Computer Physics Communications

Trigonal solid-state defects are often subjects of spontaneous symmetry breaking driven by the E⊗e Jahn-Teller effect, reflecting strong electron-phonon coupling. These systems, particularly paramagnetic defect qubits in solids are central for quantum technology applications, where accurate knowledge of their fine-structure parameters – shaped by the complex interplay of spin-orbit and electron-phonon interactions – is essential. We introduce the jahn - teller - dynamics package, a Python code that implements the first-principles approach of [Phys. Rev. X 8, {021063} (2018)] to accurately compute the spin-orbit-phonon entanglement in trigonal defects utilizing the output from density functional theory calculations (DFT) to predict fine-structure parameters of zero-phonon lines (ZPLs), including Zeeman shifts under external magnetic fields. We demonstrate its capabilities on negatively charged Group-IV–vacancy (G4V) defects in diamond: SiV-, GeV-, SnV-, PbV- and the neutral N3V0 defect in diamond, and the CH3O0 methoxy radical. Additionally, we implement a generic electron-phonon code that is capable entangling an arbitrary amount of (i) vibration modes and (ii) electronic levels by (iii) arbitrarily high order of vibronic interaction terms. Exemplarily, we demonstrate the Jahn-Teller multimode problem on the CH3O0 methoxy radical and the Pseudo Jahn-Teller case on the C4H4+ butatrien cation.

We present KnudsenModel, an open-source Python implementation of the Knudsen model for computing evaporative cooling of microscopic liquid jets in vacuum. The code builds on widely used scientific Python packages and provides a transparent and flexible framework that can be applied to both cylindrical jets and spherical droplets. A fixed-mass radial discretization is employed to consistently account for the coupled evolution of mass loss and density, ensuring physically accurate predictions of both temperature and jet or droplet diameter. We analyze the numerical behavior of the method, including convergence properties and comparisons with literature calculations based on alternative discretization schemes, revealing that, in particular for water droplets, the present formulation captures a shallow minimum in the diameter that reflects the interplay between evaporation-induced mass loss and water's density anomaly—a feature absent in previous results.

We present a high-performance open-source GPU implementation for simulating large ensembles of autochemotactic active Brownian particles in two dimensions. The code couples continuous overdamped dynamics of self-propelled particle with a discrete chemoattractant field. This software enables simulations of autochemotactic channel formation, collective search strategies, multi-target foraging dynamics, and first-passage problems in both free exploration and goal-directed navigation scenarios, where effects of rotational and translational chemotactic responses, chemosecretion, resetting and target reinforcement are important. Our implementation addresses these challenges through GPU-accelerated algorithms: two-dimensional parallelization of chemoattractant deposition, multiplicative renormalization for efficient field decay, spatial hashing for particle interactions, and asynchronous multi-stream execution for ensemble statistics. We benchmark on NVIDIA A100 hardware and characterize algorithmic scaling with number of particles and chemoattractant deposition cutoff radius. We also compare GPU execution times with these on multi-core Intel Xeon and AMD EPYC CPU systems. We release the implementation as open-source software with examples and documentation, addressing community calls for unified computational tools in motile active matter.

A framework for handling diverse surface reaction mechanisms in catalytic systems is currently unavailable in OpenFOAM. This limitation stems from the inherent complexity of heterogeneous reactions occurring at the interface between solid catalyst surfaces and surrounding gases. In this work, we introduce a novel methodology that enables the flexible development and implementation of complex surface reaction models within OpenFOAM. Based on this methodology, a new surface reaction library is developed to extend OpenFOAM with computational fluid dynamics capabilities for simulating reactive flows coupled with detailed surface chemical microkinetics. The library supports multiple classes of surface reaction formulations, including Arrhenius-type rate expressions, sticking-coefficient models, and surface coverage-dependent mechanisms. The proposed framework is validated through simulations of catalytic hydrogen oxidation over a Rh/Al2O3 catalyst in stagnation-flow and annular reactor configurations. The simulation results show good agreement with experimental and numerical results, demonstrating the accuracy and reliability of the implementation. The applicability of the framework is further demonstrated by simulating catalytic ammonia decomposition in a realistic packed-bed reactor. The developed library is implemented as native OpenFOAM code and can be readily integrated into any OpenFOAM-based solver of the same version, enabling simulations of catalytic reacting flows with particle-resolved catalyst model.

The suggested package FUMILIM, based on famous FUMILI minimization package, has the following advantages. Unlimited number of parameters (multi-set tasks). Ability to work with multidimensional experimental points, described by a vector function. The preliminary scan is envisaged for complicated tasks. For heavy user's functions the parallel fit is envisaged with the help of OpenMP service. Multi-optional user interface, including option to ignore wrong experimental points. The package contains popular intrinsic user's functions. All of them can be used without the definition of the parameter initial values. There is a package of fast track reconstruction programs for working with detectors, including drift chambers. The capacity of these programs is about of tracks per second (at 2.8\thinspace GHz). In the corrected version the - version of the scan procedure has been replaced by a stable new one. A number of other improvements have also been made. All programmes are written in FORTRAN-90. The investigation has been performed at the Veksler and Baldin Laboratory of High Energy Physics, JINR.

