Computer Physics Communications

Particle tracking is commonly used to study time-dependent behavior in many different types of physical and chemical systems involving constituents that span many length scales, including atoms, molecules, nanoparticles, granular particles, and even larger objects. Behaviors of interest studied using particle tracking information include disorder-order transitions, thermodynamic phase transitions, structural transitions, protein folding, crystallization, gelation, swarming, avalanches and fracture. A common challenge in studies of these systems involves change detection. Change point detection discerns when a temporal signal undergoes a change in distribution. These changes can be local or global, instantaneous or prolonged, obvious or subtle. Moreover, system-wide changes marking an interesting physical or chemical phenomenon (e.g. crystallization of a liquid) are often preceded by events (e.g. pre-nucleation clusters) that are localized and can occur anywhere at anytime in the system. For these reasons, detecting events in particle trajectories generated by molecular simulation is challenging and typically accomplished via ad hoc solutions unique to the behavior and system under study. Consequently, methods for event detection lack generality, and those used in one field are not easily used by scientists in other fields. Here we present a new Python-based tool, dupin, that allows for universal event detection from particle trajectory data irrespective of the system details. dupin works by creating a signal representing the simulation and partitioning the signal based on events (changes within the trajectory). This approach allows for studies where manual annotating of event boundaries would require a prohibitive amount of time. Furthermore, dupin can serve as a tool in automated and reproducible workflows. We demonstrate the application of dupin using three examples and discuss its applicability to a wider class of problems.

We present URANOS-2.0, the second major release of our massively parallel, GPU-accelerated solver for compressible wall flow applications. This latest version represents a significant leap forward in our initial tool, which was launched in 2023 (De Vanna et al. [1]), and has been specifically optimized to take full advantage of the opportunities offered by the cutting-edge pre-exascale architectures available within the EuroHPC JU. In particular, URANOS-2.0 emphasizes portability and compatibility improvements with the two top-ranked supercomputing architectures in Europe: LUMI and Leonardo. These systems utilize different GPU architectures, AMD and NVIDIA, respectively, which necessitates extensive efforts to ensure seamless usability across their distinct structures. In pursuit of this objective, the current release adheres to the OpenACC standard. This choice not only facilitates efficient utilization of the full potential inherent in these extensive GPU-based architectures but also upholds the principles of vendor neutrality, a distinctive characteristic of URANOS solvers in the CFD solvers' panorama. However, the URANOS-2.0 version goes beyond the goals of improving usability and portability; it introduces performance enhancements and restructures the most demanding computational kernels. This translates into a 2× speedup over the same architecture. In addition to its enhanced single-GPU performance, the present solver release demonstrates very good scalability in multi-GPU environments. URANOS-2.0, in fact, achieves strong scaling efficiencies of over 80% across 64 compute nodes (256 GPUs) for both LUMI and Leonardo. Furthermore, its weak scaling efficiencies reach approximately 95% and 90% on LUMI and Leonardo, respectively, when up to 256 nodes (1024 GPUs) are considered. These significant performance advancements position URANOS-2.0 as a state-of-the-art supercomputing platform tailored for compressible wall turbulence applications, establishing the solver as an integrated tool for various aerospace and energy engineering applications, which can span from direct numerical simulations, wall-resolved large eddy simulations, up to most recent wall-modeled large eddy simulations.

In this publication we introduce MAM-STM, a software to autonomously manipulate arbitrary moieties towards specific positions on a metal surface utilizing the tip of a scanning tunneling microscope (STM). Finding the optimal manipulation parameters for a specific moiety is challenging and time consuming, even for human experts. MAM-STM combines autonomous data acquisition with a sophisticated Q-learning implementation to determine the optimal bias voltage, the z-approach distance, and the tip position relative to the moiety. This then allows to arrange single molecules and atoms at will. In this work, we provide a tutorial based on a simulated response to offer a comprehensive explanation on how to use and customize MAM-STM. Additionally, we assess the performance of the machine learning algorithm by benchmarking it within a simulated stochastic environment.

