Computer Physics Communications

Orbital magnetization, a key property arising from the orbital motion of electrons, plays a crucial role in determining the magnetic behavior of molecules and solids. Despite its straightforward calculation in finite systems, the computation in periodic systems poses challenges due to the ill-defined position operator and surface current contributions. The modern theory of orbital magnetization, formulated in the Wannier representation and implemented within the Density Functional Theory (DFT) framework, offers an accurate solution through the “converse approach.” In this paper, we introduce QE-CONVERSE, a refactored and modular implementation of the converse method, designed to replace the outdated routines from Quantum ESPRESSO (version 3.2). QE-CONVERSE integrates recent advancements in computational libraries, including scaLAPACK and ELPA, to enhance scalability and computational efficiency, particularly for large supercell calculations. While QE-CONVERSE incorporates these improvements for scalability, the main focus of this work is provide the community with a performing and accurate first principles orbital magnetization package to compute properties such as Electron Paramagnetic Resonance (EPR) g-tensors and Nuclear Magnetic Resonance (NMR) chemical shifts, specially in systems where perturbative methods fail. We demonstrate the effectiveness of QE-CONVERSE through several benchmark cases, including the NMR chemical shift of 27Al in alumina and 17O and 29Si in α-quartz, as well as the EPR g-tensor of nΣ (n ≥ 2) radicals and substitutional nitrogen defects in silicon. In all cases, the results show excellent agreement with theoretical and experimental data, with significant improvements in accuracy for EPR calculations over the linear response approach. The QE-CONVERSE package, fully compatible with the latest Quantum ESPRESSO versions, opens new possibilities for studying complex materials with enhanced precision.

The scrape-off layer of a tokamak fusion reactor carries the plasma exhaust from the hot core plasma to the material surfaces of the reactor vessel. The heat loads imposed by the exhaust are a critical limit on the performance of fusion power plants. Turbulent transport of the plasma regulates the width of the scrape-off layer plasma and must be modelled to understand the intensity of these heat loads. STORM is a plasma turbulence code capable of simulating three dimensional turbulence across the full scrape-off layer of a tokamak fusion reactor, using a drift reduced, collisional fluid model. STORM uses mostly finite difference schemes, with a staggered grid in the direction parallel to the magnetic field. We describe the model, geometry and initialisation options used by STORM, as well as the numerical methods, which are implemented using the BOUT++ plasma simulation framework. BOUT++ has been enhanced alongside the development of STORM, providing better support for staggered grid methods. We summarise these enhancements, including a detailed explanation of the parallel derivative methods, which underwent a major update for version 4 of BOUT++.

Lethe is an open-source Computational Fluid Dynamics (CFD) software framework with extensive multiphase and multiphysics capabilities. By leveraging the deal.II open-source framework, Lethe finite element solvers scale well on modern high-performance computers while possessing advanced features such as dynamic mesh adaptation, load-balancing, isoparametric high-order capabilities, and a fully-fledged Discrete Element Method (DEM) module. To facilitate contributions from the community, Lethe is extensively tested with continuous integration using over 450 unit and functional tests. Furthermore, Lethe contains 74 fully documented examples with pre-processing and post-processing steps to allow users to learn how to rapidly use and modify the framework. In this article, we give an overview of the simulation models available within Lethe and illustrate these capabilities with a selected list of examples including turbulent and multiphase flows.

In this study, we introduce Fire, an open-source adaptive mesh refinement (AMR) solver for supersonic reacting flows, and conduct theoretical analyses on the efficiency of AMR methods. Fire is developed within the AMR framework of ECOGEN (Schmidmayer et al., 2020). To accurately model compressible multi-component reacting flows, the Fire solver employs the thermally perfect gas model for multi-species gaseous mixtures, mixture-averaged transport models for viscous fluxes, and detailed finite-rate chemistry for combustion processes. The solver utilizes the Harten-Lax-van Leer Contact approximate Riemann solver with low-Mach number correction to evaluate inviscid fluxes, demonstrating its superiority over the traditional Harten-Lax-van Leer Contact solver on detonation simulations. Moreover, we deduce the theoretical speedup ratio (denoted as η_the) of AMR methods over uniform-grid methods by analyzing the advancing procedures. This theoretical analysis is well-supported by the numerical speedup ratio (denoted as η_num) given by numerical tests. To further enhance computational efficiency, we propose a three-stage AMR strategy specifically tailored to the characteristics of inert flows, flame fronts, and shock-flame interactions. Comprehensive validation tests, encompassing unsteady convection and diffusion, planar deflagration, inert and reacting shock-bubble interactions, planar detonations, and detonation cellular structures, confirm the accuracy and efficiency of Fire in simulating supersonic combustions. We anticipate that this work will not only serve as a valuable numerical tool for supersonic reacting flows research but also contribute to a deeper understanding and improvement of AMR methodologies.

