Computer Physics Communications

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.

We present Version 9 of the freeware MAN (Modal Analysis of Nanoresonators) [https://doi.org/10.1016/j.cpc.2022.108627], a software package for computing, normalizing, and exploiting quasinormal modes (QNMs) to analyze the optical response of electromagnetic resonators. This release substantially extends the capabilities of the original software through three major developments. First, it introduces dedicated models and numerical tools for resonators incorporating two-dimensional materials, including graphene, using a surface-conductivity formulation. Second, it implements a coupled-QNM formalism that computes the complex coupling coefficients between resonator modes directly from the QNMs of the individual resonators. Unlike conventional coupled-mode theory, where these coefficients are often introduced phenomenologically or determined by fitting, MAN evaluates them rigorously from first principles. Third, it provides post-processing tools that determine the Fano parameters governing extinction spectra directly from the computed QNM field distributions. Together, these advances broaden the range of nanophotonic systems accessible to MAN while providing new tools for the quantitative interpretation of complex resonant phenomena.

I present HPRMAT, a self-contained, high-performance R-matrix solver framework for coupled-channel scattering calculations in nuclear physics. It provides the full R-matrix propagation machinery with the same user-supplied-potential interface as standard R-matrix packages, and is additionally a drop-in replacement for the linear algebra routines of Descouvemont's package. It employs direct linear equation solving with optimized libraries instead of traditional matrix inversion, achieving significant performance improvements. The package provides four solver backends: (1) double-precision LU factorization, (2) mixed-precision arithmetic with iterative refinement, (3) a Woodbury formula approach exploiting the kinetic-coupling matrix structure, and (4) GPU acceleration. Benchmark calculations demonstrate that the GPU solver achieves about 15× speedup over the optimized CPU direct solver, and 41× over the legacy inversion-based code, at N=25600. The mixed-precision strategy is particularly effective on consumer GPUs (e.g., NVIDIA RTX 3090/4090), where single-precision throughput exceeds double-precision by a factor of 64:1; by performing the factorization in single precision, with iterative refinement available to recover full double-precision accuracy where needed, HPRMAT overcomes the poor FP64 performance of consumer hardware while retaining the accuracy required for cross-section calculations. This makes large-scale continuum-discretized coupled-channels (CDCC) and coupled-channel calculations accessible to researchers using standard desktop workstations, without requiring expensive data-center GPUs. A single consumer card already handles the largest problems encountered in practice, and for the occasional case that exceeds one card a tested multi-GPU back-end distributes the single-precision factorization across several GPUs and recovers full double-precision accuracy through host-side iterative refinement. CPU-only solvers provide up to about 7× speedup for large matrices (system-dependent) through optimized libraries and algorithmic improvements. All solvers reproduce the reference results well within experimental uncertainties: the double-precision and CPU mixed-precision solvers agree to machine precision, and the GPU single-precision solver to the 10^−3 level in cross sections (with optional iterative refinement available for higher accuracy), validated against Descouvemont's reference code (Comput. Phys. Commun. 200, 199–219 (2016)). HPRMAT provides interfaces for Fortran, C, Python, and Julia.

We present NESTOR (Nesting and Electronic Susceptibility Toolkit for Ordered Responses), a computational framework for evaluating the electronic response of quantum materials using first-principles-derived Lindhard susceptibilities. The code computes static and dynamic response functions, 𝜒⁡(𝐪) and 𝜒⁡(𝐪,𝜔), as well as the joint density of states (JDOS), directly from single-particle eigenvalues and eigenstates obtained from electronic-structure calculations. NESTOR implements orbital-dependent form factors, spin polarization, and spin–orbit coupling, and provides full control over Brillouin-zone sampling and interpolation. It enables quantitative identification of charge-density-wave (CDW) instabilities and other Fermi-surface-driven phenomena in crystalline materials. Benchmarking across representative systems demonstrates close agreement between computed susceptibility maxima and experimentally observed nesting vectors, validating its accuracy, selectivity, and capacity to differentiate non-CDW materials. NESTOR is general and applicable to one-, two-, and three-dimensional systems. Its theoretical precision depends only on the fidelity of the input eigenstates, allowing consistent use with many-body methods such as GW and hybrid-functional. NESTOR provides a computationally efficient and physically rigorous framework for the analysis of momentum-resolved electronic instabilities in quantum materials. NESTOR is open-source and available on GitHub at NESTOR.

We present NAVIS (NAnochannel Velocity and thermal Interfacial Slip), a LAMMPS-Python scripted toolkit for computing the Navier (hydrodynamic) friction coefficient and Kapitza (thermal) resistance at arbitrary solid-fluid interfaces. NAVIS is based on equilibrium molecular dynamics (EMD) methods for calculating the linear response friction and thermal resistance at the interface, as well as the corresponding velocity and temperature slips. The methodology is based on our previous studies (Hansen, et al., Phys. Rev. E 84, 016,313 (2011); Varghese et al., J. Chem. Phys. 154, 184,707 (2021); Alosious, et al., J. Chem. Phys. 151, 194,502 (2019); Alosious, et al., Langmuir 37, 2355–2361 (2021)), and in this work we provide a pedagogical framework for the implementation of this toolkit on two systems: (i) a water-graphene system (for hydrodynamic slip) and (ii) a water-CNT system (for thermal slip). We provide detailed instructions for performing the EMD simulations using the LAMMPS package and processing the simulation outputs using Python modules to obtain the desired quantities of interest. We expect the toolkit to be useful for computational researchers studying interfacial friction and thermal transport, key factors for efficient and practical applications of nanofluidic systems.

We present BRAHMS, a cross-platform (Linux/Windows) graphical user interface, with GPU (CUDA) and CPU (OpenMP) backends, for the efficient and accurate simulation of three-wave mixing processes involving focused and pulsed Gaussian beams. The package solves the coupled 𝜒^(2) nonlinear Schrödinger equations, including diffraction, dispersion, walk-off, and phase-mismatch effects simultaneously, in full (3+1)D. To our knowledge, this is the first open-source package that solves the complete second-order (three-wave-mixing) nonlinear-optics problem in (3+1)D while fully exploiting the parallel capabilities of modern GPUs, with an equivalent CPU backend available for users without GPU hardware. The GPU implementation is inherently scalable thanks to its Thrust-based design. The package provides a valuable tool for experimental design and for studying three-dimensional field propagation in nonlinear three-wave interactions, supporting applications such as second-harmonic generation (SHG), sum-frequency generation (SFG), and optical parametric generation (OPG). Its GUI requires no programming experience, so that users can readily set up simulations and interpret their results.

Phase diagrams (PDs) illustrate the relative stability of competing phases under varying conditions, serving as critical tools for synthesizing complex materials. Reliable phase diagrams rely on precise free energy calculations, which are computationally intensive. We introduce exaPD, a user-friendly workflow that enables simultaneous sampling of multiple phases across a fine mesh of temperature and composition for free energy calculations. The package employs standard molecular dynamics (MD) and Monte Carlo (MC) sampling techniques, as implemented in the LAMMPS package. Various interatomic potentials are supported, including the neural network potentials with near ab initio accuracy. A global controller, built with Parsl, manages the MD/MC jobs to achieve massive parallelization with near ideal scalability. The resulting free energies of both liquid and solid phases, including solid solutions, are integrated into CALPHAD modeling using the PYCALPHAD package for constructing the phase diagram.