The ablation of solid objects embedded in hypersonic flows can be simulated via the direct simulation Monte Carlo method. However, current ablation algorithms can suffer from numerical and geometric issues which distort the receding surface. This in turn can affect near-surface flow behavior and the overall ablation rate. To minimize these effects and enable more physically realistic ablation models, two algorithmic modifications are proposed here. The first is to store multiple values in each grid cell surrounding the surface so that the Marching Cubes algorithm (which triangulates the surface from the grid values) can be made more accurate. The second is to adjust grid values (due to collisions and reactions) in a mass-flux-preserving manner by decrementing multiple grid values simultaneously. Several ablation test cases are simulated to illustrate the effect of these enhancements.

SPPARKS is an open-source parallel simulation code for developing and running various kinds of on-lattice Monte Carlo models at the atomic or meso scales. It can be used to study the properties of solid-state materials as well as model their dynamic evolution during processing. The modular nature of the code allows new models and diagnostic computations to be added without modification to its core functionality, including its parallel algorithms. A variety of models for microstructural evolution (grain growth), solid-state diffusion, thin film deposition, and additive manufacturing (AM) processes are included in the code. SPPARKS can also be used to implement grid-based algorithms such as phase field or cellular automata models, to run either in tandem with a Monte Carlo method or independently. For very large systems such as AM applications, the Stitch I/O library is included, which enables only a small portion of a huge system to be resident in memory. In this paper we describe SPPARKS and its parallel algorithms and performance, explain how new Monte Carlo models can be added, and highlight a variety of applications which have been developed within the code.

Since the classical molecular dynamics simulator LAMMPS was released as an open source code in 2004, it has become a widely-used tool for particle-based modeling of materials at length scales ranging from atomic to mesoscale to continuum. Reasons for its popularity are that it provides a wide variety of particle interaction models for different materials, that it runs on any platform from a single CPU core to the largest supercomputers with accelerators, and that it gives users control over simulation details, either via the input script or by adding code for new interatomic potentials, constraints, diagnostics, or other features needed for their models. As a result, hundreds of people have contributed new capabilities to LAMMPS and it has grown from fifty thousand lines of code in 2004 to a million lines today. In this paper several of the fundamental algorithms used in LAMMPS are described along with the design strategies which have made it flexible for both users and developers. We also highlight some capabilities recently added to the code which were enabled by this flexibility, including dynamic load balancing, on-the-fly visualization, magnetic spin dynamics models, and quantum-accuracy machine learning interatomic potentials. Program Summary Program Title: Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) CPC Library link to program files: https://doi .org /10 .17632 /cxbxs9btsv.1 Developer's repository link: https://github .com /lammps /lammps Licensing provisions: GPLv2 Programming language: C++, Python, C, Fortran Supplementary material: https://www.lammps .org Nature of problem: Many science applications in physics, chemistry, materials science, and related fields require parallel, scalable, and efficient generation of long, stable classical particle dynamics trajectories. Within this common problem definition, there lies a great diversity of use cases, distinguished by different particle interaction models, external constraints, as well as timescales and lengthscales ranging from atomic to mesoscale to macroscopic. Solution method: The LAMMPS code uses parallel spatial decomposition, distributed neighbor lists, and parallel FFTs for long-range Coulombic interactions [1]. The time integration algorithm is based on the Stormer-Verlet symplectic integrator [2], which provides better stability than higher-order non-symplectic methods. In addition, LAMMPS supports a wide range of interatomic potentials, constraints, diagnostics, software interfaces, and pre- and post-processing features. Additional comments including restrictions and unusual features: This paper serves as the definitive reference for the LAMMPS code. References [1] S. Plimpton, Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys. 117 (1995) 1-19. [2] L. Verlet, Computer experiments on classical fluids: I. Thermodynamical properties of Lennard-Jones molecules, Phys. Rev. 159 (1967) 98-103. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The topological constraints of nonconcatenated ring polymers force them to form compact loopy globular conformations with much lower entropy than unconstrained ideal rings. The closed-loop structure of ring polymers also enables them to be threaded by linear polymers in ring/linear blends, resulting in less compact ring conformations with higher entropy. This conformational entropy increase promotes mixing rings with linear polymers. Here, using molecular dynamics simulations for bead-spring chains, ring/linear blends are shown to be significantly more miscible than linear/linear blends and that there is an entropic mixing, negative χ, for ring/linear blends compared to linear/linear and ring/ring blends. In analogy with small angle neutron scattering, the static structure function S(q) is measured, and the resulting data are fit to the random phase approximation model to determine χ. In the limit that the two components are the same, χ = 0 for the linear/linear and ring/ring blends as expected, while χ < 0 for the ring/linear blends. With increasing chain stiffness, χ for the ring/linear blends becomes more negative, varying reciprocally with the number of monomers between entanglements. Ring/linear blends are also shown to be more miscible than either ring/ring or linear/linear blends and stay in single phase for a wider range of increasing repulsion between the two components.

