Computer Physics Communications

Recent years have seen a reawakened interest in simplified solution schemes for nanophotonic scatterers, as they allow for significantly faster and more efficient simulations of nanoparticle clusters and arrays when compared to full Maxwell solvers. Examples are the transition or T-matrices, which expand the incoming and scattered electromagnetic fields in terms of spherical multipoles, or numerical dipoles, which describe the scatterers’ response through a collection of electric and magnetic dipoles coupled together through a generalized polarizability matrix, or P-matrix in short. In order to obtain the T- and P-matrices, one has to employ a computational Maxwell solver. Here we suggest a hybrid Maxwell solver scheme that combines computational Maxwell solvers with T- and P-matrices under a common roof. For each scatterer the simulation scheme can be chosen in an interchangeable manner, and the different solvers interact with each other through solver-independent electromagnetic fields. Our approach is expected to be beneficial for fast simulations and prototyping, where in the testing stage a fast solver is used and in the production stage one can switch to any other Maxwell solver by simply changing a few lines of code. We have implemented a hybrid Maxwell solver into our nanobem toolbox, which is based on a Galerkin implementation of the boundary element method.

We introduce cuSkyrmion, a 3-dimensional Skyrme model computation and visualization software, that is written in CUDA C for rapid computation and visualization of especially the arrested Newton flow algorithm. The programme is interactive and lets the user construct Skyrmions either with configuration files, specifying coordinates, or simply at run-time using the keyboard and mouse. Rational map ansatz constituent Skyrmions can be inserted at any time and a random generator can produce a stochastic initial configuration. The software is composed into three main modules being a computational module, a rendering module and a main programme. The rendering/visualization module can readily be used by other computational modules and a Python-fork, skyrmion_solver, has been developed demonstrating the re-usability of the code.

Gaussian Type Functions (GTFs) are widely used as basis sets in electronic-structure calculations. Under many conditions, it is advantageous to express them in their Real Solid Harmonic (RSH) version, which reduce basis-set redundancy and facilitate the interpretation of results. A variation of the McMurchie–Davidson method directly utilizes RSH GTFs; however, evaluating the corresponding Hermite expansion coefficients involves intricate recurrence relations that complicate their implementation and maintenance in electronic-structure codes. To manage this, closed-source programs have used symbolic algebra tools, while public solutions are not available. We present AxolotE, an open-source framework for the automated generation of these coefficients and the corresponding C++ source code. We propose a general algorithm that focuses on the requirements imposed on indices at each recursion step rather than a fixed sequential ordering, simplifying its implementation and analysis. This tool, developed using Mathematica and Python modern symbolic algebra engines, enables reproducible derivation, validation, and code generation for arbitrary angular momentum, being tested up to ℓ=h. Benchmarks expose that the choice of symbolic algebra engine influences the compactness of the generated code and its computational performance, while preserving numerical accuracy. The resulting software will facilitate systematic comparison of Real Solid Harmonic integral implementations, providing ready-to-integrate routines for electronic-structure applications.

Melting temperature calculation is one of the important topics in computational materials science. In high-throughput in silico screening and artificial intelligence assisted design of materials, it usually requires a rapid and autonomous assessment of the melting temperature of the target. Unfortunately, molecular dynamics (MD) simulations of the melting point require many cumbersome and manual operations, making large-scale calculation of the melting point challenging. In this work, we introduce MyTm, a toolkit that employs MD to automatically determine the melting point. The method is fully modularized, and by combining these modules, the program enables fully automated melting calculations by using commonly adopted approaches, including the direct-heating method, the void method, the modified void method, the solid–liquid coexistence method, and the Z method. Moreover, a machine learning (ML) method is proposed employed to recognize and classify the solid-like and liquid-like atoms, which effectively resolve the low accuracy issue in conventional classification approaches, thus making the automated high-throughput pipeline of melting-point calculation possible. The robustness and efficacy of MyTm have been demonstrated by several well-studied systems.

We present Mumax3-cQED, a code for modeling magnon–polaritons in realistic experimental settings. It is an extension of the widely used micromagnetic simulation package Mumax3, building on its GPU-accelerated implementation. To our knowledge, Mumax3-cQED is the first numerical framework that enables the self-consistent simulation of hybrid cavity–ferromagnet dynamics at the micromagnetic level within a single model, without treating the cavity and magnetic subsystems separately. We validate the implementation by benchmarking against numerical and analytical results from the Dicke model in both the paramagnetic and superradiant phases. Mumax3-cQED accurately captures non-equilibrium dynamics and relaxation toward equilibrium. The code also enables the straightforward calculation of hybrid magnon–photon polaritons, as demonstrated by quantitative agreement with experimental observations. Finally, we highlight the potential of Mumax3-cQED for designing magnon–cavity experiments, particularly in scenarios involving strongly inhomogeneous cavity fields or complex magnetization configurations. The software is open-source and provides a versatile tool for studying microscopic saturated ferromagnets as well as spin textures, including domain walls, vortices, and skyrmions.

