Computer Physics Communications

This paper presents a new software implementation in the form of a PY_GROWTH package, specifically designed for the analysis of models of growth of thin epitaxial films and the corresponding RHEED intensities according to the kinematical approximation. This implementation translates and modernizes legacy C++ simulation algorithms into a highly optimized, testable Python package. PY_GROWTH provides three separate universal engines for solving initial value problem for nonlinear differential equations, any of which can be used depending on the scale of computations.

The discrete dipole approximation (DDA) is a widely used and versatile numerical method for solving electromagnetic scattering by arbitrarily shaped objects. Despite its popularity, quantitative comparisons between independent implementations remain challenging due to differences in linear-system conventions, solver settings, and default numerical parameters. In this work, we introduce a unified software-assisted methodology for cross-verification and benchmarking of three major open-source DDA solvers: DDSCAT, ADDA, and IFDDA. We demonstrate how machine-precision agreement can be achieved across implementations by aligning all free parameters and provide practical equivalence tables enabling reproducible and interoperable simulations. Using this methodology, we perform systematic CPU and GPU performance comparisons covering OpenMP, MPI, and CUDA/OpenCL parallelization. Beyond benchmarking, our approach serves as a practical guide for configuring consistent DDA simulations and for understanding how precision, solver choice, and hardware architecture affect runtime, scalability, and accuracy in computational light-scattering studies. The software package also supports regression testing and bitwise reproducibility verification for future code releases.

FIRE7 is a major update to the FIRE program for integration-by-parts (IBP) reduction of Feynman integrals. A large part of the improvements is related to the automatic reduction and reconstruction with the modular arithmetic approach. The performance of the classical rational polynomial approach is also significantly increased, using an improved presolve algorithm that performs Gaussian elimination to simplify IBP identities before substituting numerical indices as in the Laporta algorithm. Various new command line tools are included to facilitate tasks such as applying an IBP reduction table to reduce a loop integrand as a linear combination of individual integrals.

This manuscript introduces MatSub, an open-access software package designed to facilitate the application of Subgroup Discovery (SGD) algorithms in machine learning and data-driven scientific discovery. A key contribution of MatSub lies in the development of novel quality functions tailored to materials informatics. While existing SGD algorithms with numerical targets often emphasize statistical exceptionality, materials research typically prioritizes the identification of subgroups with extreme or optimal property values. To address this gap, MatSub incorporates quality functions that (1) guide the discovery of subgroups maximizing or minimizing a target property, (2) enforce performance-based boundary constraints to filter out undesired materials, (3) promote orthogonal subgroup discovery to reveal multiple, physically distinct mechanisms affecting material behavior, and (4) enable multitask subgroup discovery to capture subgroups that simultaneously satisfy multiple property requirements. We demonstrate the utility of these quality functions in a case study on segregation energies of single-atom alloy catalysts (SAACs), where MatSub successfully identifies diverse and interpretable subgroups linked to distinct electronic and bonding characteristics. These results highlight the software’s ability to support mechanism-aware analysis and accelerate hypothesis generation in materials science and beyond.

The open-source release of the HOS-NWT code is reported. This software is dedicated to the modelling of wave tank facilities at use in ocean engineering, leading to a so-called Numerical Wave Tank (NWT). The present numerical model is based on a pseudo-spectral approach, known as the High-Order Spectral (HOS) method, which guarantees both high accuracy and computational efficiency. The HOS-NWT code enables the reproduction of all the features of an experimental wave tank, including the presence of a wave maker to generate waves, an absorbing beach to dissipate the waves near the opposite wall, and perfectly reflective side walls. This release also includes recent developments that allow for the modelling of wave-breaking and the parallelization of the code through MPI. HOS-NWT v2.1 has been released as open-source software and is available on the GitLab platform It is developed and distributed under the terms of GNU General Public License (GPLv3). Along with the source code, a detailed HTML documentation is also available.

Over the last decades, X-ray Thomson scattering has become a valuable experimental technique to study dense plasma and warm dense matter states. Designing experiments and interpreting data typically requires the simulation of spectra for given sets of plasma parameters. Many models applicable to different conditions have been formulated by the community. However, an open, broadly available code combining these efforts has been lacking. In this work, we introduce the open-source simulation package jaxrts, a Python implementation incorporating several models, designed to be modular, extensible, transparent, and user-friendly. The software unites Python's flexibility with computational efficiency through jax's just-in-time compilation and GPU acceleration.