In this work we present a parallel algorithm for the Concurrent Atomistic Continuum (CAC) formulation that can be integrated into existing molecular dynamics codes. The CAC methodology is briefly introduced and its parallel implementation in LAMMPS is detailed and then demonstrated through benchmarks that compare CAC simulation results with corresponding all-MD (molecular dynamics) results. The parallel efficiency of the algorithm is demonstrated when simulating systems represented by both atoms and finite elements. The verification benchmarks include dynamic crack propagation and branching in a Si single crystal, wave propagation and scattering in a Si phononic crystal, and phonon transport through the phase interface in a PbTe/PbSe heteroepitaxial system. In each of these benchmarks the CAC algorithm is shown to be in good agreement with MD-only models. This parallel CAC algorithm thus offers one of the first scalable multiscale material simulation methodologies that relies solely on atomic-interaction models. (c) 2022 Published by Elsevier Inc.

script naming prefixes and suffixes, and options in script headers. We demonstrate several modular, object-like utility scripts that can be used alone or combined into other utilities. These include developer tools such as a System.Echo substitute for Windows, a Window inspector, and a script for detecting and dealing with multiple display resolutions.

The Exascale Computing Project (ECP) is invested in co-design to assure that key applications are ready for exascale computing. Within ECP, the Co-design Center for Particle Applications (CoPA) is addressing challenges faced by particle-based applications across four “sub-motifs”: short-range particle–particle interactions (e.g., those which often dominate molecular dynamics (MD) and smoothed particle hydrodynamics (SPH) methods), long-range particle–particle interactions (e.g., electrostatic MD and gravitational N-body), particle-in-cell (PIC) methods, and linear-scaling electronic structure and quantum molecular dynamics (QMD) algorithms. Our crosscutting co-designed technologies fall into two categories: proxy applications (or “apps”) and libraries. Proxy apps are vehicles used to evaluate the viability of incorporating various types of algorithms, data structures, and architecture-specific optimizations and the associated trade-offs; examples include ExaMiniMD, CabanaMD, CabanaPIC, and ExaSP2. Libraries are modular instantiations that multiple applications can utilize or be built upon; CoPA has developed the Cabana particle library, PROGRESS/BML libraries for QMD, and the SWFFT and fftMPI parallel FFT libraries. Success is measured by identifiable “lessons learned” that are translated either directly into parent production application codes or into libraries, with demonstrated performance and/or productivity improvement. The libraries and their use in CoPA’s ECP application partner codes are also addressed.

For high-Mach-number turbulent flows, the Kolmogorov length scale can be comparable to the gas-molecule mean-free path, which could introduce noncontinuum molecular-level effects into the turbulent energy cascade. To investigate this issue, compressible Taylor-Green vortex flow is simulated using both noncontinuum molecular gas dynamics and continuum computational fluid dynamics. Although the energy-decay rates are the same, molecular-level fluctuations break the flow symmetries and thereby produce different but statistically similar routes from the initial nonturbulent flow to the long-time turbulent flow.