Papers

The capacitance of thin-film diodes based on undoped semiconductors are dominated by injected charge carriers rather than doping-induced carriers. However, an analytical framework of the capacitance in these devices has remained elusive. Here, we derive an analytical description of the capacitance, fully accounting for injected charge carrier and electrode charge effects. Based on these findings, a method to extract the built-in voltage in these devices is presented. The theoretical framework is substantiated numerically by drift-diffusion simulations and experimentally on organic solar cells.

Grid-based fluid dynamics solvers routinely struggle with exhaustive meshing demands and ill-posed inverse problems. A practical mesh-free alternative is given by Physics-informed neural networks (PINNs) but, applying them to highly elastic Oldroyd-B fluids results in many training failures. The High Weissenberg Number Problem (HWNP), driven by the exponential stress growth near stagnation points is a major issue, which causes standard PINN optimizers to diverge. To prevent the network from crashing, we apply a Cholesky decomposition to the conformation tensor. This mathematical constraint stabilizes the gradients by guaranteeing a positive-definite stress field. Beyond mathematical stability, the inherent spectral bias of deep learning models can hinder the network from accurately capturing the highly elastic wake structures. Therefore, we used sparse data assimilation to force the model toward the actual physical solution. By anchoring the physics loss with targeted CFD data points and accelerating training via transfer learning, we successfully pushed the network past non-physical local minima. We validated this Cholesky-PINN approach on flow past cylindrical geometries for Reynolds numbers (Re) between 5-25, for the single-cylinder setup. In addition, the relaxation time ({\lambda}) is increased from 0.1 to 0.5 to test the stability of the network. Finally, we scale the framework to a complex 3-cylinder array which proved our constructive solid geometry approach completely bypasses the tedious re-meshing steps of traditional CFD. The combined framework accurately captured sharp viscoelastic wakes, providing a stable computational tool for complex rheological modeling.

Many systems in soft and living matter bind through a limited number of bonds per particle: proteins associate via discrete surface patches, nucleic acids form one-to-one contacts, and the phase behaviour of multivalent biomolecules is governed by the number of binding sites they carry. In simulations, valency limits are typically enforced with patchy particles, whose anisotropic potentials require integration of rotational degrees of freedom and combine hard cores with narrow patches, which forces small timesteps and commits the model to a fixed binding-site geometry that is often unknown, flexible or mobile. We introduce the valency-bounding correction (VBC), a many-body modification of generic short-range pairwise potentials that smoothly suppresses attraction once the neighbour count of either interacting particle exceeds a prescribed valency. The correction carries no angular degrees of freedom, applies on top of soft repulsive cores and evaluates in two passes over the neighbour list at the cost of a standard pairwise potential. The VBC drives the coordination number to the prescribed valency with low error, while its cluster statistics depart from Wertheim and Flory-Stockmayer predictions through unrestricted ring formation. A tuned variant exchanges bonded partners through ordinary molecular dynamics, reducing bond lifetimes at high saturation by an order of magnitude. On GPUs the cost of the correction is nearly independent of valency, reaching an almost tenfold advantage over a patchy-particle reference. We illustrate large-scale applications by reproducing the reentrant aggregation of repeat-expanded RNA, and show that the VBC also remedies the Fisher-Ruelle thermodynamic instability of soft-core potentials with attraction. The VBC is available as an open-source GPU plugin for HOOMD-blue.

Physics-informed neural networks (PINNs) struggle on PDEs whose governing physics varies across the domain. We trace this to a structural property of standard coordinate networks: their neural tangent kernel (NTK) is translation-variant and lets training points of large coordinate magnitude disproportionately influence predictions elsewhere, producing long-range coupling and gradient conflict during training. We show analytically and empirically that mixture-of-experts (MoE) architectures with centered, compact-support routers yield a uniformly banded NTK whose kernel-regression weights decay exponentially with distance, localizing the learning. Building on this, we propose Latent-MoE, which interleaves domain-aware MoE blocks within a shared backbone. Unlike FB-PINNs or X-PINNs, which rigidly partition both the domain and the parameters so that the parameters on different subdomains are updated independently, Latent-MoE is designed to preserve the localization benefit of domain-aware routing while allowing capacity to flow across regions through the shared backbone. On standard homogeneous-physics benchmarks Latent-MoE is competitive with established baselines; on benchmarks with multi-stage time-variable physics, where global models and rigid domain decompositions both fall into spurious solutions, it improves over them by more than an order of magnitude, with markedly reduced gradient conflict during training.

