Peer Revenue Publish or profit

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Physics Physics Computational Physics

physics.comp-ph Watch Computational Physics

Friday 9 October

Computational Physics

Energy-dependent trapping of backscattered helium during magnetron sputtering growth: A molecular dynamics study

Pascal Brault, Anne-Lise Thomann and Alejandro Borroto

Helium incorporation during magnetron sputtering can strongly affect thin film growth and lead to the formation of gas/solid nanocomposites and nanoporous structures. Here, molecular dynamics (MD) simulations are used to investigate the trapping mechanism of helium atoms (He) and the subsequent formation of He bubbles during the sputter deposition of aluminum (Al) and titanium (Ti) thin films. The simulations account for the kinetic energy distributions of sputtered metal atoms and backscattered gas species. Results indicate that He is efficiently incorporated into the growing films, leading to the formation of high-pressure, dense He bubbles, with a much higher He-to-metal atomic ratio in Ti ($\sim$ 60%) than in Al ($\sim$ 12%). More importantly, the simulations reveal a clear energy dependence of He trapping in Ti: background He atoms at thermal energies are essentially not incorporated, whereas efficient trapping starts at an incident energy of approximately 20 eV and increases up to a saturation regime near 100 eV. Simulated thermal desorption spectroscopy (TDS) further reveals distinct release mechanisms in the two investigated metals: He desorption from Al occurs at higher temperatures and is preceded by the removal of surface Al atoms, whereas He and Ti atom desorption starts continuously around 950 K, leading to a porous structure. Comparative simulations with argon (Ar) demonstrate negligible trapping even at comparable kinetic energy or momentum, showing that neither parameter alone accounts for the strong He incorporation observed in Ti. The above results are in very good agreement with what is reported in literature for low-kinetic energy He ion implantation in metal surfaces, suggesting similar insertion mechanisms. They clarify the atomistic conditions governing noble gas trapping during film growth and provide a basis for controlling He incorporation and porosity in films synthesized by magnetron sputtering.
Read the full abstract

Computational Physics · Cross-listed from physics.plasm-ph

Implementation and verification of a new free boundary module for Resistive Wall Modes simulations with the nonlinear 3D full MHD code SpeCyl

Francesco Galeazzi, Daniele Bonfiglio, Luca Spinicci et al. (+1 more)

Self-consistent numerical simulations of free boundary instabilities, such as resistive wall modes (RWMs) and external kink modes, require special attention given their relevance on the path towards magnetic fusion based nuclear reactors. In this work, a new implementation and a verification benchmark are presented for the nonlinear three-dimensional (3D) full magneto-hydrodynamics (MHD) code SpeCyl, which self-consistently combines a free flow 3D treatment for the edge plasma velocity with double resistive wall thin shell magnetic boundary conditions, capable of capturing the essential features of common setups in experimental devices. Two coaxial resistive shells surround the plasma. Vacuum conditions are assumed between and outside the resistive shells, whose relative positions and time constants can be varied on purpose. The implementation is extensively verified against Shafranov's analytic theory, valid for a circular cross section large aspect ratio tokamak and assuming a flat current profile. The correctness of newly implemented Boundary Conditions (BCs) is confirmed by the good agreement obtained for the linear growth rates of (m=2,n=1) RWM and external kink, over a wide range of wall positions, resistive shell time constants, and for different initial reference equilibria. The (m=2,n=1) RWM growth rates are also benchmarked against previous different RWMs modelling and successful validation studies for RFX-mod tokamak plasmas. The new BCs make SpeCyl a flexible and robust tool to investigate free boundary MHD modes in large aspect ratio magnetic confinement configurations, such as those achieved with the RFX-mod machine and expected in RFX-mod2.
Read the full abstract

All authors Francesco Galeazzi, Daniele Bonfiglio, Luca Spinicci, Susanna Cappello

Computational Physics · Cross-listed from cond-mat.mtrl-sci

Predicting Nanofabrication Variance: A Conditional Diffusion Model

Wenhan Zhang, Ben Mills, Oleksandr Buchnev et al. (+1 more)

Micro- and nanostructures fabricated by focused ion beam (FIB) milling invariably deviate from input designs due to systematic and stochastic effects such as ablation-rate fluctuations, and redeposition. As such, nominally identical iterations of a given process yield a distribution of non-identical fabrication outcomes, leading to variations in the physical (optical, mechanical) properties of fabricated structures. Existing models of the milling process typically predict a single deterministic fabrication result, neglecting this variability. Here we present a conditional diffusion model that learns the statistical distribution of process outcomes conditioned on input design, enabling it to predict a realistic distribution of possible output sample structures. These predictions accurately reproduce edge morphology, linewidth variations, and defect probabilities observed in fabricated samples, enabling statistical analysis of milling performance and the identification of dominant structural and process parameter-dependent deformation modes. The work establishes a probabilistic modelling framework for fabrication variability analysis and fabrication-aware design optimization.
Read the full abstract

All authors Wenhan Zhang, Ben Mills, Oleksandr Buchnev, Kevin F. MacDonald

Computational Physics · Cross-listed from cond-mat.mtrl-sci

VN-GraphGG: von Neumann–Mullins–based graph grain growth simulation

Yuntong Huang, Zhihao Li, Shuyang Dai et al. (+2 more)