The symmetry-constrained response tensors on transport, optical, and electromagnetic effects are of central importance in condensed matter physics because they can guide experimental detections and verify theoretical calculations. These tensors encompass various forms, including polar, axial, i-type (time-reversal even), and c-type (time-reversal odd) matrixes. The commonly used magnetic groups, however, fail to describe the phenomena without the spin-orbit coupling (SOC) effect and cannot build the analytical relationship between magnetic orders with response tensors in magnetic materials. Developing approaches on these two aspects is quite demanding for theory and experiment. In this paper, we use the magnetic group, spin group, and extrinsic parameter method comprehensively to investigate the symmetry-constrained response tensors, then implement the above method in a platform called "TensorSymmetry". With the package, we can get the response tensors disentangling the effect free of SOC and establish the analytical relationship with magnetic order, which provides useful guidance for theoretical and experimental investigation for magnetic materials.

The concept of representative volume (REV, or RVE in material sciences) is the cornerstone of continuum scale models – one needs to get the volume big enough so that it could be represented with an averaged value (scalar, vector or tensorial, etc.). While REV should be established in all larger scale simulations, this is rarely done in practice despite widespread adoption of 3D imaging devices in all research areas starting from petroleum engineering, material sciences and spanning to biology. Sometimes REV analysis is performed in the form of very simple procedures, such as a check for convergence of porosity or surface area, which is technically identical to omitting it entirely. The main reason for this to happen is the poor understanding of REV concept in general (mainly its connection to spatial stationarity and necessity for vector metrics with high information content) and unavailability of open-source robust solutions. In this work we present PARSE library that solves exactly this problem – we developed an easy to use and well-documented code based on rigorous research carried out recently in explaining the “dark sides” of representativity. In addition to REV, our code allows spatial stationarity analysis and comparison of samples (with subsequent clusterization into different groups) based on vector metrics – correlation functions, persistence diagrams and pore-network statistics, that altogether possess high information content which is critical in establishing stationarity and REV. We test our library on images produced by known statistical processes, such as Poisson spheres. After verification, we show how to compare different samples and group them depending on their “structural DNA”. All solutions explained in the paper are represented by Jupiter notebooks that can be used to perform similar analysis, moreover, the class structure of PARSE library allows painless modifications to be implemented. We believe that such an open-source library will be useful in numerous fields and will become an invaluable tool for 3D image analysis.

Integrated computational materials engineering (ICME) has become a cornerstone for modern intelligent approaches, accelerating the discovery and design of new materials by providing extensive datasets. To support this, we have developed a straightforward and efficient command-line program named AAVDP (Atomistic Analyzer of Virtual Diffraction Patterns) for high-throughput (HT) virtual diffraction, structural analysis, and in situ visualization of various atomic configurations. AAVDP has integrated a comprehensive suite of virtual diffraction methods, spanning from X-ray diffraction (XRD), neutron diffraction (NED), kinematic electron diffraction (KED), and dynamical electron diffraction (DED), to both kinematic and dynamical Kikuchi diffractions (KKD and DKD), making it a versatile tool for researching crystalline and defective structures at atomic scale. Furthermore, AAVDP provides statistical tools, including the radial distribution function (RDF) and the static structure factor (SSF), which are crucial for understanding amorphous and liquid systems. As a command-line program, AAVDP allows for the customization of complex workflows and the extraction of high-volume statistical results with minimal scripting efforts. The program’s functionality and efficiency have been rigorously validated through a series of critical evaluations and tests, which empower users to delve deeper into the intricate diffraction behaviors and diverse material structures.

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.