Classical crystallography determines orientation and unit-cell geometry from a rotation series collected from a single specimen, whereas serial crystallography acquires one still exposure from each of many randomly oriented microcrystals and must reconstruct a complete dataset by pooling partial measurements across the ensemble. In serial experiments, each frame typically contains only a subset of reflections, and frames with few reliable peaks (the small-N regime) are especially difficult to index blindly, because multiple incorrect lattices or orientations can explain sparse observations. The approach implemented in GLINT addresses this small-N regime by treating the dataset, rather than the individual frame, as the unit of inference. Candidate solutions are generated for many frames, weak but recurrent lattice hypotheses are pooled across the ensemble to identify a shared unit cell, and each frame is then registered against that consensus cell through a known-cell orientation search with lower dimensionality than blind indexing. In benchmark tests, GLINT matched the strongest blind indexer in the comparison on indexing yield at roughly 40x lower per-frame blind-indexing latency against XGANDALF's fastest configuration, and batched known-cell registration ran entirely on one GPU, allowing cell discovery on the fly during serial data collection. On experimental serial data, frames indexed blind by GLINT merged to crystallographic quality (CC* = 0.90), while the method rejected non-crystal images rather than forcing unsupported indexing assignments. These results demonstrate that indexing performance can be evaluated not only by indexing yield and throughput, but also by the quality of the resulting merged crystallographic data.

Neural quantum states (NQS) have emerged as a powerful framework for simulating non-equilibrium dynamics in strongly correlated quantum systems, offering scalable variational representations of highly entangled states. Yet, accurate NQS simulations have been found to be surprisingly challenging in some physical regimes of limited complexity. Here, we address paradigmatic quench dynamics of a one-dimensional quantum Ising model as a controlled benchmark. Through supervised state reconstruction we establish substantially tighter empirical upper bounds on the required parameter count than previous estimates, ruling out representational limitations as the key obstruction. Instead, we uncover a geometric inflation of small deviations as a hitherto overlooked challenge for accurate solutions of the infinitesimal time-dependent variational principle (TDVP): the dynamical rotation of the kernel of the quantum geometric tensor (QGT) can suddenly lend physical significance to previously irrelevant parameter deviations. The stability of matrix product state solutions of the same TDVP suggests that the non-linearity of the neural network ansatz is the origin of the sensitivity. These results identify QGT-null-space rotation as a geometric diagnostic of sensitive NQS dynamics and as a concrete target for improving TDVP algorithms

Rare-event search experiments continue to extend their sensitivities to unprecedented levels. This requires increasingly small backgrounds, and shielding schemes that can suppress external backgrounds by several orders of magnitude. Moreover, detailed simulations are required to attain a good understanding of these experimental backgrounds. Studies have suggested, however, that simulations and computing-related tasks contribute approximately 10% to the average particle physicist's carbon footprint. With backgrounds frequently below 0.01 counts per kg of target per keV of energy, simulations require more computing resources; growing a rare-event researcher's computing-related carbon footprint. Event biasing is often applied in shielding simulations as a remedy, yet there is a lack of systematic guidance on how to maximally benefit from them. An optimisation study for the importance-splitting biasing technique is discussed, focused on balancing statistical precision with simulation CPU-time, and how this can benefit the average researcher's carbon footprint.

Critical universality does not determine the microscopic conditional structure of a probability measure. We study a bicolored cluster recursion constrained to remain critical at every generation, with no equilibrium spin measure or fixed coupling imposed. In both two and three dimensions, the resulting history-dependent sequence develops a common Ising/Fortuin–Kasteleyn (FK) compatibility structure: the second-shell dependence of a one-site conditional law is strongly suppressed, nearest-neighbor effective couplings move progressively toward one another near the critical Ising value, and cluster and interface observables organize around the corresponding FK geometry. An exact cluster-coloring factorization singles out $q=2$ as the point where the residual connectivity weight disappears from the two-color spin marginal. Thus equilibrium-compatible conditional structure can emerge along a trajectory that remains critical throughout.