ScaleLat (Scale Lattice) is a computer program written in C for performing the atomic structure analysis of multi-phase system or high entropy alloys (HEAs). The program implements an atomic cluster cell extraction algorithm to obtain all symmetry independent characteristic atomic cluster cells for the complex atomic configurations which are usually obtained from molecular dynamics or kinetic Monte-Carlo simulations at nanoscale or mesoscopic scale. ScaleLat implements an efficient and unique chemical structure matching algorithm to match all extracted atomic clusters from a large supercell (>10^4 atoms) to a representative small one (∼ 10^3 or less), providing the possibility to directly use the highly accurate quantum mechanical methods to study the electronic, magnetic, and mechanical properties of multi-component alloys for complex microstructures. We demonstrate the capability of ScaleLat code by conducting both the atomic structure matching analysis for Fe-12.8 at.% Cr binary alloy and equiatomic CrFeCoNiCu high entropy alloy, successfully obtaining the representative supercells containing 10^2∼10^3 atoms for two systems. The reliability of the proposed chemical structure matching scheme is tested and confirmed by calculating the electronic structures of both examples using trial supercells with various sizes. Overall, ScaleLat program provides a universal platform to efficiently map all essential chemical structures of large complex atomic structures to a relatively easy-handling small supercell for quantum mechanical calculations of various user interested properties.

We describe legume, a free electromagnetic solver that implements the guided-mode expansion method for patterned multilayer waveguides, or photonic crystal slabs. legume has a built-in tool for automatic differentiation, which makes it suitable for the inverse design of photonic crystal structures with desired physical properties. Compared to a previous version of the method (M. Minkov et al., 2020 [12]), here we introduce several new features of the code, we discuss additional technical aspects of the method and its numerical implementation. The novel features that are treated in this paper include: (i) the separation of modes according to their mirror symmetry with respect to a vertical symmetry plane of the photonic structure, (ii) the problem of polarization mixing in coupling to far-field radiation modes, and (iii) the description of active two-dimensional layers through a suitably formulated radiation-matter coupling Hamiltonian, allowing to describe the physics of both weakly and strongly coupled exciton-photon modes, the latter leading to photonic crystal polariton eigenmodes. Detailed and direct comparisons with rigorous coupled-wave analysis simulations are used to test the accuracy of the method and the numerical efficiency of the code. These newly added features of the legume code significantly increase the prospective applications of guided-mode expansion, making it a very practical and versatile tool enabling the design of advanced photonic structures and the description of radiation-matter interaction.

This contribution presents the key elements of YADE, an extensible open-source framework for dynamic simulations. During the past 19 years, YADE has evolved from “Yet Another Dynamic Engine” to a versatile multiscale and multiphysics solver, counting a large, active, and growing community of users and developers. The computationally intense parts of the source code are written in C++, using flexible object models that allow for easy implementation of new features. The source code is wrapped in Python, equipping the software with an interactive kernel used for rapid and concise scene construction, simulation control, post-processing, and debugging. The project, including documentation and examples, is hosted on https://yade-dem.org, while the source code is freely available on GitLab. Over the last decade, YADE has expanded in terms of capabilities thanks to the contribution of many developers from different fields of expertise, including soil and rock mechanics, chemical engineering, physics, bulk material handling, and mineral processing. The rapid growth of YADE can be attributed to (1) the careful and robust design of the framework core, (2) a continuous integration pipeline with fully embedded thorough tests which are executed upon each merge request, ensuring stable compilation for various operating systems, and (3) user-friendliness, facilitated by the Python interface, detailed documentation, and rigorous user support. In this paper, we review the main features of YADE, highlighting its versatility in terms of applications, its flexibility in terms of code development, as well as recent improvements in terms of computational efficiency.

Particle shape plays a major role in the behaviour of most granular systems. This has led to increasing interest in the representation of arbitrarily shaped particles in discrete element method (DEM) simulations. In this paper, we present a simulation approach based on the representation of particle shapes using spherical harmonics where their radii can be calculated in spherical coordinates. An energy-conserving contact model is adopted which is based on the volume of overlap between interacting particles. Contact detection makes use of the bounding spheres of the interacting particles, simplifying its incorporation within a conventional sphere-based DEM code. The volume of overlap and other required quantities are calculated using Gaussian quadrature integration of the spherical cap formed by the bounding spheres. Both the accuracy and the computational cost increase with the number of quadrature points. The algorithm has been implemented as a LAMMPS user package, and verified by means of energy conservation. The performance and parallel scaling of the approach are illustrated, and an observed scaling limitation owing to load imbalance arising from the evaluation of the overlap volume is discussed.

