Computer Physics Communications

The version 2.0 of the Boundary Element Software for 3D Linear Elasticity (BESLE) is presented. BESLE is an open-source Fortran 90 code for the simulation of isotropic and anisotropic solids under quasi-static, dynamic, and high-rate boundary conditions using elastostatic and elastodynamic boundary element formulations. Compared to the initial release, this new version introduces a substantially simplified installation procedure. BESLE v1.0 required users to manually download, configure, and integrate external libraries such as MUMPS, SCOTCH, and ScaLAPACK, which often represented a barrier for new users. In contrast, version 2.0 provides an online installer, which automatically downloads, prepares, and installs the required libraries from public repositories. This new approach makes the deployment of BESLE straightforward, reducing installation time and minimising potential user errors. No changes have been made to the core numerical methods, input structure, or supported physics. BESLE v2.0 therefore retains full compatibility with existing simulations and examples, while significantly improving ease of installation, accessibility, and reproducibility.

We introduce a new open-source Python x-ray tracing code for modelling Bragg diffracting mosaic crystal spectrometers: High Energy Applications Ray Tracer (HEART). HEART's high modularity enables customizable workflows as well as efficient development of novel features. Utilizing Numba's just-in-time (JIT) compiler and the message-passing interface (MPI) allows running HEART in parallel leading to excellent performance. HEART is intended to be used for modelling x-ray spectra as they would be seen in experiments that measure x-ray spectroscopy with a mosaic crystal spectrometer. This enables the user to make predictions about what will be seen on a detector in experiment, perform optimizations on the design of the spectrometer setup, or to study the effect of the spectrometer on measured spectra. However, the code certainly has further uses beyond these example use cases. Here, we discuss the physical model used in the code, and explore a number of different mosaic distribution functions, intrinsic rocking curves, and sampling approaches which are available to the user. Finally, we demonstrate its strong predictive capability in comparison to spectroscopic data collected at the European XFEL in Germany.

Ants3 is a toolkit that serves as a front-end for particle simulations in Geant4 and offers a custom simulator for optical photons. It features a fully interactive Graphical User Interface and an extensive scripting system based on general-purpose scripting languages (Python and JavaScript). Ants3 covers the entire detector simulation/optimization cycle, providing an intuitive approach for configuration of the geometry and simulation conditions, the possibility to automatically distribute workload over local and network resources, and giving a suite of versatile tools based on CERN ROOT for the analysis of the results. The intended application area is the development of new detectors and readout methods. The toolkit has been designed to be user-friendly for those with little experience in simulations and programming.

In this work we present a covariant implementation of force for Chaos Many-Body Engine (CMBE - Grossu et al., 2021) .Net application. Thus, supposing the expression of bi-particle force is known in the center-of-mass frame, we applied specific relativistic transformations for obtaining the corresponding value in laboratory frame. As an example of use, we discuss a toy-model for relativistic nuclear collisions at Facility for Antiproton and Ion Research (FAIR) energies.

DiracBilinears.jl is a Julia package for computing Dirac bilinears, which are fundamental physical quantities of electrons in relativistic quantum theory, using first-principles calculations for solids. In relativistic quantum theory, 16 independent bilinears can be defined using the four-component Dirac field. To focus on the low-energy physics typically considered in condensed matter physics, we consider the bilinears represented by the non-relativistic two-component Schrödinger field, obtained from the 1/m expansion to leading order. This package can evaluate the spatial distributions and Wannier matrix elements of the Dirac bilinears in solids quantitatively by connecting to the external first-principles calculation packages, including Quantum ESPRESSO, Wannier90, and wan2respack.

We present a general-purpose, open-source software suite, SQUIRREL (Streamlined Quantum Unified Interface for Researching Real-time Excitations with Light), for propagating the time-dependent Schrödinger equation on complex geometries in the presence of time-dependent electric and/or magnetic fields. To handle large systems that can be executed on a conventional desktop computer, the SQUIRREL software suite uses a suite of efficient propagation methods for various quantum dynamics applications, including a new perturbation-based element-dropping algorithm that improves computational performance with minimal loss of accuracy. We analyze the efficacy of these optimizations for Crank-Nicolson, scaled Taylor series approximation, and split-operator propagation methods and discuss the range of their applicability to a variety of quantum dynamics problems. In addition, we provide several examples of time-dependent dynamics calculations and extensive documentation for generating custom geometries, potentials, and time-propagation approaches. Our numerical benchmarks and results demonstrate the versatility of the SQUIRREL software suite for efficiently calculating quantum dynamics in complex nanoscale geometries, particularly in the presence of time-dependent magnetic fields, which have received less attention in previous quantum dynamics studies.

