Computer Physics Communications

The study of the strength of materials is a cornerstone in material science and engineering, playing a critical role in shaping the progress and application of materials in diverse industrial sectors. The strength of a material is meticulously examined to understand the behavior of the material under different stress conditions and environments, thereby guiding material selection and structural design. Herein, we introduce the SMATool, a computational toolkit for the efficient calculation and analysis of material strength at both zero and finite temperatures for 3D, 2D, 1D, and tubular 2D-based nanostructures and nanotubes, as well as 1D nanoribbons. The toolkit is capable of calculating tensile, shear, ultimate, yield, and indentation (Vickers' hardness) strengths in various dimensions, as well as the energy storage capacity. We conducted several calculations both at zero and finite temperatures to validate the accuracy and reliability of the developed software. The results show that the SMATool package provides accurate predictions that align with existing data on material strength. SMATool integrates seamlessly with widely used electronic structure codes like VASP and Quantum Espresso, providing a user-friendly interface catering to academic researchers and industry professionals. SMATool is useful for either the exploration of the strength of materials or high-throughput new material design. SMATool is open-source and available on GitHub at SMATool@github and at Zenodo at 10.5281/zenodo.10780514.

The lattice Boltzmann method (LBM) has emerged as a prominent technique for solving fluid dynamics problems due to its algorithmic potential for computational scalability. We introduce XLB library, a Python-based differentiable LBM library based on the JAX platform. The architecture of XLB is predicated upon ensuring accessibility, extensibility, and computational performance, enabling scaling effectively across CPU, TPU, multi-GPU, and distributed multi-GPU or TPU systems. The library can be readily augmented with novel boundary conditions, collision models, or multi-physics simulation capabilities. XLB's differentiability and data structure is compatible with the extensive JAX-based machine learning ecosystem, enabling it to address physics-based machine learning, optimization, and inverse problems. XLB has been successfully scaled to handle simulations with billions of cells, achieving giga-scale lattice updates per second. XLB is released under the permissive Apache-2.0 license and is available on GitHub at https://github.com/Autodesk/XLB.

Accurate simulations of complex combustion phenomena associated with supersonic reacting flows require the use of detailed chemical kinetic mechanisms. However, detailed chemical mechanisms may consist of a large number of species and reactions resulting in extremely high computation cost. In order to accelerate the simulations of supersonic reacting flows with detailed chemical mechanisms, a heterogeneous CPU-GPU parallel algorithm and its portable software implementation are presented. The parallel algorithm was broken down into two parts: the CPU handled the computation of fluid dynamics while the GPU evaluated the chemical source terms and gas physical properties. The use of overlapping computations of chemical source terms on GPU and calculations of viscous flux on CPU is also presented. A study of performance tests was conducted. The performance results show that evaluating chemical source terms and gas physical properties on 2 GPUs are about 157.8× and 78.5× faster than running on the 16-core CPU when using the most complex mechanism on a grid of 3.3 million cells, respectively, resulting in an excellent speedup of the whole iteration up to 47. The significant performance improvement provided by the parallel algorithm can provide a significant perspective for designing heterogeneous CPU-GPU algorithms for applications in simulating supersonic reacting flows with detailed chemical kinetics.

In this manuscript we present the recently developed flexible framework for building both fluid and electron kinetic models of the tokamak Scrape-Off Layer in 1D - ReMKiT1D (Reactive Multi-fluid and Kinetic Transport in 1D). The framework can handle systems of non-linear ODEs, various 1D PDEs arising in fluid modelling, as well as PDEs arising from the treatment of the electron kinetic equation. As such, the framework allows for flexibility in fluid models of the Scrape-Off Layer while allowing the easy addition of kinetic electron effects. We focus on presenting both the high-level design decisions that allow for model flexibility, as well as the most important implementation aspects. A significant number of verification and performance tests are presented, as well as a step-by-step walkthrough of a simple example for setting up models using the Python interface.

