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 Størmer-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.
Sprague, Michael S.; Ananthan, Shreyas A.; Gruchalla, Kenny G.; lawson, michael j.; Rood, Jon R.; Swirydowicz, K.S.; Thomas, Steve T.; Vijayakumar, Ganesh V.; Crozier, Paul C.; Dohrmann, Clark R.; Hu, Jonathan J.; Williams, Alan B.; Turner, John A.; Prokopenko, A.P.; Moser, Robert M.; Melvin, J.M.
The SPARC (Sandia Parallel Aerodynamics and Reentry Code) will provide nuclear weapon qualification evidence for the random vibration and thermal environments created by re-entry of a warhead into the earth’s atmosphere. SPARC incorporates the innovative approaches of ATDM projects on several fronts including: effective harnessing of heterogeneous compute nodes using Kokkos, exascale-ready parallel scalability through asynchronous multi-tasking, uncertainty quantification through Sacado integration, implementation of state-of-the-art reentry physics and multiscale models, use of advanced verification and validation methods, and enabling of improved workflows for users. SPARC is being developed primarily for the Department of Energy nuclear weapon program, with additional development and use of the code is being supported by the Department of Defense for conventional weapons programs.
This report evaluates several interpolants implemented in the Digital Image Correlation Engine (DICe), an image correlation software package developed by Sandia. By interpolants we refer to the basis functions used to represent discrete pixel intensity data as a continuous signal. Interpolation is used to determine intensity values in an image at non - pixel locations. It is also used, in some cases, to evaluate the x and y gradients of the image intensities. Intensity gradients subsequently guide the optimization process. The goal of this report is to inform analysts as to the characteristics of each interpolant and provide guidance towards the best interpolant for a given dataset. This work also serves as an initial verification of each of the interpolants implemented.
Computational science and engineering application programs are typically large, complex, and dynamic, and are often constrained by distribution limitations. As a means of making tractable rapid explorations of scientific and engineering application programs in the context of new, emerging, and future computing architectures, a suite of "miniapps" has been created to serve as proxies for full scale applications. Each miniapp is designed to represent a key performance characteristic that does or is expected to significantly impact the runtime performance of an application program. In this paper we introduce a methodology for assessing the ability of these miniapps to effectively represent these performance issues. We applied this methodology to three miniapps, examining the linkage between them and an application they are intended to represent. Herein we evaluate the fidelity of that linkage. This work represents the initial steps required to begin to answer the question, "Under what conditions does a miniapp represent a key performance characteristic in a full app?"
This report summarizes the result of LDRD project 12-0395, titled "Automated Algorithms for Quantum-level Accuracy in Atomistic Simulations." During the course of this LDRD, we have developed an interatomic potential for solids and liquids called Spectral Neighbor Analysis Poten- tial (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The SNAP coef- ficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. Global optimization methods in the DAKOTA software package are used to seek out good choices of hyperparameters that define the overall structure of the SNAP potential. FitSnap.py, a Python-based software pack- age interfacing to both LAMMPS and DAKOTA is used to formulate the linear regression problem, solve it, and analyze the accuracy of the resultant SNAP potential. We describe a SNAP potential for tantalum that accurately reproduces a variety of solid and liquid properties. Most significantly, in contrast to existing tantalum potentials, SNAP correctly predicts the Peierls barrier for screw dislocation motion. We also present results from SNAP potentials generated for indium phosphide (InP) and silica (SiO 2 ). We describe efficient algorithms for calculating SNAP forces and energies in molecular dynamics simulations using massively parallel computers and advanced processor ar- chitectures. Finally, we briefly describe the MSM method for efficient calculation of electrostatic interactions on massively parallel computers.
This document summarizes the work done in our three-year LDRD project titled 'Physics of Intense, High Energy Radiation Effects.' This LDRD is focused on electrical effects of ionizing radiation at high dose-rates. One major thrust throughout the project has been the radiation-induced conductivity (RIC) produced by the ionizing radiation. Another important consideration has been the electrical effect of dose-enhanced radiation. This transient effect can produce an electromagnetic pulse (EMP). The unifying theme of the project has been the dielectric function. This quantity contains much of the physics covered in this project. For example, the work on transient electrical effects in radiation-induced conductivity (RIC) has been a key focus for the work on the EMP effects. This physics in contained in the dielectric function, which can also be expressed as a conductivity. The transient defects created during a radiation event are also contained, in principle. The energy loss lead the hot electrons and holes is given by the stopping power of ionizing radiation. This information is given by the inverse dielectric function. Finally, the short time atomistic phenomena caused by ionizing radiation can also be considered to be contained within the dielectric function. During the LDRD, meetings about the work were held every week. These discussions involved theorists, experimentalists and engineers. These discussions branched out into the work done in other projects. For example, the work on EMP effects had influence on another project focused on such phenomena in gases. Furthermore, the physics of radiation detectors and radiation dosimeters was often discussed, and these discussions had impact on related projects. Some LDRD-related documents are now stored on a sharepoint site (https://sharepoint.sandia.gov/sites/LDRD-REMS/default.aspx). In the remainder of this document the work is described in catergories but there is much overlap between the atomistic calculations, the continuum calculations and the experiments.