Efficient simulations of grain growth are essential for investigating the process-structure-property relationships in polycrystalline materials. We introduce a two-dimensional von Neumann-Mullins-based graph grain growth simulation (VN-GraphGG), which couples the grain-scale kinetics with microstructure reconstruction. The polycrystalline microstructure is represented as a graph with grain and junction nodes. The grain-area evolution on the grain graph is governed by the von Neumann–Mullins (VN) law, while a graph neural network is trained to reconstruct the microstructure from the graph. This separation allows kinetic coefficients to be varied without retraining the neural reconstruction model. Topological changes during grain growth such as disappearance of grains and changes in neighbor relations are handled straightforwardly through reconstruction and postprocessing. Simulations demonstrate accurate predictions of VN kinetics and coarsening statistics, and significant speedup over the phase-field simulations. The proposed method provides a foundation for efficient grain growth simulation based on grain-scale kinetics, opening the way to incorporate a broader range of physical mechanisms. An application example is presented for abnormal grain growth, in which kinetic heterogeneity significantly influences the grain microstructure evolution.
Read the full abstract

All authors Yuntong Huang, Zhihao Li, Shuyang Dai, Chang Ma, Yang Xiang

Computational Physics · Cross-listed from math.NA

Global Optimization of Beam Profiles for Suppressing the Bump-On-Tail Instability

Sandro Mignelli and Lukas Einkemmer

A particle beam with constant intensity that propagates into a plasma triggers a bump-on-tail instability. It has been demonstrated that beam modulation can be used to significantly reduce this instability. However, finding the optimal beam profiles that accomplish this is a complex optimization problem that suffers from an extremely rugged optimization landscape and a relatively high-dimensional parameter space. In this paper, we evaluate a number of optimization algorithms and find that population based methods (in particular, CMA-ES and Differential Evolution) perform best. In particular, we investigate beam profiles where the beam is either completely turned ON or OFF. This configuration is easier to accomplish experimentally, but leads to a higher-dimensional and thus more challenging optimization problem. We obtain ON/OFF beam profiles that reduce the instability by more than 80% and thus perform significantly better than what has been reported in the literature.
Read the full abstract

Computational Physics · Cross-listed from cs.LG

Recovering Irreducible Three-Body Interactions with Lagrangian Neural Networks

Sena Kalabalik, Sukru Caglar, Abdullah Umut Hamzaogullari et al. (+1 more)

Lagrangian Neural Networks (LNNs) learn mechanical systems from trajectory data by parameterizing a scalar Lagrangian and deriving the dynamics through the Euler-Lagrange equations, biasing the model toward motion that respects the variational structure of mechanics. The interactions studied so far have been pairwise-additive, leaving open whether LNNs can recover an irreducible many-body interaction from motion alone. We address this using the Axilrod-Teller triple-dipole interaction, the leading three-body dispersion term, whose dependence on the collective geometry of three particles cannot be represented by any sum of pairwise terms. We prescribe the kinetic energy exactly and learn only the potential, as a function of interparticle distances and angles. Combined with a signal-aware sampling strategy that concentrates training data where the three-body signal is strongest while preserving broad coverage of configuration space, this recovers the interaction from accelerations alone. The learned potential reproduces the radial scaling, angular dependence, and characteristic sign reversal of the Axilrod-Teller interaction, and generates stable trajectories. Embedding it within a pairwise Lennard-Jones potential lets us examine its observability: the three-body term is recovered once its dynamical signature exceeds the residual modeling error, which it does cleanly at amplified coupling but not at the coupling of a real noble gas, bracketing the transition between the two. Finally, symbolic regression on the trained potential, with no assumed functional form, returns the Axilrod-Teller expression with angular numerator, radial law, and coupling constant intact. An LNN with domain-specific structure can thus recover an irreducible three-body interaction from trajectories as a closed-form law, pointing toward learning interaction laws whose structure is not known a priori.
Read the full abstract

All authors Sena Kalabalik, Sukru Caglar, Abdullah Umut Hamzaogullari, Arkadas Ozakin

Computational Physics · Cross-listed from cond-mat.other

Ultrafast laser-stimulated field emission from a metal: a non-equilibrium Green's functions approach

Luciano Jacopo D'Onofrio, Adolfo Avella and Martin Eckstein

Ultrafast laser-assisted field emission from metallic nanotips is a promising route for generating coherent electron pulses. However, existing theoretical models often neglect many-body strong correlation effects in the emitter's electronic structure and its response to the optical field. In this work, we propose a theoretical framework based on the non-equilibrium Green's function formalism to simulate the out-of-equilibrium electron dynamics across a strongly driven matter-vacuum interface, in a one-dimensional model. Treating the tip-vacuum coupling perturbatively, we find that the interplay between strong-field emission and multi-photon emission regimes can be described in terms of a filtering of the emitter's local density of occupied states through field-dressed vacuum propagators. By including electron-electron interactions via the second-Born approximation, we show that correlation-induced modifications in the electronic structure are directly transferred to the far-field spectrum. Finally, by self-consistently solving the Dyson equation at the interface, we uncover a dynamic back-action regime driven by the optical field, characterized by a periodic polarization of the emitter, which reshapes the far-field spectrum. This shows that the non-equilibrium Green's functions approach provides a versatile framework for exploring and analyzing field emission of correlated electrons.
Read the full abstract

Computational Physics · Cross-listed from cs.LG

Low-Rank Kolmogorov-Arnold Generator Flows for Interpretable Operator Learning

Minjong Cheon

Neural operators that learn physical laws from data should generalize beyond their training distribution and expose the learned law in closed form. The Kolmogorov-Arnold neural operator achieves both for position-dependent dynamics by learning a dense pseudo-differential symbol over all positions and frequencies, at a cost that grows with the square of the resolution and with closed forms that must be read out by hand. This dense symbol is unnecessary: a finite-order variable-coefficient differential operator has a symbol whose separation rank is bounded by the number of its coefficient functions, independently of the resolution, and the symbols that this network learns on its own benchmarks have rank two. We introduce the Kolmogorov-Arnold flow (KAFlow), which learns the generator of a physical operator as a low-rank symbol built from univariate Kolmogorov-Arnold functions, fixes the gauge of these functions so that every term has a unique place, integrates the dynamics with exact exponential integrators, and distills its closed form automatically. On the synthetic benchmarks of the Kolmogorov-Arnold neural operator, KAFlow lowers the out-of-distribution loss by 3.4 to 11 times and recovers every coefficient to within 10^-4 without symbolic templates, reaching the precision floor of the data. On long-horizon quantum dynamics, it lowers the state infidelity by up to 6 orders of magnitude, and by 30 times when trained only on position and momentum measurements. KAFlow also learns a variable-order fractional operator 15 times more accurately than existing operator networks and discovers nonlocal, fractional and non-polynomial dispersion relations on which sparse regression fails.
Read the full abstract