Data-driven constitutive models for path-dependent plasticity are commonly formulated using nonlinear recurrent neural networks, whose sequential state evolution limits parallel training and whose predictions may depend on the discretization of the applied strain path. We introduce a Constitutive State Space (CSS) model that reformulates structured state-space dynamics as an incremental constitutive operator. The strain increment is decomposed into magnitude and direction: the loading direction drives the latent state-space system, while the increment magnitude enters the zero-order-hold discretization of its continuous-time linear recurrence. This mechanics-tailored construction guarantees stationarity under zero increments, strongly reduces sensitivity to strain-path resolution, and retains the parallel-scan structure of S5 for efficient training on long constitutive histories. The CSS and Minimal State Cell (MSC) architectures are compared for four multiaxial path-dependent material models including isotropic J2 plasticity, pressure-sensitive foam plasticity, and combined isotropic-kinematic hardening. CSS matches or exceeds the prediction accuracy of the MSC, including one order of magnitude lower validation losses for the plastically incompressible materials. Importantly, CSS maintains low errors across large changes in strain-path discretization, whereas the MSC error increases substantially when evaluated at coarser resolutions than used for training. CSS trains substantially faster and requires fewer strain-stress pairs to attain comparable or better accuracy. Analysis of the learned state further reveals latent structure consistent with the dimensionality of the underlying physical constitutive models. These results establish mechanics-tailored structured state-space dynamics as a computational framework for efficient and discretization-robust data-driven constitutive modeling.

We consider a mixed quantum-classical formulation for open nonequilibrium molecular systems. We employ the pseudoparticle nonequilibrium Green's function (PP-NEGF) method as a fully quantum description and a starting point for introducing classical nuclear dynamics for an open system. We use this formulation to derive a density-matrix equation of motion describing the quantum-classical evolution. We compare our results with previous formulations and highlight important differences related to the nonequilibrium and open character of the molecular system. We also analyze the approximations needed to reduce full quantum-classical dynamics to the fewest-switches surface hopping (FSSH) method and show that these approximations are unreasonable when adiabatic surfaces come close to each other.

Physics-Informed Neural Networks (PINNs) embed PDE residuals into neural network training, but their internal representations remain opaque: it is unknown what physical features their hidden layers encode or whether those features have a localized causal role. We present PhysSAE, a mechanistic interpretability framework that trains overcomplete sparse autoencoders (SAEs) on PINN penultimate-layer activations and evaluates dictionary atoms through direct causal intervention in the original frozen hidden state: $h_{\mathrm{cf}} = h - \alpha z_k d_k$, bypassing the SAE decoder entirely. Across six PDE families, with 3 PINN seeds and 3 SAE seeds each—we show that (i) Our discovered SAE atoms align with independently-defined physical observables (max Pearson $|r|=0.951$, always $\gg$ permutation null), (ii) the causal footprint of top-aligned atom ablation is 1.2–4.2$\times$ more spatially concentrated canonical than PCA or ICA interventions, and (iii) top-aligned atoms outperform matched random controls on causal localization for structured physical concepts (ESF$_{80}$ advantage 0.04-0.44). Two-atom bilateral representations improve concept regression R$^2$ by $\Delta R^2\!=\!0.05\text{-}0.15$ over single atoms, while random pairs decrease it by up to 0.60. These results demonstrate that PINNs develop sparse, physically structured latent representations that can be identified and causally interrogated post-hoc, opening a path toward interpretability-aware scientific machine learning.

