled $^{31}$P nuclei. Average reconstruction fidelities range from 0.975 to 0.995 across a benchmark set of 20 two-qubit states, including the computational basis states, Bell states, and the outputs of three fundamental quantum gates (CNOT, H, and T). Although developed for NMR, the modular architecture facilitates adaptation to other spectroscopic platforms and alternative measurement protocols.

The k$\cdot$p method can be applied directly to first-principles energies and momentum matrix elements, but the resulting models converge slowly with the number of bands and are inexact wherever the underlying Hamiltonian is nonlocal. We show that both limitations can be largely removed by renormalizing the eigenvalue spectra of the Hermitian momentum matrices. These spectra are gauge invariant, and are identical along symmetry-related directions; hence scaling the recurring magnitudes provides a modest set of parameters that preserves degeneracies and crystal symmetry without needing to construct a symmetry-adapted basis. We choose to renormalize the models against reference eigenvalues and band velocities on rays out of a high-symmetry point. For GaP, a 15-band model can reproduce the band structure in the near-gap reference regions within a few meV. For zincblende and wurtzite AlN, similar accuracy requires 30- or 85-band models, which in the case of zincblende is traced to the strong warping in the [110] direction. The scheme readily generalizes to rocksalt PbTe, which includes spin-orbit coupling, and has $L$-centered valence- and conduction-band extrema. Further, we demonstrate, for GaP, how compact four-band Hamiltonians can be obtained by downfolding within the same renormalization scheme. Moreover, by changing the fitting regime to encompass the entire Brillouin zone, the scheme can be used to generate full-zone models that reproduce the density of states. A practical use of the method is illustrated with a 59-band k$\cdot$p model for GaP, which can be evaluated on meshes far denser than the reference calculation, that in turn can be used both to resolve fine features in the density of states and to compute the hole conductivity at low temperature.

We introduce a systematic block entropy area based mode optimization algorithm for many-body quantum states of interacting fermions represented by matrix product states. From the gradient of a global cost function, the block entropy area, a long-ranged, non-interacting effective disentangler Hamiltonian is formed. We then simulate the time-dependent Schrödinger equation driven by the disentangler Hamiltonian by employing the time-dependent variational principle based on projector splitting, and minimize the cost function. The combination of the density matrix renormalization group with this gradient-based entanglement minimization forms an efficient low-rank iterative ground-state algorithm that also provides an optimized single-particle basis for matrix product state representation. We demonstrate the method on two-dimensional lattice models of interacting fermions and the Fe${_4}$S${_4}$ cluster, and show its robustness and superiority over earlier protocols using nearest-neighbor mode rotations and reorderings.

Coarse spatial resolution limits the ability of subseasonal prediction models to resolve extreme precipitation. Downscaling with either dynamical or deep generative models can overcome this issue, but the comparative performance of these models for extremes across different atmospheric regimes remains poorly understood. In this work, we evaluate the Weather Research and Forecasting (WRF) model against a diffusion-based generative model by downscaling two physically distinct, extreme precipitation events up to lead times of 3 weeks. For a fair comparison with WRF, which can downscale boundary conditions from different driving models without model-specific training, the diffusion model is trained in an unpaired fashion. Both approaches improve upon the raw European Centre for Medium-Range Weather Forecasts forecasts, in comparison to fused rain gauge-radar observations in Switzerland (CombiPrecip), but exhibit regime-dependent strengths. WRF achieves the highest probabilistic skill for a multicell, non-stationary event. Conversely, the diffusion model is more consistent across different performance metrics for the two events, outperforming WRF in a more stationary supercell event. These results demonstrate that explicit dynamical modeling can add value for specific precipitation events for subseasonal lead times, and that generative downscaling adds value more broadly in different situations.

In a fusion device plasma-wall interactions \edit{on the sheath scale} can be modeled as a collisionless problem. When modeling these regions particle-in-cell codes suffer from statistical error originating from undersampling the velocity space. On the other hand, Vlasov codes do not have this issue as they evolve the full distribution function. Here, we present a new finite-volume Vlasov code, kobra, equipped with adaptive-mesh refinement to reduce computational effort. Currently, the code solves the Vlasov-Poisson equations. We validate our code in 1d1v and 1d2v using established benchmarks, i.e. the two-stream instability, Landau damping, the Dory-Guest-Harris instability, and also a classical electrostatic plasma sheath. We find that the code reproduces the theoretical properties of these problems well. More importantly, the adaptive grid provides a computational gain that is likely to scale to higher dimensional, plasma-wall simulations.