Computational Physics

Alter-excitons in Altermagnets: Symmetry, Splitting and Dispersion

Yuanfan Xiong, Liangyu Wang, Xuan Wu et al. (+2 more)

Magnetism provides a fertile platform for discovering emergent quantum phenomena governed by symmetry. Recent developments of spin-space-group (SSG) theory have revealed altermagnets as a distinct class of nonrelativistic collinear magnets with unconventional spin-dependent electronic structures. Here, we demonstrate that altermagnetic symmetry imposes fundamentally different constraints on electronic and excitonic excitations. Although the quasiparticle and two-body Hamiltonians possess identical SSG symmetries, we find that symmetry-protected degeneracies present in the electronic band structure can be intrinsically lifted in the exciton spectrum at specific momenta. This splitting arises solely from the many-body nature of excitons and does not require spin-orbit coupling or noncollinear magnetic order. Combining symmetry analysis with many-body perturbation theory, we predict a novel class of spin-split excitonic quasiparticles, termed alter-excitons, and reveal their characteristic dispersions in altermagnetic systems. Our results establish excitons as a sensitive probe of hidden spin-space symmetries and uncover a new route toward symmetry-engineered many-body phenomena in collinear magnets.
Read the full abstract

All authors Yuanfan Xiong, Liangyu Wang, Xuan Wu, Wei Hu, Jinlong Yang

Computational Physics

All-electron RT-TDDFT with Exact Exchange based on Numeric Atom-centered Orbitals for Condensed Matter

Jianhang Xu, Christopher Shepard and Yosuke Kanai

Real-time time-dependent density functional theory (RT-TDDFT) has become a widely used first-principles method for studying excited-state and nonequilibrium electron dynamics. Hybrid exchange-correlation (XC) functionals, which incorporate a fraction of nonlocal exact exchange, are particularly important for accurately describing processes such as charge transfer and hot-carrier dynamics. However, the high computational cost which accompanies exact exchange, as required in hybrid XC functionals, has largely restricted RT-TDDFT simulations of condensed matter systems to using semi-local XC functionals. In this work, we present an all-electron implementation of exact exchange for RT-TDDFT based on numeric atom-centered orbitals, extending an efficient ground-state resolution-of-identity (RI) framework to the complex, time-dependent Hamiltonians required for real-time propagation. We validate the implementation against an established RI approach, for an isolated water molecule, finding exact agreement for both valence and core (K-edge) excitations. We also demonstrate the method on two extended periodic systems, crystalline silicon and silicon carbide. For silicon, the HSE06 spectrum reproduces previously reported real-time TDDFT results and agrees with experiment to within approximately 0.2~eV, consistent with plane-wave pseudopotential implementations. For silicon carbide, we compute valence and X-ray absorption spectra at the silicon $L$-edge and carbon $K$-edge. These results establish an efficient, all-electron route to hybrid-XC functional RT-TDDFT for both molecular and condensed matter systems.
Read the full abstract

Computational Physics · Cross-listed from math.NA

Riemannian Hessian analysis of Sobolev gradient flows for the Gross–Pitaevskii ground state problem

Shixin Zheng and Xiangxiong Zhang

Sobolev gradient flows for the Gross–Pitaevskii ground state problem are Riemannian gradient descent methods on the sphere under a metric induced by a discrete elliptic operator. We analyze their convergence through the condition number of the Riemannian Hessian at the ground state. Bounds of the Rayleigh quotient of the Riemannian Hessian give a uniform analysis of these methods for different metrics. The uniform analysis applies to the classical metrics, such as the shifted Laplacian and the energy-adaptive metrics. Inspired by this analysis, we also consider three new metrics. One of them is a Thomas–Fermi metric, whose condition number is bounded uniformly in the interaction strength for the one-dimensional harmonic trap and whose iteration count does not grow in the Thomas–Fermi regime in the numerical tests. The performance suggested by our analysis is consistent with three-dimensional numerical tests with up to one billion unknowns, for both Riemannian gradient descent and Riemannian conjugate gradient methods, whose practical performance is also related to the Hessian condition number. The Thomas–Fermi flow needs the fewest iterations, while the metrics inverted exactly by tensor-product solvers are the fastest in computational time on one GPU card.
Read the full abstract

Thursday 8 October

Computational Physics

Polyconvex neural-network surrogates with learned paired directional features for anisotropic hyperelastic homogenization