Laws from physics to social science rest on a small, enumerable number of factors explaining variation in the systems they describe. While the orthodoxy of economics, physics and chemistry relies on a small number of analytically interpretable laws, large data models pursuing universality throw in all available data. Both fail in their extremes through the same flaw, an incorrect basis: too few orthogonal variables to describe system variation, or an explosion of dimensionality around fundamentally low dimensional but unknown dynamics. We propose and validate a framework that extracts an interpretable, mathematically sound, low dimensional and dynamically informed basis directly from arbitrary timeseries. Combining Takens' Delay Embedding Theorem, dimensionality reduction and optimal transport, we produce a phase space of phase spaces (PSoPS) on which all interrelated dynamical series exist: barycenters formed from many phase spaces, together with the families of transport plans between individual phase spaces and the barycenter, yield a basis describing the relational geometry of each timeseries and the topology of economic relationships. The macroeconomic PSoPS built from 281,536 series is a single connected object of effective dimension 7.3 +/- 0.2, into which the canonical laws (Phillips, Okun, Solow) embed as roughly 2-dimensional looped sub-attractors. The coupling between concept barycenters is directed, with the price of money and productivity as net sources and credit and output as sinks, recovering monetary transmission from geometry alone. IAAFT phase-randomized surrogates destroy 36-43% of the dimension across the three laws and 38% on the full corpus, so the basis is irreducibly nonlinear. We close with proposed extensions to fields whose dynamics share attractor geometries and to systems that are intrinsically low dimensional yet hard to navigate in raw coordinates.

The advent of X-ray free-electron lasers and high-harmonic generation has made time-resolved X-ray spectroscopy a powerful tool for probing local atomic environments, yet whether localized core excitations can report on global collective distortions remains open. Cyclo[18]carbon (C$_{18}$), with its polyynic ground state (D$_\text{9h}$) and cumulenic transition state (D$_\text{18h}$), provides an ideal model to address this long-standing issue in bond-length alternation (BLA) dynamics. Mapping two-dimensional potential energy surfaces by first-principles simulations, we find that core ionization symmetrizes the ground-state double-well potential along the BLA coordinate. Our calculated X-ray spectra reveal remarkable sensitivity to bond-length variations: C1s ionization potentials vary by up to 2.4~eV across the BLA coordinate (1.1–1.4~Å), with a 0.9~eV variation for minima predicted by different functionals, while NEXAFS $\pi^*$ peaks shift by up to 4~eV across the same coordinate. These predicted signatures provide a quantitative spectroscopy–structure dictionary for decoding transient structures in future ultrafast X-ray experiments and monitoring bond-alternation dynamics in real time.

FBPIC (Fourier-Bessel particle-in-cell) is a high-performance simulation code for relativistic plasma and accelerator physics. Its original accelerator backend relies on Numba CUDA, which limits its direct deployment on accelerators using the HIP (Heterogeneous-Compute Interface for Portability) programming environment, such as DCU (Deep Computing Unit) accelerators. In this work, we develop an accelerator backend compatible with HIP that enables FBPIC to run efficiently on DCU platforms while preserving its Python user interface and high level simulation workflow. For the evaluated LWFA (laser-wakefield acceleration) workloads, the proposed backend achieves 1.32-1.54x speedups over the original FBPIC implementation on an NVIDIA V100 GPU and enables efficient execution on the DCU platform. We also summarize the key lessons learned from porting FBPIC to the DCU platform. Multi-DCU experiments achieve a 1.88x strong-scaling speedup on four accelerators and a 2.72x increase in aggregate throughput at approximately 68% weak-scaling efficiency, with communication analysis identifying inter-node communication and synchronization as the main scalability limitations. Beyond FBPIC, the proposed approach provides a practical reference for porting and optimizing other scientific computing applications developed with Python on heterogeneous accelerator platforms.

A nonlinear EM inversion method regularized by $\ell_1 / \ell_2$-norm of the model gradient is proposed. The $\ell_1 / \ell_2$-norm, defined as the ratio of $\ell_1$-norm to $\ell_2$-norm, exhibits a scale-invariant property that enables it to more accurately characterize sparsity in the solution compared with the conventional $\ell_1$-norm. To address the nonconvexity and nonsmoothness introduced by the quotient structure of $\ell_1 / \ell_2$-norm, the inversion problem is reformulated and solved using the alternating direction method of multipliers, resulting in an efficient optimization algorithm comprising five sub-steps. The Gauss–Newton method is incorporated into the first sub-step to linearize the nonlinear inversion. The proposed method is validated by a series of numerical examples using both synthetic and experimental datasets, and is compared against standard regularization methods such as Tikhonov and total variation. The results demonstrate that the proposed method yields superior reconstructions of permittivity profiles, particularly in preserving sharp edges and suppressing artifacts, without incurring additional computational cost. Furthermore, the method exhibits strong robustness to measurement noise and reduced sensitivity to the choice of the regularization weight.

