Computer Physics Communications

This paper presents GeoDualSPHysics, an open-source, graphics processing unit (GPU)-accelerated smoothed particle hydrodynamics (SPH) solver designed for simulating large-deformation geomaterial and their interactions with multi-body systems. Built upon the popular open-source SPH solver DualSPHysics, the solver leverages its highly parallelised SPH scheme empowered by the CUDA parallelisation while extending its capabilities to large-deformation geomechanics problems with particles up to the order of 10⁸ on a single GPU. The SPH geomechanics model is enhanced by a noise-free stress treatment technique that stabilizes and accurately resolves stress fields, as well as an extended modified Dynamic Boundary Condition (mDBC) ensuring first-order consistency in solid boundary modelling. Additionally, the coupling interface between DualSPHysics and the multi-body dynamics solver Project Chrono is adapted for simulating interactions between geomaterials and multiple interacting rigid bodies. Benchmark validations confirm the solver’s accuracy in resolving geotechnical failures, impact forces on solid boundaries, and geomaterial-multibody system interactions. GPU profiling of the newly implemented CUDA kernels demonstrates their performance metrics are similar to those of the original DualSPHysics solver. Performance evaluations demonstrate its saving in memory usage of 30-50% and improvements in computational efficiency over existing SPH geomechanics solvers, achieving practical simulation speeds for systems with tens of millions of particles and showing a speedup of up to 180x compared to the optimised multi-core CPU implementation. These advances position GeoDualSPHysics as a versatile, efficient tool for high-fidelity simulations of complex geotechnical systems.

The diverse spectrum of material characteristics, including band gap, mechanical moduli, color, phonon and electronic density of states, along with catalytic and surface properties, are intricately intertwined with the atomic structure and the corresponding interatomic bond lengths. This interconnection extends to the manifestation of interplanar spacings within a crystalline lattice. Analysis of these interplanar spacings and the comprehension of any deviations-whether it be lattice compression or expansion, commonly referred to as strain, hold paramount significance in unraveling various unknowns within the field. Transmission Electron Microscopy (TEM) is widely used to capture these atomic-scale ordering, facilitating direct investigation of interplanar spacings. However, creating critical contour maps for visualizing and interpreting lattice stresses in TEM images remains a challenging task. This study introduces an open-source, AI-assisted application, developed entirely in Python, for processing TEM images to facilitate strain analysis through advanced visualization techniques. This application is designed to process a diverse range of materials, including nanoparticles, 2D materials, pure crystals, and solid solutions. By converting local variations in interplanar spacings into contour maps, it provides a visual representation of lattice expansion and compression. With highly versatile settings, as detailed in this paper, the tool is readily accessible for TEM image-based material analysis. It facilitates an in-depth exploration of strain engineering by generating strain contour maps at the atomic scale, offering valuable insights into material properties.

Version 14 of XtalOpt, an evolutionary multi-objective global optimization algorithm for crystal structure prediction, is now available for download from its official website https://xtalopt.github.io, and the Computer Physics Communications Library. The new version of the code is designed to perform a ground state search for crystal structures with variable compositions by integrating a suite of ab initio methods alongside classical and machine-learning potentials for structural relaxation. The multi-objective search framework has been enhanced through the introduction of Pareto optimization, enabling efficient discovery of functional materials. Herein, we describe the newly implemented methodologies, provide detailed instructions for their use, and present an overview of additional improvements included in the latest version of the code.

FukuiGrid is a Python-based code that calculates Fukui functions and Fukui potentials in systems with periodic boundary conditions, making it a valuable tool for solid-state chemistry. It focuses on chemical reactivity descriptors from Conceptual Density-Functional Theory (c-DFT) and enables the calculation of Fukui functions through methods such as finite differences and interpolation. FukuiGrid addresses the challenges associated with periodic boundary conditions when calculating the electrostatic potential of a Fukui function (known as the Fukui potential) by integrating various corrections to alleviate the compensating background of charge. These corrections include the electrode approach and self-consistent potential correction as post-processing techniques. This package is compatible with VASP outputs and specifically designed to study the reactivity of surfaces and adsorbates. It generates surface reactivity maps and provides insights into adsorption site preferences, as well as regions prone to electron donation or withdrawal. FukuiGrid has been designed to make c-DFT easier for the surface chemistry community.