Accurate quantification of local packing density and mixing in simulations of particulate systems is essential for many industrial applications. Traditional methods which simply count the number of particle centres within a given volume of space (cell) introduce discontinuities at cell boundaries, leading to unreliable measurements of packing density. We introduce Packing3D.jl, an open-source Julia package providing analytic partial-volume calculations for spheres intersecting Cartesian and cylindrical meshes. We derive closed-form solutions for single, double and triple spherical-cap intersections, plus sphere-cylinder overlaps. We implement efficient mesh-generation routines, principal-cell indexing, and data-splitting functions for time-series analyses. Performance and accuracy were validated against simple cubic and face-centred cubic lattices and via boundary-shift continuity tests. Packing3D.jl converges exactly to theoretical lattice densities, eliminates discontinuities at sub-particle resolution, and scales linearly with particle count. Memory usage remains modest (40 B per particle, 48 B per cell). Packing3D.jl provides researchers with continuous, reproducible volume-fraction fields and robust mixing indices at high performance, facilitating sensitivity analyses and optimisation in granular process engineering.

Various methods are commonly applied for data acquisition in the melting process of substances under high pressure. However, throughout the application of these methods, challenges persist, including significant time and computational requirements, as well as issues related to hysteresis effects. We introduce the GTS package, a Python toolkit based on the work of J. W. Gibbs to obtain melting data at high pressures in a geometrical manner. We outline the theory behind constructing the Gibbs thermodynamic surface, which includes regions representing the solid and liquid phases. Several examples are presented to demonstrate program execution and validate accuracy by comparing results with prior studies. GTS is openly accessible on GitHub: https://github.com/computation-mineral-physics-group/GTS.

Nuclear fission reactors are abundant sources of antineutrinos for neutrino physics experiments. The flux and spectrum of antineutrinos emitted by a reactor can indicate its activity and composition, suggesting potential applications of neutrino measurements beyond fundamental scientific studies that may be valuable to society. The utility of reactor antineutrinos for applications and fundamental science is dependent on the availability of precise predictions of these emissions. For example, in the last decade, disagreements between reactor antineutrino measurements and models have inspired revision of reactor antineutrino calculations and standard nuclear databases as well as searches for new fundamental particles not predicted by the Standard Model of particle physics. Past predictions and descriptions of the methods used to generate them are documented to varying degrees in the literature, with different modeling teams incorporating a range of methods, input data, and assumptions. The resulting difficulty in accessing or reproducing past models and reconciling results from differing approaches complicates the future study and application of reactor antineutrinos. The CONFLUX (Calculation Of Neutrino FLUX) software framework is a neutrino prediction tool built with the goal of simplifying, standardizing, and democratizing the process of reactor antineutrino flux calculations. CONFLUX includes three primary methods for calculating the antineutrino emissions of nuclear reactors or individual beta decays that incorporate common nuclear data and beta decay theory. The software is prepackaged with the current nuclear databases, including ENDF.B/VIII, JEFF-3.3, and ENSDF, and it includes the capability to predict time-dependent reactor emissions, adjust nuclear database or beta decay inputs/assumptions, and propagate related sources of uncertainty. This paper describes the CONFLUX software structure, details the methods used for flux and spectrum calculations, and provides examples of potential use cases.

As a machine-learned potential, the neuroevolution potential (NEP) method features exceptional computational efficiency and has been successfully applied in materials science. Constructing high-quality training datasets is crucial for developing accurate NEP models. However, the preparation and screening of NEP training datasets remain a bottleneck for broader applications due to their time-consuming, labor-intensive, and resource-intensive nature. In this work, we have developed NepTrain and NepTrainKit, which are dedicated to initializing and managing training datasets to generate high-quality training sets while automating NEP model training. NepTrain is an open-source Python package that features a bond length filtering method to effectively identify and remove non-physical structures from molecular dynamics trajectories, thereby ensuring high-quality training datasets. NepTrainKit is a graphical user interface (GUI) software designed specifically for NEP training datasets, providing functionalities for data editing, visualization, and interactive exploration. It integrates key features such as outlier identification, farthest-point sampling, non-physical structure detection, and configuration type selection. The combination of these tools enables users to process datasets more efficiently and conveniently. Using CSPbI3 as a case study, we demonstrate the complete workflow for training NEP models with NepTrain and further validate the models through materials property predictions. We believe this toolkit will greatly benefit researchers working with machine learning interatomic potentials.