In this paper, we present a high-order, stable summation-by-parts (SBP) finite difference method for solving the Vlasov-Maxwell system in a 2D2V phase space. Central SBP operators for the advection terms are not stable when the solution becomes non-smooth and fine-scale filamentary structures develop, as is typical in high-dimensional Vlasov-Maxwell simulations. To address this issue, the method is stabilized using high-order upwind SBP operators. High-order explicit Runge-Kutta methods are employed for time integration. We prove that the fully discrete scheme exactly conserves mass and preserves momentum up to truncation error. Furthermore, we present a matrix-free implementation of the method on modern GPU architectures. A range of challenging benchmark problems is solved to demonstrate the accuracy, robustness, and performance of the proposed scheme.

We propose a symmetry-preserving Boson-informed neural network (BINN) for the variational Monte Carlo computation of interacting-boson ground states in continuous space. The framework explicitly separates the two dominant physical scales of the system. At the microscopic interaction scale, a cell Jastrow factor absorbs the leading near-contact stiffness, while at the macroscopic scale, a potential-aware envelope encodes the external confinement, leaving the neural network to capture a smoother many-body residual. Across both two- and three-dimensional tests, the BINN approaches the Hartree and Gross-Pitaevskii limits with an empirical $\mathcal{O}(N^{-1})$ finite-size trend in both energy and density. The energies agree with diffusion Monte Carlo to within $0.06\%$ for the particle number tested up to $350$ in the three-dimensional shrinking-range case. Applications are made under various trapping scenarios, justifying robust performance of BINN.

A computation-based study is presented that utilizes the finite-difference time-domain (FDTD) method to perform optical simulations. These simulations were used to observe the effects of core-shell nanostructures with triangular cross-sections embedded inside the light absorbing layer of thin-film solar cells (TFSCs), for improving the optoelectronic performance. Despite extensive research, the relationship between the geometric parameters of light trapping nanostructures and TFSC performance is not yet fully elucidated. To assist in finding the optimal set of parameters, optimization algorithms are used to find a set of parameters that provide maximal short-circuit current density ($J_{SC}$). Three separate algorithms were applied, and the results were scrutinized in-depth with a sensitivity analysis, observing the effects of small changes in the optimal parameters. The optimal parameters of the nanostructure produced $J_{SC}$ values ranging from 28.90 to 35.39 mA/cm$^2$ compared to 13.69 mA/cm$^2$ for a reference TFSC with no nanostructure present. From the results of this study, it can be inferred that optimized periodic grating structures can provide substantial improvement in performance of TFSCs.

Reconstructing physical fields from sparse observations is central to system identification, forecasting, and control, yet sparse measurements generally underdetermine the full field. This makes reconstruction an ill-posed inverse problem rather than simple interpolation. Although many deterministic and generative methods have been developed, there is still no clear consensus on when a single point estimate is sufficient and when a distribution of plausible reconstructions is more useful. We conduct a fair comparison of a deterministic U-Net, conditional diffusion, and prior-guided diffusion under matched experimental settings, including 2D Poisson equation, 2D Navier-Stokes flow, and 1D Kuramoto-Sivashinsky dynamics. Through this comparison, we make three observations. First, accuracy is field- and regime-dependent, with no systematic advantage for diffusion under higher complexity or sparser observations. Second, ensemble means improve phase-aligned accuracy, whereas individual samples better preserve variability and can retain high-wavenumber power in selected regimes. Third, conditional diffusion provides more reliable uncertainty estimates at lower cost, while prior-guided diffusion is more robust to mask-distribution shifts but requires substantially higher inference cost and guidance tuning. These results clarify when generative reconstruction is useful and provide guidance for improving uncertainty estimation, fine-scale sample fidelity, robustness, and computational efficiency in sparse field reconstruction.