S. Ares de Parga, A. Cornejo, J. A. Hernández et al. (+1 more)

Approximating the effective anisotropic hyperelastic response of a representative volume element (RVE) requires constitutive expressiveness, mechanical consistency, and computational efficiency. This work develops a polyconvex physics-augmented neural network (PANN) construction based on paired directional features whose orientations, stretch powers, and determinant exponents adapt to the RVE response. The features are composed with convex, componentwise nondecreasing cores, realized as an input-convex neural network (ICNN) or an input-convex Kolmogorov-Arnold network (ICKAN). The complete energies satisfy objectivity, an exactly stress-free reference, two-dimensional polyconvexity, and prescribed growth conditions by construction, and nonnegative energy is certified separately for the selected models. On a multicavity and a single-cavity periodic cell, these energies are compared with an Unconstrained energy, trained identically without the curvature and growth constraints, and with three hyperreduced projection-based reduced-order models (HPROMs), which serve as physics-based references. On the first cell, learning the features lowers the stress error sevenfold, and six features suffice on both. The constrained energies remain below one percent in stress error and in the displacement error of a tensile-coupon FE$^2$ application with a full-order reference, whereas the Unconstrained energy and the HPROMs are more accurate inside the data. Beyond the data, however, the Unconstrained energy loses rank-one convexity, and under confined compression it stops below half of the 500 MPa that the constrained energies reach. These results quantify what the guarantees cost in accuracy and what they secure beyond the data.
Read the full abstract

All authors S. Ares de Parga, A. Cornejo, J. A. Hernández, R. Rossi

Computational Physics · Cross-listed from cond-mat.str-el

Homogeneous to multi-scale inhomogeneous complex magnetism in Ba3LnRu2O9 (Ln = Gd, Ho), Revealed by muSR and Machine-Learned Muon-Site Calculations

S. Ghosh, K. Sharma, E. Kushwaha et al. (+7 more)

We report a comprehensive muon spin rotation and relaxation (muSR) study of the hexagonal 6H-perovskites Ba3LnRu2O9 (Ln = Gd, Ho) to investigate the role of rare-earth anisotropy in the magnetic ground state of 4d-4f coupled systems. Ba3HoRu2O9 exhibits two successive magnetic orderings at TN1 = 46 K and TN2 = 10 K, leading to a complex magnetic ground state, whereas Ba3GdRu2O9, with Gd3+ (L = 0) S-state ions, shows primarily single magnetic ordering at TN = 14.5 K. Zero-field (ZF) and longitudinal-field (LF) muSR measurements reveal markedly different magnetic behavior despite their structural similarity. Ba3HoRu2O9 displays a multi-scale inhomogeneous magnetic state characterized by two-component exponential relaxation, broad local-field distributions, incomplete LF decoupling, and persistent spin dynamics. The muSR results identify two characteristic magnetic energy scales near 46 and 10 K, associated with correlated Ru-Ho interactions and quasistatic magnetic ordering, respectively. Redfield analysis further demonstrates strong local magnetic fields and distributed spin fluctuation timescales, confirming magnetic inhomogeneity. Machine-learning-assisted muon-site and dipolar-field calculations show that the broad field distribution naturally arises from muons sensing distinct coexisting magnetic domains at different structural sites. In contrast, Ba3GdRu2O9 exhibits a comparatively homogeneous antiferromagnetic ground state below 14.5 K, with Gaussian relaxation consistent with a narrow static field distribution. These results establish that the rare-earth ion in Ba3LnRu2O9 is a powerful tuning parameter, driving the ground state from homogeneous to complex, multi-scale inhomogeneous behavior through competing 4d-4f magnetic interactions.
Read the full abstract

All authors S. Ghosh, K. Sharma, E. Kushwaha, M. Kumar, G. Roy, A. Pandey, F. L. Pratt, S. Cottrell, D. T. Adroja, T. Basu

Computational Physics · Cross-listed from physics.ao-ph

Evaluating LLMs for Translating Legacy Fortran Atmospheric Physics to GPU-Accelerated JAX

Iris Linck, John Dennis, John Schreck et al. (+2 more)

Legacy Fortran physics packages in atmospheric models such as CESM, WRF, and E3SM pose a barrier to GPU modernization, while manual porting requires accelerator-programming expertise. We present LAFT (LLM-Assisted Fortran Translation), an agentic framework for converting Fortran atmospheric physics parameterizations into GPU-accelerated JAX while preserving numerical agreement with the Fortran reference. LAFT coordinates source-code analysis, translation, repair, runtime validation, and performance profiling. It combines a deterministic front end for metadata extraction and bridge generation, structured translation prompts, numerical validation, and profiling-guided feedback to identify inefficient execution patterns. The bridge handles array-layout conversion separately from scientific computation. We evaluate LAFT using the Kessler warm-rain microphysics scheme and four LLMs: Claude, GPT, Gemini, and Qwen. All four validated translations reproduce the Fortran reference outputs to near floating-point precision for the evaluated case, but their GPU performance differs substantially with execution design. The Gemini-generated implementation achieves up to a 357-fold speedup over serial Fortran in cached execution at 1,000,000 atmospheric columns, excluding compilation and host-device transfers. The results demonstrate that numerical agreement alone does not ensure efficient GPU execution and motivate combining numerical validation with hardware-aware performance analysis. LAFT provides a practical workflow for modernizing legacy atmospheric physics code through LLM-assisted translation, deterministic interface generation, automated validation and repair, and reproducible profiling.
Read the full abstract

All authors Iris Linck, John Dennis, John Schreck, Karen Stengel, David John Gagne II

Computational Physics · Cross-listed from math.NA

Fourier Transform-Based Computation of Two-Electron Four-Center Coulomb Repulsion Integrals

William Chen, Lior Silberman, Ury Segal et al. (+1 more)

We present a Fourier-based method for evaluating two-electron four-center Coulomb repulsion integrals over Slater-type orbitals. Spherical Fourier-Bessel coefficients of the convolution factors are precomputed and reused. Since the spherical part of the integral can be evaluated directly, there remains only a one-dimensional quadrature problem in the radial direction. For the ten reference integrals reported here, the largest absolute discrepancy is $1.7\times10^{-9}$. A preliminary resident-cache benchmark computes approximately $4.2\times10^5$ unique integrals per second on an NVIDIA V100 at the contraction stage.
Read the full abstract