LAMMPS is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. LAMMPS has potentials for soft materials (biomolecules, polymers) and solid-state materials (metals, semiconductors) and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale. LAMMPS runs on single processors or in parallel using message-passing techniques and a spatial-decomposition of the simulation domain. The code is designed to be easy to modify or extend with new functionality.
We have performed molecular dynamics simulations of cascade damage in the gadolinium pyrochlore Gd{sub 2}Zr{sub 2}O{sub 7}, comparing results obtained from traditional methodologies that ignore the effect of electron-ion interactions with a 'two-temperature model' in which the electronic subsystem is modeled using a diffusion equation to determine the electronic temperature. We find that the electron-ion interaction friction coefficient {gamma}{sub p} is a significant parameter in determining the behavior of the system following the formation of the primary knock-on atom (here, a U{sup 3+} ion). The mean final U{sup 3+} displacement and the number of defect atoms formed is shown to decrease uniformly with increasing {gamma}{sub p}; however, other properties, such as the final equilibrium temperature and the oxygen-oxygen radial distribution function show a more complicated dependence on {gamma}{sub p}.
Application performance is determined by a combination of many choices: hardware platform, runtime environment, languages and compilers used, algorithm choice and implementation, and more. In this complicated environment, we find that the use of mini-applications - small self-contained proxies for real applications - is an excellent approach for rapidly exploring the parameter space of all these choices. Furthermore, use of mini-applications enriches the interaction between application, library and computer system developers by providing explicit functioning software and concrete performance results that lead to detailed, focused discussions of design trade-offs, algorithm choices and runtime performance issues. In this paper we discuss a collection of mini-applications and demonstrate how we use them to analyze and improve application performance on new and future computer platforms.
A molecular-scale interpretation of interfacial processes is often downplayed in the analysis of traditional water treatment methods. However, such an approach is critical for the development of enhanced performance in traditional desalination and water treatments. Water confined between surfaces, within channels, or in pores is ubiquitous in technology and nature. Its physical and chemical properties in such environments are unpredictably different from bulk water. As a result, advances in water desalination and purification methods may be accomplished through an improved analysis of water behavior in these challenging environments using state-of-the-art microscopy, spectroscopy, experimental, and computational methods.
We have enhanced our parallel molecular dynamics (MD) simulation software LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator, lammps.sandia.gov) to include many new features for accelerated simulation including articulated rigid body dynamics via coupling to the Rensselaer Polytechnic Institute code POEMS (Parallelizable Open-source Efficient Multibody Software). We use new features of the LAMMPS software package to investigate rhodopsin photoisomerization, and water model surface tension and capillary waves at the vapor-liquid interface. Finally, we motivate the recipes of MD for practitioners and researchers in numerical analysis and computational mechanics.
We present all-atom molecular dynamics simulations of biologically realistic transmembrane potential gradients across a DMPC bilayer. These simulations are the first to model this gradient in all-atom detail, with the field generated solely by explicit ion dynamics. Unlike traditional bilayer simulations that have one bilayer per unit cell, we simulate a 170 mV potential gradient by using a unit cell consisting of three salt-water baths separated by two bilayers, with full three-dimensional periodicity. The study shows that current computational resources are powerful enough to generate a truly electrified interface, as we show the predicted effect of the field on the overall charge distribution. Additionally, starting from Poisson's equation, we show a new derivation of the double integral equation for calculating the potential profile in systems with this type of periodicity.
This LDRD project has involved the development and application of Sandia's massively parallel materials modeling software to several significant biophysical systems. They have been successful in applying the molecular dynamics code LAMMPS to modeling DNA, unstructured proteins, and lipid membranes. They have developed and applied a coupled transport-molecular theory code (Tramonto) to study ion channel proteins with gramicidin A as a prototype. they have used the Towhee configurational bias Monte-Carlo code to perform rigorous tests of biological force fields. they have also applied the MP-Sala reacting-diffusion code to model cellular systems. Electroporation of cell membranes has also been studied, and detailed quantum mechanical studies of ion solvation have been performed. In addition, new molecular theory algorithms have been developed (in FasTram) that may ultimately make protein solvation calculations feasible on workstations. Finally, they have begun implementation of a combined molecular theory and configurational bias Monte-Carlo code. They note that this LDRD has provided a basis for several new internal (e.g. several new LDRD) and external (e.g. 4 NIH proposals and a DOE/Genomes to Life) proposals.