Neural quantum states (NQSs) have emerged as a powerful ansatz for quantum dynamics. Their high entanglement capacity promises to overcome the entanglement barrier. However, existing time-dependent NQS methods are ill-equipped to propagate sparsely supported initial states due to the inherent mismatch between the supports of such states and their time derivatives. To address this issue, we introduce the interpolation sampling method as a novel form of importance sampling, where samples are drawn from a distribution that interpolates between the wave function and its time derivative. Combined with a global-in-time variational principle, we demonstrate with the example of the two-dimensional transverse-field Ising model that interpolation sampling allows for accurate propagation of extremely peaked states that standard wave function sampling could not tackle. We further improve sampling efficiency by introducing (i) a configuration-time joint sampling scheme where spin configurations and time are both treated as random variables, and (ii) a hybrid strategy for sample proposal that incorporates the knowledge of a Krylov subspace. Our work extends the scope of time-dependent NQS methods to a wider range of physically relevant scenarios, while challenging the prevailing reliance on the Born distribution and its close variants as the default basis for sampling.

Quasicrystals lack the translational symmetry that underlies Bloch decomposition and Brillouin-zone integration, making full-band momentum-resolved electronic structure difficult to formulate and compute. We develop a systematically convergent reciprocal-space tight-binding framework for a broad class of quasicrystals. The method combines two systematically refinable components: a Fourier-module scattering-channel Hamiltonian that yields local spectral and current-current correlation quantities at each physical momentum, and an expanding hierarchy of pseudo-Brillouin zones that converts the resulting local quantities into bulk thermodynamic observables through an exact local-to-global relation. Applied to the Penrose and Ammann-Beenker models, the framework uncovers full-band momentum-resolved quasibands and a multichannel mechanism for pseudogap formation, both beyond the scope of low-energy effective models. It further resolves Zeeman-driven gap closings and reopenings, quantized Chern plateaus, and the phason invariance of bulk spectral and topological observables. This framework provides a unified reciprocal-space route to full-band spectral and topological properties of quasicrystals.

Quasicrystals occupy a distinctive position between the translational order of crystals and the disordered amorphous matter. Simulating this physics has remained challenging, since quasiperiodic structures lack the translational symmetry exploited for crystals and generally require costly diagonalization of large finite approximants. Quasicrystalline order admits two equivalent descriptions, a cut-and-project scheme from a higher-dimensional periodic crystal, and a discrete set of substitution rules acting on a finite alphabet. We show that the latter, written in a numeration system adapted to the substitution, defines a deterministic finite automaton with output, the digits of a site index are fed, and the automaton returns the letter occupying that site. We further exploit another equivalence to a different construction, the transition matrices are exactly the tensors of a matrix product state, whose bond dimension is the number of automaton states and is independent of system size. This allows efficient representation of extremely large tight-binding Hamiltonians in the tensor-train language, thereby yielding an exact matrix product operator for the quasicrystal Hamiltonian at any system size. We show how this framework works for two families of one-dimensional quasicrystals, the metallic-mean and $k$-bonacci families and we explicitly construct the Fibonacci, silver-mean, and Tribonacci quasicrystals. By leveraging efficient tensor-network compression and the kernel polynomial method, we compute spectral densities for chains with more than $10^9$ sites and directly resolve the hierarchical structure of the spectrum.

Thermoacoustic instability in premixed flames results from the coupling between the flame's heat release, as determined by combustion parameters, and surrounding acoustics, as determined by combustor geometry. A primary instability results in flame flattening as intrinsic flame instability modes are stabilised. Secondary thermoacoustic instability results in a parametric flame instability and drastic growth of acoustic amplitudes. Due to their relative expense, numerical simulations of these phenomena remain scarce. In this work, Direct Numerical Simulations (DNS) of thermoacoustically unstable idealised premixed flames in a tube with acoustically closed upstream and open downstream ends are presented. Results herein demonstrate nonlinear saturation of the primary instability as the flame flattens as well as an oscillating flame fingering characteristic of the unsteady Rayleigh-Taylor effect. To reduce computational cost, we perform DNS only on the region surrounding the flame. Acoustics at in- and outflows are described using the Navier-Stokes Characteristic Boundary Condition (NSCBC) method to model their delayed reentry into the domain in a formulation referred to as the Acoustic Delay Characteristic Boundary Condition (ADCBC) method. A new Averaged Proportional and Integral Linear Relaxation (APILR) method is also introduced, which modifies the Classic Linear Relaxation (CLR) method to maintain time-averaged values of inflow velocity and outflow pressure. Here, an integral control term is used to remove non-zero equilibrium time-averaged inflow velocities which impinge control over flame position. Both new methods demonstrate their capability in inert and counterflow flames test cases. These methods enable the numerical simulation of combustion instabilities at significantly reduced computational expense.

