Computer Physics Communications

Recent years have witnessed a surge of research on the structure, property and performance engineering of two-dimensional (2D) materials by ion irradiation. Compared to the 3D counterparts, 2D systems exhibit drastically different and even counter-intuitive irradiation response, and an atomic insight into the ion bombardment and defect formation is essential. In this work, we develop a theoretical framework I2DM for simulating ion irradiation on two-dimensional (2D) materials using Monte Carlo (MC) algorithm. I2DM can generate incident ions with adjustable ion species, incident energy, ion fluence and incident angle. Based on binary collision approximation (BCA), the primary collisions, cascade collisions and defect recombination during irradiation process are explicitly described. As output, details on the defect type/yield and morphology of irradiated material are provided. We have performed systematic simulations on three typical 2D structures, including graphene, h-BN, and MoS_2 under different ion irradiation conditions, and reveal that the obtained results are in excellent agreement with the available experimental measurements and molecular dynamics data. The developed framework is generally applicable and computationally efficient, highly valuable for understanding the fundamental mechanism of ion irradiation on 2D systems and designing/optimizing low-dimensional structures for nanoelectronics, spintronics, optics, energy storage and environmental protection.

With the establishment of machine learning (ML) techniques in the scientific community, the construction of ML potential energy surfaces (ML-PES) has become a standard process in physics and chemistry. So far, improvements in the construction of ML-PES models have been conducted independently, creating an initial hurdle for new users to overcome and complicating the reproducibility of results. Aiming to reduce the bar for the extensive use of ML-PES, we introduce Asparagus, a software package encompassing the different parts into one coherent implementation that allows an autonomous, user-guided construction of ML-PES models. Asparagus combines capabilities of initial data sampling with interfaces to ab initio calculation programs, ML model training, as well as model evaluation and its application within other codes such as ASE or CHARMM. The functionalities of the code are illustrated in different examples, including the dynamics of small molecules, the representation of reactive potentials in organometallic compounds, and atom diffusion on periodic surface structures. The modular framework of Asparagus is designed to allow simple implementations of further ML-related methods and models to provide constant user-friendly access to state-of-the-art ML techniques.

We present torchami, an advanced implementation of algorithmic Matsubara integration (AMI) that utilizes pytorch as a backend to provide easy parallelization and GPU support. AMI is a tool for analytically resolving the sequence of nested Matsubara integrals that arise in virtually all Feynman perturbative expansions. In this implementation we present a new AMI algorithm that creates a more natural symbolic representation of the Feynman integrands. In addition, we include peripheral tools that allow for import and labeling of simple graph structures and conversion to torchami input. The code is written in c++ with python bindings provided.

Finding first integrals of second-order nonlinear ordinary differential equations (nonlinear 2ODEs) is a very difficult task. In very complicated cases, where we cannot find Darboux polynomials (to construct an integrating factor) or a Lie symmetry (that allows us to simplify the equations), we sometimes can solve the problem by using a nonlocal symmetry. In [1], [2], [3] we developed (and improved) a method (S-function method) that is successful in finding nonlocal Lie symmetries to a large class of nonlinear rational 2ODEs. However, even with the nonlocal symmetry, we still need to solve a 1ODE (which can be very difficult to solve) to find the first integral. In this work we present a novel way of using the nonlocal symmetry to compute the first integral with a very efficient linear procedure.

PyWolf is an open-source software with a graphical user interface that performs numerical simulations of the cross-spectral density function propagation of planar sources using parallel computation through PyOpenCL. In the previous versions of PyWolf, the user could select the OpenCL devices and platforms to perform the parallel computations on several tasks, except for that related to the two-dimensional (2D) fast Fourier transform (FFT) algorithm. The latter task can have a large computation time since one has to perform a large amount of 2D FFTs over 2D slices of a four-dimensional array. The option of using multithread-based computation on these loops and other tasks can be an advantage for multi-core CPUs and can significantly decrease the computation time. Here, I present version 3.0.0 of PyWolf, which adds a multithreading option to be used for the 2D FFT computations. This multithreading option can also be easily implemented in other time-consuming tasks.

This paper introduces Ph3pyWF, a Python software package we designed to facilitate high-throughput analysis of lattice thermal conductivity in ceramic materials. The user interface caters to individuals with varying expertise, accommodating both novices and experts in the field. For beginners, only the initial structure file is required as input, as the software automatically populates other necessary parameters. Advanced users can customize numerous procedure parameters to suit their specific research needs. At its core, Ph3pyWF aims to establish an efficient data exchange and task management system. This paper elucidates the design details of the software package and presents several examples of its application to oxide ceramics, showcasing its general applicability and practicality in the analysis of lattice thermal conductivity.