All authors William Chen, Lior Silberman, Ury Segal, Chen Greif

Computational Physics

Parallel Design of a GPU-Resident Semi-Lagrangian Drift-Kinetic Solver on Unstructured Extruded Tokamak Meshes

Mark F. Adams

Semi-Lagrangian (SL) schemes advect a distribution function along characteristics and remap it to a fixed grid. They are unconditionally stable without a global solve, and with discontinuous Galerkin (DG) integration the transfer along a magnetic field line conserves mass exactly. This paper presents the parallel and GPU design of a 3X+2V SLDG drift-kinetic tokamak solver on unstructured, flux-surface-aligned poloidal planes of shaped cross section, extruded toroidally. Transport follows magnetic field lines across the planes, and each velocity channel advances a fixed distance per step, proportional to its parallel speed, so the halo depth a channel needs grows with its speed. Velocity-dependent ragged halos exploit this, sending each channel only its own upwind depth. The 4D decomposition includes the toroidal angle, the 2D poloidal plane and the magnetic moment, and leaves the parallel velocity whole: no stage transports across the magnetic moment, while a parallel-velocity partition would put a halo inside the acceleration stage. On NVIDIA A100s the design strong-scales over the magnetic-moment axis from one to 12 four-GPU nodes, and a weak series of three points refining all five phase-space dimensions together, 16x in total per series point, holds a weak-scaling efficiency of 75% on 16 GPUs and 57% on 256, from a one-GPU base case. The pointwise interpolations within the poloidal plane do not conserve mass, and the full step's mass drift stays below 2.1x10^-5 at every series point. The design was implemented with an AI code assistant under a machine-checked regression discipline, in which every change is held to one of four exactness classes, from bit-identical to a physics rate within a stated tolerance.
Read the full abstract

Computational Physics · Cross-listed from cond-mat.supr-con

Enhanced Coulomb pseudopotentials in high-pressure hydrides from first principles

Kieran Bozier and Chris J. Pickard

In the Eliashberg theory of superconductivity, Coulomb interactions oppose the phonon-mediated attraction and suppress the superconducting critical temperature $T_{\mathrm{c}}$. However, most first-principles predictions continue to treat this repulsion with an empirical Coulomb pseudopotential $\mu^*_{\text{ME}}$ in the range 0.1-0.15. Here we present first-principles calculations of the Coulomb parameter $\mu$ and pseudopotential $\mu^*_{\text{ME}}$ in superconducting hydrides across a range of pressures. The screened Coulomb interaction is evaluated at the random phase approximation level with the Mantel code. We demonstrate that several hydride systems, including H$_3$S, YSbH$_6$ and Mg$_2$IrH$_6$, have strong Coulomb repulsions driven by a large density of states at the Fermi energy, $N_{\mathrm{F}}$. For Mg$_2$IrH$_6$ we obtain $\mu=0.62$ and $\mu^*_{\text{ME}}\approx0.39$, leading to a 40 K suppression in $T_{\mathrm{c}}$ relative to a standard $\mu^*_{\text{ME}}=0.16$. Our obtained $\mu^*_{\text{ME}}$ is far above the conventional range and, accounting for the energy dependence of the density of states, resolves the factor-of-two discrepancy between $T_{\mathrm{c}}$ estimates of previous studies. Across all systems and pressures studied, we find that $\mu$ closely tracks $N_{\mathrm{F}}$, suggesting that commonly used $\mu^*_{\text{ME}}$ values lead to overestimated $T_{\mathrm{c}}$ in materials where the density of states at the Fermi energy is large.
Read the full abstract

Computational Physics · Cross-listed from cs.LG

Bi-FORK: Generative Modeling of High-Dimensional Bifurcating Systems

Anna Zimmel, Fleur Hendriks, Markus Holzleitner et al. (+4 more)

Bifurcations are ubiquitous in physical systems, from structural buckling to fluid and climate dynamics, yet they remain largely unexplored in deep learning. At a symmetry-breaking bifurcation, a single input admits multiple equally valid solutions, violating the one-to-one assumption underlying most learned physical surrogates. We introduce Bi-FORK, a generative framework for learning these one-to-many solution maps in high-dimensional systems. Bi-FORK generates complete trajectories through latent flow matching, preserving space and time coherence, and uses repulsion-guided sampling to recover distinct solution branches in a single amortized pass. We evaluate Bi-FORK on buckling beams, mechanical metamaterials, and Allen-Cahn phase separation, spanning continuous, discrete, and field-valued bifurcations with discretizations up to 260,000 points. Bi-FORK recovers the multimodal solution structure while scaling several orders of magnitude beyond prior approaches, opening generative modeling to high-dimensional bifurcating physical systems.
Read the full abstract

All authors Anna Zimmel, Fleur Hendriks, Markus Holzleitner, Florian Sestak, Martin Weichselbaumer, Vlado Menkovski, Johannes Brandstetter

Computational Physics · Cross-listed from math.NA

A neural characteristic mapping method: Lagrangian PINNs based on flow maps for transport-dominated problems

Martin Campos Pinto, Victor Fournet, Emmanuel Franck et al. (+2 more)

We propose a novel numerical method for transport-dominated problems using Physics-Informed Neural Networks (PINNs). In such problems, the solution can develop large gradients and fine structures over time. This is particularly challenging for classical PINNs, which represent the solution itself. In our approach, rather than the solution, we choose to approximate the flow map of the equation. The solution is then recovered by pulling back the exact initial condition, so that its fine structures are produced by the map rather than represented by the network. Our method is constructed by approximating the flow map of the equation with multiple PINNs, one per time subinterval, and composing them using the semigroup property of the flow, so that each network only has to represent a map close to the identity. To highlight the advantages of our method compared to classical PINNs or classical numerical schemes, we present numerical results on the linear advection equation, the incompressible Euler equation in vorticity formulation, the Vlasov-Poisson equation, and finally a drift-kinetic equation.
Read the full abstract