The present DDHADTENS program is provided to extract variables characterizing anisotropic damage of elastic tensors. The theory is based on harmonic analysis. The program takes as input: (a) an undamaged elastic tensor and (b) a series of damaged elastic tensors. The output is a series of damage variables, defined as the coefficients of the spherical harmonics describing scalar damage orientation functions, which can be interpreted as the damage of elastic coefficients varying with the direction. These tensors can be obtained externally by numerical fracture simulations in a Representative Volume Element. A 2D version of the code is first provided. In this case, the elastic tensor is given by a 3 x 3 matrix and 6 damage variables are obtained. Second, a 3D version of the code is proposed, where the elastic tensor is given by a 6 x 6 matrix and 21 damage variables are obtained. In both case, a set of reduced variables using PCA is also provided. Numerical examples associated with degradation of elastic properties of Representative Volume Elements (RVE) are described.

In this work, we present RePlaChem, a software library for reducing detailed large-scale plasma chemical mechanisms to smaller skeletal ones. The library parses a plasma chemical mechanism in the well-established format compatible with the software ZDPlasKin, runs the reduction algorithm, and generates automatically a skeletal chemical mechanism. This feature allows the seamless implementation of the skeletal chemistry in well-known solvers including ZDPlasKin. In turn, the reduction of the chemistry accelerates otherwise computationally expensive numerical simulations. Furthermore, RePlaChem can be used as an analysis tool to shed light into the underlying reaction physics by identifying the dominant species and associated reactions. In order to validate and demonstrate its capabilities, RePlaChem is used to substantially reduce a large-scale methane-hydrogen plasma chemical mechanism with 77 species and 4404 reactions at various orders with a relatively small loss of accuracy.

Accurate evaluation of singular and nearly singular integrals is crucial for the successful application of numerical techniques such as the Boundary Element Method (BEM) and the Generalized/eXtended Finite Element Method (G/XFEM). Employing a unified framework based on Duffy-type mappings and an asymptotic analysis, we identify distinct radial and angular near-singular behaviors. Several established variable transformation techniques are analyzed and compared for evaluating weakly singular and nearly singular integrals over spatial surface elements. Comprehensive numerical experiments are conducted to assess the accuracy and robustness of various radial (Polynomial, Sinh, Exponential, L_1^{-1/5} , and Xiao’s [1]) and angular (Xiao’s [1], Sigmoidal and PART’s) transformations for weakly singular integrals and for weakly/strongly nearly singular integrals over planar and representative curved surfaces. The results demonstrate that Sinh and Xiao’s radial transformations offer superior performance for (nearly) singular integrals across diverse scenarios. In contrast, Polynomial and L_1^{-1/5} transformations exhibit limitations, particularly for stronger near-singularities or challenging geometries. Furthermore, Xiao’s angular transformation is shown to effectively mitigate angular near-singularities arising from edge/corner proximity within the employed coordinate system. These comparative insights provide practical guidance for selecting optimal integration strategies. Based on the analysis, we present a computational algorithm and its implementation in the MATLAB/Octave package, offering a versatile framework for accurately evaluating these challenging integrals in computational mechanics.

The analysis of current-voltage (J-V) characteristics is essential for understanding charge transport, injection barriers, and performance metrics in organic photovoltaic (OPV) devices. However, most available approaches either require advanced programming skills or focus on a limited subset of models. This work introduces a free, cross-platform Python-based tool that integrates multiple theoretical frameworks for J-V curve analysis through a user-friendly graphical interface. The software implements modules for illuminated curves, enabling the extraction of $J_{sc}$, $V_{oc}$, fill factor (FF), power conversion efficiency (PCE), and resistive losses, as well as modules dedicated to dark curves, including the Mott-Gurney law, the circuital model, and the Richardson-Schottky formalism, which allow estimation of carrier mobility, effective mobility, saturation current density, and injection barrier height. Graph customization options are included to generate publication-ready figures directly within the program. The tool was validated against experimental data, providing reliable results consistent with theoretical expectations. Future releases will expand its scope by adding a multiplot functionality for the simultaneous comparison of multiple datasets and a lifetime analysis module to monitor the temporal evolution of PCE, $V_{oc}$, $J_{sc}$, FF, and carrier mobility. By combining rigor, accessibility, and extensibility, the proposed tool contributes to the systematic characterization and optimization of next-generation organic solar cells.