The electrostatic potential of a point charge inside a grounded conducting sphere is a canonical boundary-value problem with an exact image-charge Green-function solution and an equivalent separated representation in Legendre polynomials. This article presents a reproducible computational framework that evaluates both representations, generates machine-readable data, regenerates publication-quality figures, and validates primary and post-processed electrostatic quantities. In response to the need for validation beyond exact-solution benchmarking, the revised framework also contains an independent cell-centred finite-volume solution of the singularity-subtracted reaction potential. It evaluates field and boundary errors, induced surface charge, image energy, physical radial force, multipole decay, computational cost, spatial error distributions, algebraic and boundary residuals, charge conservation, and inter-grid differences. The Legendre calculations improve monotonically with multipole order over the tested cases, while the independent finite-volume results display approximately second-order refinement and agree with the analytical and Legendre reaction fields. Residual and inter-grid indicators provide practical diagnostics when an exact solution is unavailable. The package is designed for computational electrostatics, spectral approximation, and verification of numerical Poisson and Laplace solvers.

Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We present RLABC (Reinforcement Learning for Accelerator Beamline Control), an open-source Python framework that automatically transforms standard Elegant beamline configurations into reinforcement learning environments. RLABC integrates with the widely-used Elegant beam dynamics simulation code via SDDS-based interfaces, enabling researchers to apply modern RL algorithms to beamline optimization with minimal RL-specific development. The main contribution is a general methodology for formulating beamline tuning as a finite-horizon decision process: RLABC automatically preprocesses lattice files to insert diagnostic watch points before each tunable element, constructs a 57-dimensional observation from beam statistics, correlation information, and aperture constraints, and provides a configurable reward function for transmission optimization. The environment follows the Gymnasium interface and can therefore be coupled to continuous-control RL algorithms; the present study evaluates DDPG and implements an optional stage-learning curriculum. Validation on a test beamline derived from the VEPP-5 injection complex (37 control parameters across 11 quadrupoles and 4 dipoles) demonstrates that the framework successfully enables RL-based optimization, with a Deep Deterministic Policy Gradient agent achieving 70% particle transmission. The framework’s stage learning capability allows decomposition of complex optimization problems into manageable subproblems and provides an optional warm start for later training stages. The complete framework, including configuration files and example notebooks, is available as open-source software to facilitate adoption and further research.

We present HELIOS (HomogEneous and Layered medIa Optical Scattering), an open-source surface integral equation (SIE) software designed for modeling light scattering by particles embedded in homogeneous or layered media and periodic backgrounds. The code implements the Poggio-Miller-Chang-Harrington-Wu-Tsai (PMCHWT) formulation that has demonstrated exceptional reliability in solving scattering problems with penetrable objects. Domain boundaries are discretized using triangular meshes, upon which the electric and magnetic surface current densities are expanded using the Rao-Wilton-Glisson (RWG) basis functions. For periodic structures, such as photonic crystals and metasurfaces, HELIOS employs Ewald’s transformation to efficiently evaluate the infinite series associated with 2D lattices. Regarding stratified media, the code utilizes a matrix-friendly approach for the layered media Green’s tensor, computing Sommerfeld integrals and accelerating calculations through a tabulation-interpolation scheme. The source code is implemented in C++, while a Python interface manages the workflow, including simulation setup, solver run, and post-processing. The accuracy and versatility of HELIOS are demonstrated through various examples that cover all its functionalities.

We present PASCHEN-1D (Plasma Advanced Solver with Coupled High-fidelity Emission and external Network), a one-dimensional time-dependent fluid plasma solver developed for self-consistent simulation of gas discharges and plasma breakdown with coupled electrode surface emission and flexible external circuit networks. The code solves drift-diffusion continuity equations for electrons and ions together with Poisson’s equation. It is dynamically coupled to lumped RLC circuits, which self-consistently treat plasma transport, plasma-surface interaction, dielectric effects, and circuit response within a single framework. The electrode emission module includes secondary electron emission, Fowler-Nordheim and Murphy-Good field emission, Richardson-Dushman thermionic emission, and photoemission based on a general, exact quantum mechanical emission theory. A finite-volume formulation with Kurganov-Tadmor fluxes, explicit diffusion, and fourth-order Runge-Kutta time integration is employed to ensure stable transient (sometimes ultrafast) evolution across breakdown and glow regimes. The solver is validated against multiple benchmark cases, including nanosecond pulsed dielectric-barrier discharges, DC breakdown and glow transitions, Paschen curve construction for argon and nitrogen, and picosecond photoemission induced plasmas, with results consistent with published studies. With high-fidelity emission physics and a flexible circuit-coupling framework, PASCHEN-1D provides a versatile and efficient tool for modeling breakdown and transient discharge phenomena.

Wave-based inverse problems, which determine material or structural parameters from measured wavefields, are fundamental to non-destructive evaluation (NDE), biomedical diagnostics, and geophysics. For repeated-query applications, deep learning shifts the computational effort from iterative inversion to offline training, enabling low-latency prediction, yet large-scale adoption remains limited by challenges in reproducibility, fair architecture comparison, large-dataset handling, and stable distributed training. This paper introduces WaveDL, an open-source Python framework that addresses these challenges through an integrated pipeline covering memory-efficient data streaming, a model zoo comprising 71 network variants adapted for multi-target regression, synchronized multi-GPU training, soft physics-constraint penalties, and automated deployment export via Open Neural Network Exchange (ONNX). Built on PyTorch and Hugging Face Accelerate, WaveDL enables a complete workflow from raw data to deployed model with a single command, lowering the barrier for domain scientists to apply deep learning without specialized machine learning expertise. The framework is demonstrated using a 120 GB Lamb wave dispersion curve dataset to evaluate out-of-core training and systematic cross-architecture benchmarking for finite-dimensional parameter inversion. WaveDL is freely installable via pip install wavedl.