All authors Martin Campos Pinto, Victor Fournet, Emmanuel Franck, Philipp Krah, Victor Michel-Dansac

Computational Physics · Cross-listed from cs.AI

Prior or Feedback? What an LLM Uses When Adapting Neural Operators

Julian Chan and Javier Mora Jimenez

Do LLM scientific agents rely only on their initial task context, or do they adapt their decisions in response to experimental feedback? We study this question in neural operator adaptation, where a large language model (LLM) selects fine-tuning configurations under a limited trial budget. Across transfers within and between partial differential equation (PDE) families, the LLM achieves lower held-out test nRMSE than random search and Bayesian optimisation in nearly every matched comparison. Endpoint performance alone cannot distinguish what happens, so we verify each attribution with controlled interventions. Before observing any validation score, the LLM's first configuration already ranks near the top of the corresponding random-search pool, indicating a useful initial bias. A complementary cold-start intervention shows that the selected base learning rate shifts with the PDE description. Once feedback becomes available, reassigning validation scores among evaluated configurations changes the next proposal in every case tested, whereas a value-preserving rewrite produces no comparable aggregate effect. These interventions establish that the LLM's decision-level actions respond to the given task and observed outcomes, showing that it combines a task-dependent prior with sensitivity to experimental feedback.
Read the full abstract

Computational Physics

Ghost tasking for parametrized Gaussian Processes solving linear differential equations

Johanna Moser, Christopher Albert and Sascha Ranftl

Physics-informed machine learning has gained significant attention in recent years. In regimes of limited data, parametrized Gaussian processes have become popular. Existing approaches, however, often face limitations, such as requiring parametrizable (also called controllable) systems or a large number of output tasks. In this work, we introduce a systematic procedure we call "ghost tasking", using auxiliary tasks to circumvent these limitations. We prove that such ghost tasks can render any non-parametrizable system effectively parametrizable, enabling algorithmic construction of parametrized Gaussian Processes while keeping the number of required tasks (i.e. output dimensions) and latent functions low. We find that ghost tasking performs especially well in an inverse problem setting, even with very few available data. We show the usage and power of ghost tasking in three experiments, providing systematic comparisons to the only other currently available method applicable to all experiments. We provide necessary syntax and explications for two computer algebra programs that compute parametrizations for systems with polynomial or rational coefficients. Our theoretical results extend to systems with meromorphic functions.
Read the full abstract

Computational Physics · Cross-listed from physics.bio-ph

Femtosecond Three-Dimensional Imaging of Single-Protein with Hard X-ray Laser

Paul Lourdu Xavier, Andrew J. Morgan, Johan Bielecki et al. (+66 more)

The extremely intense pulses of X-ray free-electron lasers (XFELs) have enabled imaging of radiation-sensitive samples, such as macromolecular microcrystals, beyond radiation damage limits. These sources have the potential to deliver biomolecular single-particle imaging, similar to cryo-electron microscopy but without the need for cryo-fixation and with temporal resolution from femtoseconds to milliseconds. While this possibility was recognized before XFELs were built, the biological single-particle imaging work-flow has previously only been demonstrated on large virus particles. Based on decades of improvements in X-ray beam focusing, particle delivery, diffraction detection, and advanced analysis, here we demonstrate imaging of a single molecular complex, the giant-hemoglobin erythrocruorin (Ery) with X-ray laser pulses. Two-dimensional classes of diffraction patterns could be reconstructed to 15 Angstrom resolution, and 3D images to approximately 20 Angstrom, while the 3D merged intensity in reciprocal space extended beyond 20 Angstrom. The resolution discrepancy is likely due to heterogeneity caused by gas-phase compaction of the complexes. With increased throughput, this approach could be used to reveal in-situ structural details during mass spectrometry studies of biomolecules, while improvements in sample delivery may provide ultrafast snapshot imaging of biological single-particles in their native-state beyond the limitations of radiation damage.
Read the full abstract

All authors Paul Lourdu Xavier, Andrew J. Morgan, Johan Bielecki, Amit K. Samanta, Simon Dold, Chufeng Li, Xinyue Gao, Spencer K. Passmore, Ruslan P. Kurta, Tim B. Berberich, Emiliano De Santis, Wenhui Zhang, Juncheng E, Faisal H. M. Koua, Romain Letrun, Luca Gelisio, Oleksii Turkot, Egor Sobolev, Lukas V. Haas, Jannik Lübke, Jingxuan He, Madeline B. F. Memovich, Kevin Janson, Stefanie Lenzen, Safi Rafie-Zinedine, Mansi Butola, Oleksandr Yefanov, Fabian Trost, Chan Kim, Tomas Popelar, Tokushi Sato, Yevheniy Ovcharenko, Rebecca Boll, Sergey Usenko, Thomas M. Baumann, Alberto De Fanis, Björn Senftleben, Matteo Porro, Andrea Castoldi, Ibrahym Dourki, Cyril Danilevski, Ekaterina Round, Christina Schmidt, Huijong Han, Roberto Alvarez, Ervin Chia, Cornelia Cazey, Carolin Seuring, Ruojie Sha, Maia Azubel, David A. Bushnell, Sébastien Boutet, Sergey Ovchinnikov, Adrian P. Mancuso, Michael Meyer, Richard Bean, Anton Barty, Filipe R. N. C. Maia, Richard A. Kirian, Thomas D. Grant, N. Duane Loh, Carl Caleman, Erik G. Marklund, Nadia Zatsepin, Andrew V. Martin, Saša Bajt, Roger D. Kornberg, Jochen Küpper, Henry N. Chapman

Computational Physics · Cross-listed from cond-mat.mtrl-sci

Can end-to-end learning from raw electronic structure explain magnetic anisotropy?