SpectraMatcher is a cross-platform desktop application for interactive comparison of experimental and computed vibronic spectra, designed to assist in the recognition and assignment of spectral patterns. It provides an intuitive graphical interface — with no coding or scripting required — for importing experimental spectra, visualizing them alongside the corresponding theoretical spectra constructed from Gaussian frequency calculations, and adjusting key parameters such as peak width, intensity scaling factors, and vibration-type-specific anharmonic corrections. SpectraMatcher features an automated peak-matching algorithm that assigns experimental and computed peaks based on their intensity ratio and proximity. Assignments and spectra can be exported in multiple formats for publication or for further analysis. The software remains responsive even for large datasets, and supports efficient and reproducible interpretation of vibronic spectra.

We present an open-source simulation framework for optically detected magnetic resonance, developed in Python. The framework is designed to simulate multipartite quantum systems composed of spins and electronic levels, enabling the study of systems such as nitrogen-vacancy centers in diamond and photo-generated spin-correlated radical pairs. Our library provides system-specific sub-modules for these and related problems. It supports efficient time-evolution in Lindblad form, along with tools for simulating spatial and generalized stochastic dynamics. Symbolic operator construction and propagation are also supported for simple model systems, making the framework well-suited for classroom instruction in magnetic resonance. Designed to be backend-agnostic, the library interfaces with existing Python packages as computational backends. We introduce the core functionality and illustrate the syntax through a series of representative examples.

The Singularity Expansion Method Parameter Optimizer - SEMPO - is a toolbox to extract the complex poles, zeros and residues of an arbitrary response function acquired along the real frequency axis. SEMPO allows to determine this full set of complex parameters of linear physical systems from their spectral responses only, without prior information about the system. The method leverages on the Singularity Expansion Method of the physical signal. This analytical expansion of the meromorphic function in the complex frequency plane motivates the use of an accuracy-driven improved version of the Cauchy method constrained by properties of physical systems, as well as an auto-differentiation-based optimization approach. Both approaches can be sequentially associated to provide highly accurate reconstructions of physical signals in large spectral windows. The performances of SEMPO are assessed and analysed in several configurations that include the dielectric permittivity of materials and the optical response spectra of various optical metasurfaces. SEMPO’s performances are thoroughly analyzed and benchmarked with other state-of-the-art methods to highlight its capability to retrieve the natural poles of a physical system.

We introduce mosca, a Mathematica package designed to facilitate on-shell calculations in effective field theories (EFTs). This initial release focuses on the reduction of Green’s bases to physical bases, as well as transformations between arbitrary operator bases. The core of the package is based on a diagrammatic on-shell matching procedure, grounded in the equivalence of physical observables derived from both redundant and non-redundant Lagrangians. mosca offers a complete set of tools for performing basis transformations, diagram isomorphism detection, numerical substitution of kinematic configurations, and symbolic manipulation of algebraic expressions. Planned future developments include extension to one-loop computations, thus providing support for EFT renormalization directly in a physical basis and automated computation of one-loop finite matching, including contributions from evanescent operators.

Fractures in rock masses are a central focus in research areas such as unconventional energy extraction, nuclear waste disposal, and carbon sequestration. Laboratory investigations of fracture parameters are essential for optimizing field operations. In recent years, CT scanning has emerged as a widely adopted non-destructive inspection technique. However, existing methods for post-processing CT scan data face persistent challenges in achieving high accuracy and efficiency. To address these challenges, we propose a novel Python-based post-processing framework that integrates a slice-by-slice thinning algorithm, local thickness computation, and point cloud data processing techniques. This framework enables precise characterization of fractured digital rocks by quantifying fracture width distribution and fracture surface orientation, alongside standard structural evaluation metrics such as the fractal dimension, volume ratio, and the H-index. Its feasibility, accuracy, and flexibility are validated through analyses of diverse fracturing samples, including fluid-fractured samples, shear-induced fracture samples, and samples containing multiple secondary fractures.

We present the library ggxy, written in C++, which can be used to compute partonic and hadronic cross sections for gluon-induced processes with at least one closed heavy quark loop. It is based on analytic ingredients which avoids, to a large extent, expensive numerical integration. This results in significantly shorter run-times than other similar tools. Modifying input parameters, changing the renormalization scheme and varying renormalization and factorization scales is straightforward. In Version 1 of ggxy we implement all routines which are needed to compute partonic and hadronic cross sections for Higgs boson pair production up to next-to-leading order in QCD. We provide flexible interfaces and allow the user to interact with the built-in amplitudes at various levels.