Computer Physics Communications

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.

A Fortran package SWEXPHC is presented, designed to calculate nonrelativistic energies for the bound-state three-body problem with Coulomb interaction. The implementation is based on MPFUN2020 package written by D.H. Bailey, which allows calculations with arbitrary precision, where the number of working digits can be adjusted by the user. The approximate wave function is chosen in the form of a variational exponential expansion, which has proven itself over many years as an effective method for obtaining highly accurate solutions for various three-particle systems such as the helium atom and/or the molecular hydrogen ion.

CASL-HJX is a high-performance C++ framework for solving deterministic and stochastic Hamilton-Jacobi equations in two spatial dimensions. It integrates operator-splitting techniques with implicit treatment of parabolic terms, yielding substantial speedups over explicit methods commonly used for stochastic problems. The solver leverages monotone schemes to ensure convergence to viscosity solutions, for which we provide numerical evidence through systematic validation. The Hamilton-Jacobi-Bellman formulation enables global optimization beyond local methods. This performance advantage opens the door to applications that were previously intractable, including real-time control and rapid design iteration. We demonstrate the framework’s capabilities on benchmark PDEs as well as a neuroscience case study designing energy-efficient controllers for neural populations. The modular architecture allows users to define custom Hamiltonians and boundary conditions, making CASL-HJX broadly applicable to optimal control, front propagation, and uncertainty quantification across finance, engineering, and machine learning. Although currently limited to two spatial dimensions, CASL-HJX addresses critical gaps where gradient-based methods struggle in non-convex landscapes and local optimization yields suboptimal results. Complete source code, documentation, and examples are freely available.

This article introduces TinyDEM, a lightweight implementation of a full-fledged discrete element method (DEM) solver in 3D. Newton’s damped equations of motion are solved explicitly for translations and rotations of a polydisperse ensemble of dry, soft, granular spherical particles, using quaternions to represent their orientation in space without gimbal lock. Particle collisions are modeled as inelastic and frictional, including full exchange of torque. With a general particle-mesh collision routine, complex rigid geometries can be simulated. TinyDEM is designed to be a compact standalone program written in simple C++11, devoid of explicit pointer arithmetics and advanced concepts such as manual memory management or polymorphism. It is parallelized with OpenMP and published freely under the 3-clause BSD license. TinyDEM can serve as an entry point into classical DEM simulations or as a foundation for more complex models of particle dynamics.