Rafał Topolnicki, Jan Navrátil and Piotr Błoński

Machine-learning models trained directly on electronic spectra can predict spin–orbit-driven properties, yet models with comparable predictive accuracy may learn different electronic dependencies. We examine this problem using magnetic anisotropy in atomic-scale magnets and a validation strategy designed to assess the physical relevance of learned spectral relationships. We compare a bidirectional gated recurrent unit and a one-dimensional convolutional neural network, both trained to predict magnetic anisotropy energy (MAE) from spin- and orbital-resolved scalar-relativistic densities of states (SR-DOS). Despite comparable in-domain (ID) accuracy, the architectures learn only partly overlapping spectral dependencies. Shapley additive explanations identify spectral features shared between the two architectures that can be associated with plausible spin–orbit-coupling pathways consistent with second-order perturbation theory (PT2). Adding PT2-derived MAE contributions to the model inputs has little effect on ID performance but can improve transfer beyond the training domain, with gains differing between architectures. Controlled spectral perturbations further demonstrate that similar attribution patterns do not imply the same functional dependence of the predicted MAE on spectral weight. The contrasting responses provide a functional context for architecture-dependent out-of-domain transfer. End-to-end learning can identify candidate electronic signatures of magnetic anisotropy. Their credible microscopic interpretation, however, rests on convergent evidence from predictive performance, model comparison, physical theory, controlled spectral interventions, and evaluation under distribution shift.
Read the full abstract

Computational Physics · Cross-listed from physics.optics

Learning qBIC Resonances across Metasurface Families in Dielectric Fourier Space

Shuangteng Lei, Li Yu, Tianxin Li et al. (+1 more)

Bound states in the continuum (BIC) metasurfaces are typically described by geometry-specific parameters, hindering cross-geometry comparison, while ultranarrow qBIC features are easily diluted in full-spectrum learning. Here, 2015 samples from seven dielectric metasurface families are mapped to a shared reciprocal-lattice grid, where two frozen low-order Fourier channels capture resonance shifts with mean within-branch $R^2$ values of 0.871-0.999. Field-level analysis of two representative branches further confirms that these shifts are consistent with the Maxwell-Fourier perturbation picture. A five-channel K-space backbone models the broadband spectrum, while a local complex K-space expert parameterizes the qBIC resonance through a differentiable Fano layer. The expert reduces resonance-position mean absolute error (MAE) from 3.2 to 0.95 nm and the resonance-depth error by 14-fold on a geometry-blocked test set. The same coordinate supports spectrum-to-structure reconstruction.
Read the full abstract

All authors Shuangteng Lei, Li Yu, Tianxin Li, Wei Lu

Computational Physics · Cross-listed from physics.optics

Simulations of Fluorescence and Energy Transfer in Periodic Systems: Pitfalls and Considerations for Bound States in the Continuum

Ilia Lykov, Sergejs Boroviks and Olivier J. F. Martin

Bound states in the continuum (BICs) combine high quality factors with strong electromagnetic-field localization and are therefore widely explored for enhancing light–matter interactions. However, large field amplitudes do not directly translate into useful fluorescence enhancement or energy transfer enhancement between two donor–acceptor fluorophores. Here, we investigate this question for a hybrid plasmonic–photonic resonant waveguide grating supporting a symmetry-protected BIC. We emphasize some of the pitfalls associated with the numerical simulations, especially the utilization of periodic boundary conditions for fluorescence. Strong BIC-related fields can enhance acceptor absorption while useful fluorescence enhancement remains moderate. Extensive simulations and the comparison of different figures of merit, indicate that, while a BIC can suppress radiative leakage, it can also redirect a large fraction of the energy into Ohmic losses. As a result, the useful fluorescence enhancement of donors remains moderate, whereas absorption by acceptors can be strongly enhanced in Förster resonance energy transfer (FRET) experiments. It is essential to separate near-field FRET from long-range energy transfer and reabsorption mechanisms, both of which are mediated by the delocalized BIC mode. Finally, results obtained for finite gratings are compared with infinite periodic simulations and conceptual inconsistencies explained. This work provides a consistent framework for interpreting fluorescence, absorption, and energy transfer in BIC-based periodic nanophotonic systems.
Read the full abstract

Computational Physics · Cross-listed from physics.optics

Inverse Design of Integrated Photonic Components for Visible-Light Applications

Christoph Stockinger, Marko Šimić, John O. Adeniran et al. (+3 more)

Photonic integrated circuits (PICs) operating in the visible spectral range are crucial for quantum technologies, optical sensing, and nonlinear optics applications. However, their development is hampered by limited integration density and operational bandwidth, linked to low refractive index contrast and restricted parameter space in intuition-based PIC design. We address these bottlenecks with an adjoint-based, fabrication-aware inverse design workflow tailored for visible-spectrum photonics. By expanding the capabilities of FDTDX, an open-source, GPU-accelerated FDTD solver, we provide a versatile memory and runtime efficient inverse design platform. Demonstrating this approach on a silicon nitride (Si3N4) platform, we design, fabricate, and experimentally validate ultra-compact, broadband components for light routing, multiplexing, and polarization control. This work provides a scalable framework for high-density, high-performance visible-light PICs.
Read the full abstract

All authors Christoph Stockinger, Marko Šimić, John O. Adeniran, Natale G. Pruiti, Marc Sorel, Peter Banzer

Wednesday 7 October

Computational Physics

Fast Angular Sweep Monostatic Radar Cross Section Computation via Model Order Reduction