The four-state method [{ Phys. Rev. B , 224,429 (2011)}] is a reliable approach for calculating parameters of spin Hamiltonian in magnetic materials. However, the conventional self-consistent implementations often suffer from convergence difficulty and high computational costs. Here, we propose a method to generate appropriate initial spin densities that significantly reduces the number of the required self-consistent iterations. Remarkably, we find that this initial distribution even enables the non-self-consistent calculations to yield reasonable results. To facilitate the application of this method, we have developed Spinss, an open-source code that creates the initial spin densities and other necessary files for both self-consistent and non-self-consistent density functional theory calculations. We provide a detailed description of the algorithm, input and output files, and application examples which demonstrate the effectiveness of Spinss for both bulk and low-dimensional systems.

Nexus-CAT (Cluster Analysis Toolkit) is an open-source Python package for cluster detection and percolation analysis of atomistic simulation trajectories. Standard structural tools, such as the pair distribution function or structure factor, fail to capture the long-range connectivity changes underlying amorphous-amorphous transitions in glassy materials. Nexus-CAT addresses this gap by reading extended XYZ trajectory files and identifying clusters via a Union-Find algorithm with path-compression. Four clustering strategies, i.e., distance-based, bonding, coordination-filtered, and shared-neighbor, are implemented through a Strategy Factory design pattern, enabling the treatment of diverse network topologies. The program computes key percolation properties with percolation detection based on a rigorous period vector algorithm. The package is validated against theoretical predictions and applied to glasses with different bonding environments, namely vitreous silica, vitreous ice, and amorphous silicon. One original result is the observation of a percolation transition prior to crystallization in the latter, indicating that pressure-induced crystallization is initially driven by an amorphous transformation with similar coordination number. The code is also designed to be readily extended to gels, cements, and other disordered materials. Nexus-CAT is fully available on GitHub and PyPI.

We present BO-PBK (BO-Product-Bi-Kappa), a new solver for kinetic dispersion relations of obliquely propagating waves in magnetized plasmas with complex velocity distributions. It reformulates the linearized Vlasov-Maxwell system into a compact eigenvalue problem, enabling direct computation of multiple wave branches and unstable modes without iterative initial-value searches. Key innovations include a unified framework supporting product-bi-kappa, kappa-Maxwellian, Maxwellian-kappa, bi-Maxwellian, and hybrid distributions with multi-component and loss-cone features; a concise rational-form eigenvalue formulation; and a 2-3 times reduction in matrix dimensions compared to the BO-KM solver, with improved efficiency at larger kappa indices. Benchmark tests confirm accurate reproduction of standard kinetic results and efficient resolution of waves and instabilities. BO-PBK thus provides a computationally efficient tool for wave and stability analysis in space and laboratory plasmas.

This paper presents the Parallel Coupler for Multimodel Simulations (PCMS), a new GPU accelerated generalized framework for coupling simulation codes on leadership class supercomputers. PCMS includes distributed control and field mapping methods for up to five dimensions. For field mapping, PCMS can utilize discretization and field information to accommodate physics constraints. PCMS is demonstrated with a coupling of the gyrokinetic microturbulence code XGC with a Monte Carlo neutral transport code DEGAS2 and with a 5D distribution function coupling of an energetic particle transport code (GNET) to a gyrokinetic microturbulence code (GTC). A scaling study is also presented to stress the PCMS APIs and underlying rendezvous-based communication protocol implemented with ADIOS2. It demonstrates the high performance of the ADIOS2 SST engine using remote direct memory access versus the filesystem-based BP4 engine on up to 260 nodes of Frontier; 16 to 2048 processes for the application being scaled and a fixed 16 processes each for the coupler and the second application.

This paper introduces an efficient tensor-vector product technique for the fast and accurate approximation of integral operators within physics-informed deep learning frameworks. Our approach leverages Kolmogorov-Arnold networks to evaluate problem dynamics at specific points, while employing Gaussian quadrature formulas to approximate the integral components, even in the presence of semi-infinite domains or singularities. We demonstrate the applicability of the proposed method to both Fredholm and Volterra integral operators, as well as to optimal control problems involving continuous time. Additionally, we outline how this approach can be extended to approximate fractional derivatives and integrals and propose a fast matrix-vector product algorithm for efficiently computing the fractional Caputo derivative. In the numerical section, we conduct comprehensive experiments on forward and inverse problems. For forward problems, we evaluate the performance of our method on over 50 diverse mathematical problems, including multi-dimensional integral equations, systems of integral equations, partial and fractional integro-differential equations, and various optimal control problems in delay, fractional, multi-dimensional, and nonlinear configurations. For inverse problems, we test our approach on several integral equations and fractional integro-differential problems. Finally, we introduce the pinnies Python package to facilitate the implementation and usability of the proposed method.