Cryogenic buffer-gas cells are widely used to produce cold molecular beams, but the microscopic dynamics governing beam formation remain challenging to model. Here we present fully kinetic simulations of a cryogenic buffer-gas cell using the Direct Simulation Monte Carlo method implemented in the PICLas framework, treating the buffer gas and ablated molecules within a single unified model. We capture characteristic features of cryogenic buffer-gas sources, including plume cooling, directed transport toward the aperture, and the formation of a slow molecular beam, while also resolving energy transfer from the hot ablation plume to the helium buffer gas that is inaccessible to existing approaches relying on the background-gas approximation. Our results demonstrate that fully kinetic simulations can provide detailed insights into buffer-gas cell dynamics and open a route toward a systematic optimization of such sources.

Magnetic materials play a crucial role in energy-related technologies, mobility, and sensing, but their complex multiscale behaviour and the coexistence of multiple unit systems pose persistent challenges for data exchange and interpretation. This paper presents a domain ontology for magnetic materials, developed within the European Union funded Magnetic Multiscale Modelling Suite (MaMMoS) project and aligned with the Elementary Multiperspective Material Ontology (EMMO). The ontology formalises intrinsic, hysteretic, and microstructural properties across multiple length scales and supports semantic interoperability between simulation tools, databases, and experimental workflows. One key feature of the ontology is its code-based and human-readable structure, enabled through the EMMOntoPy framework, which allows for direct manipulation and versioning without relying on opaque .owl or .ttl files. This facilitates collaborative development and improves transparency. The ontology supports FAIR (Findable, Accessible, Interoperable, Reusable) principles and is openly available for extension by the community. The aim of the MaMMoS project is to foster reproducibility, improve traceability, and promote the adoption of ontology in the magnetism domain. The ontology that has been developed already serves as the foundation for multiple software tools that handle all kinds of magnetic material data.

While self-consistent diagrammatic approaches are widely used to compute the physical properties of correlated quantum materials, their applicability may get severely hindered precisely in the parameter regions where the most exciting physics is observed. One of the major issues, referred to as "misleading convergence", is the tendency of iterative schemes to converge to unphysical fixed points for intermediate-to-strong electronic interactions, regardless of the numerical accuracy of the computation. Here, we explicitly verify that the origin of this problem in several established self-consistent many-electron approaches, defined in the general diagrammatic framework of the boson-exchange formalism, resides exclusively in the stability condition of the respective iteration schemes, and not in an intrinsic breakdown of their self-consistent diagrammatic description. This insight enables a simple and general remedy, as recently proposed in Phys. Rev. Lett. 137, 016502 (2026): The redefinition of the iterative procedure, by inverting the unstable eigendirections of the Jacobian associated with the fixed point of the self-consistent algorithm. We illustrate the successful outcome of this procedure by means of systematic calculations performed on exactly solvable testbed models. Our results demonstrate that the physical fixed point of the diagrammatic schemes we considered can be stabilized, de facto, across the entire parameter range, including the most challenging nonperturbative/strong-coupling regimes.

HfO$_2$ exhibits rich polymorphism, and competition among different phases underpins many of its functional properties. Yet bulk free-energy relations alone cannot explain phase selection at mixed-phase boundaries, where interface orientation and structural continuity constrain collective rearrangements. Here, using machine-learning atomistic simulations and a Hf-centered local phase classification scheme, we show that crystallographic interface matching redirects phase competition and accessible transformation pathways. The M(100)/T(100) interface remains pinned as an M/T mixture throughout 3 ns simulations from 300 to 1800 K. M/PO, M/AO, and PO/AO interfaces retain two-phase coexistence up to 900 K, whereas all T/PO interfaces become PO-dominant. At 1800 K, all non-M interfaces become T-dominant, while M-containing interfaces retain a monoclinic majority. Nudged elastic band calculations reveal lower-barrier routes through interface states. For M(100)$\rightarrow$T(100), the interface-mediated route in a long cell ($\sim$12 nm) yields a barrier of 136.03 meV/f.u., 29.2 % lower than the direct route in a short cell ($\sim$3 nm). This difference is associated with sequential phase-front motion absent from the short cell. These results identify phase boundaries as active participants in phase stability and transformation and establish interface orientation and crystallographic matching as variables for stabilizing metastable polymorphs and directing phase conversion in HfO$_2$.