Noelia Naranjo and Valentin de la Rubia

The radar cross section (RCS) is a fundamental parameter in radar engineering, as it determines the detectability of a given target by a radar system. Increasing interest in very low observability (VLO) targets has arisen recently. As a result, especial effort is put in developing numerical tools to accurately predict the electromagnetic scattering from perfect electric conductor objects. Integral equation methods are commonly used in this task. However, full-wave computation of the monostatic RCS at a single angle can be rather time-consuming, nevermind computing the RCS in a specific angular interval with a fine sampling. In fact, this is what is needed in industrial applications. A reduced-order model for fast full-wave monostatic RCS evaluation is proposed to easily achieve fine details in the desired angular domain. An open source integral equation solver, taken into account as a black box, is used to get the electromagnetic scattering in non-penetrable objects at some specific incident angles. Several radar targets will show the possibilities and capabilities of these model order reduction approaches.
Read the full abstract

Computational Physics · Cross-listed from cs.LG

Gen-PINNs: Generative Adversarial Physics Informed Neural Networks for solving partial differential equations

Muhammad M. Akmal, Kamy Sepehrnoori and Michael J. Pyrcz

Physics-Informed Neural Networks (PINNs) are a widely used data-free method for solving Partial Differential Equations (PDEs) using machine learning. With recent advances in Generative Adversarial Networks (GANs), adversarial learning has shown strong capabilities for modeling complex data-driven problems; however, the use of GANs in deterministic physics-informed PDE solutions remains limited. In this work, we first identify limitations of standard PINNs for solving PDEs, including spectral bias, loss imbalance, and optimizer stagnation. We then propose Generative Adversarial Physics-Informed Neural Networks (Gen-PINNs), a unified deterministic residual-adversarial framework designed to improve data-free solutions of PDEs with sharp or shock-front behavior. The generator learns the underlying PDE solution using dynamically weighted physics-informed loss components, while separate discriminators evaluate complementary PDE residual features against ideal zero-residual states. The framework further develops and adapts several methodological components, including a Fourier representation for resolving high-frequency spatial content, an orthonormal spectral diagnostic for quantifying frequency-dependent solution errors, and a modified gradient-based dynamic weighting system for physics, initial-condition, boundary-condition, and adversarial loss objectives. Gen-PINNs is tested against standard PINNs on nonlinear and higher-order PDEs, including the Burgers, Allen-Cahn, and Kuramoto-Sivashinsky equations. The results demonstrate substantial improvements in accuracy and convergence across sharp-front, stiff, and higher-order PDE solutions, highlighting the potential of deterministic residual-adversarial learning as an effective approach for solving challenging nonlinear PDEs.
Read the full abstract

Computational Physics · Cross-listed from physics.med-ph

Technical Preparations for the First Carbon-Ion Therapy Facility in the United States

Jun Tan, Xiaoying Liang, Keith M Furutani et al. (+4 more)

Carbon ion radiotherapy (CIRT) provides greater biological effectiveness than conventional photon therapy because of its high linear energy transfer and its ability to induce complex DNA damage. Mayo Clinic Florida is developing the first CIRT facility in the United States, using a synchrotron-based system with dose-driven continuous scanning (DDCS). Although DDCS improves delivery efficiency by keeping the beam on during spot transitions, it also creates the challenge of accurately modeling the dose delivered during beam motion (move dose), which is not fully addressed in current treatment planning systems. In this work, we present a method for accurate simulation of move dose to support Monte Carlo (MC) dose calculations. The method models realistic beam trajectories, including the characteristic hockey stick motion caused by different scanning speeds in the X and Y directions, and accounts for time-dependent beam intensity with linear ramp-up behavior. The beam path is divided into small segments, each represented by a move source with analytically determined position, intensity, and monitor units. Beam intensity fluctuations are also included using a stochastic model based on measurements. Validation using TOPAS simulations for a 430 MeV/u carbon ion beam in a water phantom showed accurate modeling of move dose and the resulting dose distributions. This method provides a practical framework for MC-based dose calculation in clinical CIRT.
Read the full abstract

All authors Jun Tan, Xiaoying Liang, Keith M Furutani, Justin Park, Sridhar Yaddanapudi, Bo Lu, Chris J. Beltran

Computational Physics · Cross-listed from cs.LG

NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning

Jay L. Kaplan, Samuel Varner, Rebecca Willett et al. (+1 more)

Equivariant graph neural networks have emerged as foundational architectures for machine-learned interatomic potentials, approaching quantum-chemical accuracy at a fraction of the computational cost. These models describe local atomic environments accurately, but finite spatial cutoffs truncate long-range information flow, and stacking message-passing layers can lead to over-smoothing and over-squashing. Existing long-range extensions either prescribe a fixed analytical propagation kernel, restrict long-range communication to scalars or degree-preserving channels, are only approximately equivariant, or incur super-linear computational cost. Combining learnable long-range equivariant transport with multiscale many-body expressivity and efficient scaling for larger systems remains a central challenge. We introduce Neural Equivariant Multipole Operators (NEMORA), a neural equivariant extension of the Fast Multipole Method (FMM) for learning long-range tensorial representations. NEMORA generalizes the FMM's analytical multipole expansion and translation operators to learned equivariant counterparts on an adaptive spatial hierarchy. Its operators couple angular degrees and form many-body interactions across length scales, retaining the FMM's hierarchical organization and analytical radial factors as physical inductive biases while learning data-dependent long-range couplings. NEMORA evaluates in linear time and memory complexity, allowing it to treat larger systems than other long-range methods reaching hundreds of thousands of atoms, and it augments both symmetry-constrained and unconstrained short-range backbones. On non-local benchmarks, it reduces force and energy errors relative to the short-range backbones by over an order of magnitude and up to three orders of magnitude, respectively, which is better than or competitive with existing long-range extensions in accuracy.
Read the full abstract

All authors Jay L. Kaplan, Samuel Varner, Rebecca Willett, Juan J. de Pablo