Computer Physics Communications

We provide an implementation of the unstructured Finite-Volume Arbitrary Lagrangian / Eulerian (ALE) Interface-Tracking method for simulating incompressible, immiscible two-phase flows as an OpenFOAM module. In addition to interface-tracking capabilities that include tracking of two fluid phases, an implementation of a Subgrid-Scale (SGS) modeling framework for increased accuracy when simulating sharp boundary layers is enclosed. The SGS modeling framework simplifies embedding subgrid-scale profiles into the unstructured Finite Volume discretization. Our design of the SGS model library significantly simplifies adding new SGS models and applying SGS modeling to Partial Differential Equations (PDEs) in OpenFOAM.

We present an open source program, PolyPal, that can generate a polycrystalline virtual specimen in the micrometer scale for atomistic calculations and visualization. Unlike regular meshes or perfect lattices, atomic positions in polycrystalline materials need to be defined before calculations, and the capability of an atom-generation code is evaluated by the maximum size of the virtual specimen it can generate as well as by the efficiency of the necessary input-output process. Present atom-generation codes are implemented in a serial fashion, and the maximum size of the virtual specimen is limited by the on-board memory. Furthermore, it is difficult to handle a single position file with billions of atoms not only because it takes a long time to read in a row but also full domain decomposition takes hours. PolyPal addresses these challenges with a fully parallelized MPI input-output scheme that supports multiple export options on a Linux cluster. It has no limit in the system size with virtually perfect scalability. Additionally by controlling the size distribution and homogeneity of grains, the program can simulate different microstructures, as typically found in the bulk system or in thin-film samples, prepared with different fabrication processes. PolyPal will harness molecular dynamics codes in the coming age of the exascale computing.

We introduce HepLean, an open-source project to digitalise definitions, theorems, proofs, and calculations in high energy physics using the interactive theorem prover Lean 4. HepLean has the potential to benefit the high energy physics community in four ways: making it easier to find existing results, allowing the creation of new results using artificial intelligence and automated methods, allowing easy review of papers for mathematical correctness, and providing new ways to teach high energy physics. We will discuss these in detail. We will also demonstrate the digitalisation of three areas of high energy physics in HepLean: Cabibbo-Kobayashi-Maskawa matrices in flavour physics, local anomaly cancellation, and Higgs physics.

We present TriMe++, a multi-threaded software library designed for generating two-dimensional meshes for intricate geometric shapes using the Delaunay triangulation. Multi-threaded parallel computing is implemented throughout the meshing procedure, making it suitable for fast generation of large-scale meshes. Three iterative meshing algorithms are implemented: the DistMesh algorithm, the centroidal Voronoi diagram meshing, and a hybrid of the two. We compare the performance of the three meshing methods in TriMe++, and show that the hybrid method retains the advantages of the other two. The software library achieves significant parallel speedup when generating large-scale meshes containing between 10^4 to 10^7 points. TriMe++ can handle complicated geometries and generates adaptive meshes of high quality.

ComDMFT is a parallel computational package designed to study the electronic structure of correlated quantum materials from first principles. Our approach is based on the combination of first-principles methods and dynamical mean field theories. In version 2.0, we implemented fully-diagrammatic GW+EDMFT from first-principles self-consistently. In this approach, correlated electrons are treated within full GW+EDMFT and the rest are treated within full-GW, seamlessly. This implementation enables the electronic structure calculation of quantum materials with weak, intermediate, and strong electron correlation without prior knowledge of the degree of electron correlation.

In our effort to facilitate machine learning-assisted computational fluid dynamics (CFD), we introduce the second iteration of JAX-Fluids. JAX-Fluids is a Python-based fully-differentiable CFD solver designed for compressible single- and two-phase flows. In this work, the first version is extended to incorporate high-performance computing (HPC) capabilities. We introduce a parallelization strategy utilizing JAX primitive operations that scales efficiently on GPU (up to 512 NVIDIA A100 graphics cards) and TPU (up to 1024 TPU v3 cores) HPC systems. We further demonstrate stable parallel computation of automatic differentiation gradients across extended integration trajectories. The new code version offers enhanced two-phase flow modeling capabilities. In particular, a five-equation diffuse-interface model is incorporated which complements the level-set sharp-interface model. Additional algorithmic improvements include positivity-preserving limiters for increased robustness, support for stretched Cartesian meshes, refactored I/O handling, comprehensive post-processing routines, and an updated list of state-of-the-art high-order numerical discretization schemes. We verify newly added numerical models by showcasing simulation results for single- and two-phase flows, including turbulent boundary layer and channel flows, air-helium shock bubble interactions, and air-water shock drop interactions.

There is increasing interest in deep exclusive meson production (DEMP) reactions, as they provide access to Generalized Parton Distributions over a broad kinematic range, and are the only means of measuring pion and kaon charged electric form factors at high Q^2. Such investigations are a particularly useful tool in the study of hadronic structure in QCD's transition regime from long-distance interactions described in terms of meson-nucleon degrees of freedom, to short-distance interactions governed by hard quark-gluon degrees of freedom. To assist the planning of future experimental investigations of DEMP reactions in this transition regime, such as at Jefferson Lab and the Electron-Ion Collider (EIC), we have written a special purpose event generator, DEMPgen. Currently, DEMPgen can generate the following reactions: t-channel p(e, e'π^+)n, p(e, e'K^+)Λ[Σ^0] and n(e, e'π^-)p from a polarized 3He target. DEMPgen is modular in form, so that additional reactions can be added over time. The generator produces kinematically-complete reaction events which are absolutely-normalized, so that projected event rates can be predicted, and detector resolution requirements studied. The event normalization is based on parameterizations of theoretical models, appropriate to the kinematic regime under study. Both fixed target modes and collider beam modes are supported. This paper presents the structure of the generator, the model parameterizations used for absolute event weighting, the kinematic distributions of the generated particles, some initial results using the generator, and instructions for its use.

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.