We propose an all-reflective two-mirror projection system for extreme ultraviolet (EUV) lithography operating at exposure wavelengths of $13.5$~nm (Mo/Si) and $11.2$~nm (Ru/Be), delivering a fourfold ($4\times$) demagnification of the periodic mask pattern at a numerical aperture approaching unity ($\mathrm{NA}_{\max} \approx 0.993$). In contrast to conventional EUV projection objectives that incorporate 6–10 aspheric mirrors with an overall optical throughput of less than $15\%$, the proposed design redirects each accepted discrete spatial diffraction order scattered by the mask onto the wafer via a dedicated pair of planar mirror facets. The number of reflections is strictly fixed at two for all accepted orders, retaining $50$–$60\%$ of the power leaving the mask in each accepted order. We derive a spatial geometry providing rigorous optical path length equalization across all diffraction orders, thereby removing order-dependent propagation phase shifts. Individually optimized 30-bilayer Bragg multilayer coatings are designed for each facet using the transfer matrix method combined with global evolutionary optimization algorithms. The architecture is generalized to a three-dimensional vector formulation with a two-dimensionally periodic mask. Utilizing inverse lithography technology, Fourier parameterization, and a differentiable electromagnetic modal waveguide solver, we solve the synthesis problem for binary absorber masks (La absorber on a Ru/Be/Sr multilayer mirror). We demonstrate simulated aerial images of sub-10-nm features on the wafer (isolated peaks with a full width at half maximum (FWHM) of approximately $5.4$~nm and line pairs with a critical dimension of $6$~nm) and find that the two peaks remain resolved for the tested wafer defocus values from $0$ to $5$~nm along the $z$-axis.

A three-dimensional multiphysics finite element model for the simulation of high-frequency induction welding of tubes is presented. The model couples a time-harmonic $h-\phi$ formulation with a stabilized quasi-stationary advection-diffusion heat transport equation, enabling accurate prediction of electromagnetic and thermal fields under industrial operating conditions. The model incorporates parameterized geometry generation and semi-automated, physics-tailored mesh construction and is implemented using the open-source tools GetDP and Gmsh. Validation against measurements from a commercial induction welding line for AISI 304 stainless steel tubes demonstrates good agreement with operating data. The validated model is subsequently applied to investigate the influence of impeder material by comparing a conventional FeNiZnV ferrite with the soft magnetic composite Ferrotron 559H for the induction welding of AISI 304 stainless steel tubes.

Granular slip forecasting can conflate material state, loading progress, and the geometry of event-centered sampling. We separated these contributions in slowly sheared two-dimensional frictional disks using a compact neural score of stress, pressure, coordination, non-affine motion, and force-network observables. The model was developed on 36 trajectories and frozen efore testing on 18 new trajectories under two nested stress-drop definitions. Inspection of held-out results revealed post-event sampling asymmetry; recovery-aware analyses are therefore descriptive. With trajectories weighted equally, the compact score ranked near-slip windows above both prevalence and within-trajectory circular-phase controls under both definitions (representative average precision 0.310 versus prevalence 0.173; phase-null upper bound 0.257). Loading-history coordinates ranked more strongly, reaching 0.534 for causal elapsed strain. The recovery-aware rule retained 78.4% of activity-gated events and preferentially selected longer preceding intervals; ranking by time since the previous catalogued event remained compatible with a count-conditioned geometry null. Compact observables thus contain temporally aligned slip information, but stronger loading-history baselines and window-geometry sensitivity bound that evidence. These startup data do not isolate a state-specific short-horizon precursor beyond loading history or support a renewal interpretation of elapsed-strain ranking.

The High-Granularity Dual-Readout Calorimeter (HG-DREAM) designed for FCC-ee aims to achieve unprecedented energy resolution through fine three-dimensional shower imaging, simultaneous measurement of Cherenkov and scintillation signals, and machine learning capabilities. A key enabling technology is longitudinal segmentation via timing measurements, where multiple energy deposits along optical fibers are distinguished by the arrival times of Cherenkov photons. We present quantized one-dimensional convolutional neural networks that predict pulse locations directly from 80-sample, 16 ns waveforms digitized at 200 ps, and evaluate their implementation in front-end electronics. Three architectures, a compact baseline and two variants using dilation and stride to reach the $\sim$ 20-sample receptive field set by the SiPM pulse width, achieve ROC AUC above 0.95 with four to eight filters per layer. Fixed-point quantization to a $<$16,8$>$ baseline costs less than $10^{-3}$ in AUC relative to the floating-point reference. Applied to pairwise deposits, the smallest model separates two pulses with better than 95% probability at longitudinal separations of 20 cm and above. Synthesized at 400 MHz, this model uses 9663 LUT on a Xilinx XCVU13P FPGA and occupies $\sim$ 18500 $\mu$$m^2$ in TSMC 28 nm CMOS with reconfigurable weights. Assuming a triggered readout architecture for FCC-ee, the $\sim$ 500 kHz Z-pole trigger rate will be the most stringent throughput requirement across all FCC-ee operating modes. The ASIC implementation of the model sustains 4.8 MHz, exceeding the 0.5 MHz requirement by an order of magnitude, while the reconfigurable-weight FPGA implementation fails to do so. Sustaining the 40 MHz FCC-ee Z-pole bunch-crossing frequency in the triggerless architecture is not feasible with the models developed and the hardware targeted in this work.