Inelastic neutron scattering (INS) has unique advantages in probing how atoms vibrate and how the vibrations propagate and interact. Such dynamic information is crucial in understanding various material properties, from heat capacity, thermal conductivity, phase transitions, and chemical reactions to more exotic quantum behavior. The analysis and interpretation of the INS spectra often start from a model structure of the sample, followed by a series of calculations to obtain the simulated spectra to compare with experiments. The conventional way to perform such calculations usually requires significant time, computing resources, and specialized expertise. Here, we present a new program named INSPIRED (Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design), which enables users to perform rapid INS simulations in several different ways on their personal computers in just a few clicks, with the crystal structure as the only input file. Specifically, the users can choose a pre-trained symmetry-aware neural network (coupled with an autoencoder) to predict the phonon density of states (DOS), 1D S(E) and 2D S(|Q|,E) spectra for any given structure. One can also choose an existing density functional theory (DFT) calculation from a database (containing over 12,000 crystals), and quickly obtain the simulated INS spectra for single crystals and powders. It is also possible to use pre-trained universal machine learning force fields to relax a given crystal structure, calculate the phonon dispersion and DOS, and, subsequently, the INS spectra. All these functions are implemented with a PyQt graphic user interface. We expect these new tools will benefit broad user communities and significantly improve the efficiency of experiment design, execution, and data analysis for INS.

Laser plasma instabilities (LPIs) have significant influences on the laser energy deposition efficiency and therefore are important processes in inertial confined fusion (ICF). Numerical simulations play important roles in revealing the complex physics of LPIs. Since LPIs are typically a three wave coupling process, the precise simulations of LPIs with kinetic effects require to resolve the laser period (around one femtosecond) and laser wavelength (less than one micron). In the typical ICF experiments, however, LPIs are involved in a spatial scale of several millimeters and a temporal scale of several nanoseconds. Therefore, the precise kinetic simulations of LPIs in such scales require huge computational resources and are hard to be carried out by present kinetic codes like particle-in-cell (PIC) codes. In this paper, a full wave fluid model of LPIs is constructed and numerically solved by the particle-mesh method, where the plasma is described by macro particles that can move across the mesh grids freely. Based upon this model, a two-dimensional (2D) GPU code named PM2D is developed. The PM2D code can simulate the kinetic effects of LPIs self-consistently as normal PIC codes. Moreover, as the physical model adopted in the PM2D code is specifically constructed for LPIs, the required macro particles per grid in the simulations can be largely reduced and thus overall simulation cost is considerably reduced comparing with PIC codes. More importantly, the numerical noise in the PM2D code is much lower, which makes it more robust than PIC codes in the simulation of LPIs for the long-time scale above 10 picoseconds. After the distributed computing is realized, our PM2D code is able to run on GPU clusters with a total mesh grids up to several billions, which meets the requirements for the simulations of LPIs at ICF experimental scale with reasonable cost.

We reformulate the Landau analysis of Feynman integrals with the aim of advancing the state of the art in modern particle-physics computations. We contribute new algorithms for computing Landau singularities, using tools from polyhedral geometry and symbolic/numerical elimination. Inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ) on generalized Euler integrals, we define the principal Landau determinant of a Feynman diagram. We illustrate with a number of examples that this algebraic formalism allows to compute many components of the Landau singular locus. We adapt the GKZ framework by carefully specializing Euler integrals to Feynman integrals. For instance, ultraviolet and infrared singularities are detected as irreducible components of an incidence variety, which project dominantly to the kinematic space. We compute principal Landau determinants for the infinite families of one-loop and banana diagrams with different mass configurations, and for a range of cutting-edge Standard Model processes. Our algorithms build on the Julia package Landau.jl and are implemented in the new open-source package PLD.jl available at https://mathrepo.mis.mpg.de/PLD/.