We introduce "matrix product belief propagation" (MP-BP) as a controlled method for the approximate contraction of two-dimensional tensor networks and graphical models, which generically leads to a quadratic reduction of errors relative to existing methods: at similar computational effort, the number of accurate digits is asymptotically doubled. For infinite systems, MP-BP can be understood as a practical implementation of Baxter's corner-transfer-matrix method; for reflection-symmetric networks, it further coincides with the boundary matrix-product-state and corner-transfer-matrix renormalization group algorithms as conventionally implemented. In the limit of unit matrix-product rank, MP-BP is equivalent to belief propagation, and more generally, can be understood as a generalization of BP in which the "messages" live on surfaces and are assumed to have a matrix-product factorization. We further extend the quadratic improvement to the computation of expectation values, and numerically demonstrate the qualitatively improved convergence for a variety of classical and quantum 2D tensor network problems.

We present $\texttt{VarFlExI}$ (Variable Fidelity Unsteady Flow-FEniCS Exchange Interface), a modular aeroelastic solver for modeling two-way fluid-structure interaction (FSI) of flexible lifting surfaces. The framework employs a reformulated Vortex Particle Method (rVPM) to solve the incompressible Navier-Stokes equations without the need for computationally expensive volumetric meshing, while supporting variable-fidelity aerodynamic modeling using the $\texttt{FLOWUnsteady}$ framework. On the structural side, a Reissner-Mindlin plate formulation is discretized using the finite element method and integrated in time through the generalized-$\alpha$ method, with the nonlinear equilibrium equations solved using a Gauss-Newton procedure within the $\texttt{FEniCS}$ framework. Fluid and structural solvers are coupled through an explicit staggered partitioned scheme, ensuring conservation of virtual work for load and displacement transfer across the non-matching interface. To accommodate the multi-representative nature of the aerodynamic loads and geometry, the interface coupling employs separate work-conservative force and reverse-geometry transfer operators via a common intermediate interface. The framework is validated against water-tunnel experiments, demonstrating accurate prediction of the coupled aeroelastic response rather than independent validation of the constituent solvers. The computational efficiency of the meshless aerodynamic solver enables simulations at significantly lower computational cost while maintaining accuracy. The solver is further evaluated through sensitivity analyses and parameter studies spanning different flow conditions, structural properties, and coupling parameters.

The Nielsen-Ninomiya theorem dictates that Weyl nodes must appear in pairs of opposite chirality to preserve global charge neutrality. However, in crystals, specific crystalline symmetries can stabilize multi-Weyl nodes, circumventing this pairwise constraint and enabling compensated Weyl complexes with mixed chiral charges. The minimal configuration of this type is a triplet Weyl complex (TWC), comprising exactly three Weyl nodes. Here, we systematically investigate the symmetry conditions required to realize TWCs. By screening all 1651 magnetic space groups (MSGs) in both spinless and spinful systems, we establish that: (i) Only TWCs with charge magnitudes of $\{1,1,2\}$ and $\{1,2,3\}$ are permitted; (ii) the $\{1,1,2\}$ configuration can be realized in 166 spinless MSGs and 70 spinful MSGs; and (iii) the $\{1,2,3\}$-TWCs, which has not been reported before, can occur in 10 MSGs for both spinless and spinful cases. We explicitly demonstrate the existence of $\{1,2,3\}$-TWC in a tight-binding model. Furthermore, we present the first electronic realization of $\{1,1,2\}$-TWC topological semimetal state in the chiral carbon allotrope DZQH-C$_{36}$, in which the three Weyl nodes form a collinear configuration, leading to a characteristic “S”-shaped surface Fermi arc pattern. Our findings uncover novel topological states featuring mixed chiral charges and provide guidance for exploring their physics in concrete material systems.