The observation of superthermal plasma distributions in space reveals a multitude of distributions with high-energy tails, and the kappa-Maxwellian distribution is a type of non-Maxwellian distribution that exhibits this characteristic. However, accurately determining the multiple roots of the dispersion relation for superthermal plasma waves propagating obliquely presents a challenge. To tackle this issue, we have developed a comprehensive solver, BO-KM, utilizing an innovative numerical algorithm that eliminates the need for initial value iteration. The solver offers an efficient approach to simultaneously compute the roots of the kinetic dispersion equation for oblique propagation in magnetized plasmas. It can be applied to magnetized superthermal plasma with multi-species, characterized by anisotropic drifting kappa-Maxwellian, bi-Maxwellian distributions, or a combination of the two. The rational and J-pole Padé expansions of the dispersion relation are equivalent to solving a linear system's matrix eigenvalue problem. This study presents the numerical findings for kappa-Maxwellian plasmas, bi-Maxwellian plasmas, and their combination, demonstrating the solver's outstanding performance through benchmark analyses.

The Neoclassical Toroidal Viscosity (NTV) torque is a crucial source of toroidal momentum in tokamaks, exerting significant influence on plasma instability and performance. Accurate numerical modeling of NTV torque is essential for experimental design and operation, as well as for gaining insight into the relevant physical processes. However, the time-consuming nature of NTV torque calculation poses challenges for its practical application in experiment analysis and physical investigations. In this study, we have developed NTVTOK-ML, a surrogate model for NTV torque calculation that combines the expressive power and fast inference of machine learning methods to achieve simultaneous accuracy and time efficiency. To obtain datasets for NTV torque, extensive numerical calculations using NTVTOK and MARS-F codes were performed under various plasma conditions of Experimental Advanced Superconducting Tokamak (EAST), covering a wide range of experimentally relevant parameter regimes and incorporating rich physical effects such as pitch angle scattering, full toroidal geometry, resonances, etc. For fixed magnetic perturbation case, NTVTOK-ML employs Multi-Layer Perceptron (MLP) deep neural network and eXtreme Gradient Boosting (XGBoost) ensemble learning techniques respectively. Furthermore, when considering linear plasma response effect, Convolutional Neural Network (CNN) is utilized to process two-dimensional magnetic perturbation data. The prediction accuracy of NTVTOK-ML is evaluated based on statistical metrics including coefficient of determination (R^2), mean squared error (MSE), and relative error; single sample prediction ability; and generalization ability - demonstrating its reliability in NTV torque prediction tasks. Importantly, the computational time required for predicting NTV torque using our proposed approach is significantly reduced compared to the original numerical code by several orders of magnitude. Additionally, the flexibility offered by the NTVTOK-ML framework allows users to optimize model performance under specific circumstances. Overall, our developed method provides an accessible solution for rapid yet accurate prediction of NTV torque while incorporating essential physical effects - thereby facilitating real-time or inter-shot analysis in experiments as well as comprehensive multi-scale nonlinear time evolution modeling.

The Fluid Properties module within the Multiphysics Object-Oriented Simulation Environment (MOOSE) is used to compute fluid properties for numerous applications, ranging from nuclear reactor thermal hydraulics to geothermal energy. Those applications drove the development of the module to enable numerous different fluid equations of states, property lookups with primitive and conserved flow variable to cater to pressure and density-driven solvers, and an object-oriented design facilitating expansion and maintenance. Each fluid property is implemented in its own class but inherits capabilities such as automatic differentiation, automated out-of-bounds handling or variable conversion capabilities. This paper presents the module, its design, its user and developer interface, its content in terms of fluids and properties, and several of its applications showing its major role in the MOOSE simulation ecosystem.

The software MCEND (Multi-Configuration Electron-Nuclear Dynamics) is a free open-source program package which simulates the quantum dynamics of electron-nuclei simultaneously for diatomic molecules. Its formulation, implementation, and usage are described in detail. MCEND uses a grid-based basis representation for the nuclei, and the electronic basis is derived from standard electronic structure basis sets on the nuclear grid. The wave function is represented as a sum over products of electronic and nuclear wave functions, thus capturing correlation effects between electrons, nuclei, and electrons and nuclei. The LiH molecule was used as an example for simulating the molecular properties such as the dipole moment and absorption spectrum.