We have developed a parallel implementation of an Elasto-Viscoplastic Fast Fourier Transform-based (EVPFFT) micromechanical solver to enable computationally efficient crystal plasticity modeling for polycrystalline materials. Our primary focus lies in achieving performance portability, allowing a single EVPFFT implementation to run optimally on various homogeneous architectures, including multi-core Central Processing Units (CPUs), as well as on heterogeneous computer architectures comprising multi-core CPUs and Graphics Processing Units (GPUs) from different vendors. To accomplish this goal, we have leveraged MATAR, a C++ software library that simplifies the creation and utilization of multidimensional dense or sparse matrix and array data structures. These data structures are designed to be portable across diverse architectures through the use of Kokkos, a performance-portable library. Additionally, we have employed the Message Passing Interface (MPI) to efficiently distribute the computational workload among processors. The heFFTe (Highly Efficient FFT for Exascale) library is used to facilitate the performance portability of the fast Fourier transforms (FFTs) computation. The computational performance of EVPFFT is evaluated and presented in terms of parallel scalability and simulation runtime on different high-performance computing (HPC) architectures. The utility of the developed framework to efficiently simulate the micro-mechanical fields in polycrystalline microstructures in engineering applications is discussed.

The rise of exascale supercomputers has fueled competition among GPU vendors, driving lattice QCD developers to write code that supports multiple APIs. Moreover, new developments in algorithms and physics research require frequent updates to existing software. These challenges have to be balanced against constantly changing personnel. At the same time, there is a wide range of applications for HISQ fermions in QCD studies. This situation encourages the development of software featuring a HISQ action that is flexible, high-performing, open source, easy to use, and easy to adapt. In this technical paper, we explain the design strategy, provide implementation details, list available algorithms and modules, and show key performance indicators for SIMULATeQCD, a simple multi-GPU lattice code for large-scale QCD calculations, mainly developed and used by the HotQCD collaboration. The code is publicly available on GitHub.

We present the Olsson.wl Mathematica package which aims to find transformations for some classes of multivariable hypergeometric functions. It is based on a well-known method developed by P. O. M. Olsson [1] (1964) to derive the analytic continuations of the Appell F_1 double hypergeometric series using the linear transformations of the Gauss 2F_1 hypergeometric function. We provide a brief description of the method of Olsson and demonstrate the use of the commands of the Olsson.wl package using some examples that are presented in the text and in some ancillary Mathematica notebooks. In particular, we reproduce various results of the literature on multivariable hypergeometric functions and show practical applications of this package in the derivation of novel formulas. In the context of high energy physics, we also demonstrate how it can be used to disentangle some known results about the analytic continuation of some series representations of the one-loop pentagon in multi-Regge kinematics and D = 6 - 2ϵ. We also provide a companion package, called ROC2.wl, which is dedicated to the derivation of the regions of convergence of double hypergeometric series. This package can be used independently of Olsson.wl.

Geant4 Detector Construction Pattern (G4DCP) is a template developed to flexibly construct complex detectors in Geant4 applications. The elements of G4DCP, including G4VUserDetectorConstruction, form an elegant template for detector setups. We construct a sample detector geometry utilizing this template and make the developed code available to the public.

The Python functions distributed with this article can be used for calculating the parameters of multichannel quantum defect theory models describing excited bound states of complex atoms. These parameters are obtained by fitting a model to experimental data provided by the user. The two main formulations of the theory are supported, namely the one in which the parameters of the model are a set of eigen channel quantum defects and a transformation matrix, and the one where these parameters are the elements of a reactance matrix. The distribution includes programs for calculating theoretical energy levels, calculating mixing coefficients and channel fractions and producing Lu-Fano plots.

This paper introduces PolyWeight, a Python software featuring a user-friendly graphical user interface (GUI), which offers two distinct approaches for MWD determination: an analytical relation-based method and a parametric model-based method. By utilizing dynamic moduli, users can calculate MWD as well as molecular weight averages such as M_n, M_w, and M_z. The functionality of PolyWeight is validated using synthetic data and real data obtained from the literature, exhibiting good agreement. Log files and datasets used in this work are available on GitHub.