As quantum hardware scales to larger devices, the classical software layers that interface with it must evolve in step. Postprocessing routines developed and tested primarily in simulator settings can encode assumptions that no longer hold on utility-scale devices, leading to data loss that can be difficult to detect from high-level model outputs alone. We present a case study of SamplerQNN, the sampling-based quantum neural network class in the Qiskit Machine Learning library. Here, the postprocessing method applies a filter that assumes measurement bit-strings are in virtual qubit space. On our quantum hardware runs, where bit-strings span over 100 physical qubits, this filter led to the loss of 85 to 99.6% of valid measurement shots, depending on the transpiler's qubit placement. The resulting probability vector is unnormalised, allowing distorted prediction and loss values to propagate through the model without an API-level warning. We demonstrate the impact across five experiments on two IBM backends: for inference, accuracy drops from 0.94 to 0.39 on the same raw measurements; for training, the loss signal is compressed by 22 to 27$\times$, substantially reducing the sensitivity of the optimiser to the objective landscape. The behaviour arises in all released versions of the library (0.8.4 to 0.9.0). We implemented a layout-based marginalisation fix, merged into the GitHub codebase as Pull Request \#1041, that makes SamplerQNN postprocessing forward-compatible with current and upcoming hardware.

We use a combination of path integral and lattice dynamics methods to calculate the thermal conductivity of solid parahydrogen. Path integral molecular dynamics is first used to calculate a centroid potential of mean force at each temperature, on which a harmonic phonon basis is constructed using the finite displacement method. We then calculate anharmonic force constants on the centroid potential and solve the linearised phonon Boltzmann transport equation for the thermal conductivity. The resulting renormalised phonon dispersion curves, heat capacity, and thermal conductivity are all in remarkably good agreement with experimental measurements. We find that the heat transport is dominated by collective relaxon modes at low temperatures and cannot be captured by making the phonon relaxation time approximation. Indeed, quantitative agreement with the experimental thermal conductivity is only achieved when the nuclear quantum effects in the centroid potential of mean force, phonon boundary scattering, and hydrodynamic transport are all explicitly considered. This is also likely to be the case for other quantum crystals.

Accurate nano-photonics simulations of large scale devices like optical metasurfaces require high accuracy reduced models for the device constituents. We present an automated framework for the optimization of Global Polarizability Matrix (GPM) models, which represent a complex scatterer as a small set of non-local effective dipoles. Our goal is to find the most frugal model that reproduces a particle's scattering response within a user-defined accuracy. The method iteratively removes redundant dipoles while re-adapting the positions of the remaining ones via gradient based optimization, stopping at the smallest model that still meets the target. Automatic differentiation, combined with an untrained neural network that reparametrizes the dipole positions, helps to place the dipoles at physically intuitive locations. We demonstrate the versatility of this approach across diverse geometries, from two dimensional ridges over simple spheres to complex three-dimensional particles, achieving compression factors of typically two orders of magnitude compared to full-wave simulations, for target accuracies in the order of few percent. We finally demonstrate how accurate, frugal effective models enable large-scale meta-deflector optimization without periodic approximations. This robust recipe for constructing frugal effective models paves the way for the rapid simulation of large-scale photonic assemblies, required for example for metasurface design.

Running an unfamiliar nuclear-physics code is rarely difficult because of the physics alone. One must find and build the program, learn its input conventions, and decide whether a plausible output is actually correct. A general-purpose coding agent helps with the first two tasks but may make the last one harder: it can write an input file that runs with the wrong physical convention. FUSION addresses this problem with code-specific skills. A skill obtains the code from its public source, starts from a verified input, runs and parses the calculation, records known failure modes, and must reproduce a stated benchmark to a stated tolerance before reporting a result. The current release covers twenty codes, spanning optical models and reactions, nuclear structure, fission and statistical models, astrophysics and R-matrix analysis, and heavy-ion transport. It also includes an offline, searchable collection of 61 167 pages derived from the nucl-th literature. User notes and credentials remain outside the public repository. FUSION is available under the MIT license at https://github.com/jinleiphys/FUSION; documentation is at https://vibeinscience.com. Here I describe the design, the checks behind the current release, and one complete calculation from input to comparison with measured data.