Strong scaling of general-purpose molecular dynamics simulations on GPUs

We describe a highly optimized implementation of MPI domain decomposition in a GPU-enabled, general-purpose molecular dynamics code, HOOMD-blue (Anderson and Glotzer, arXiv:1308.5587). Our approach is inspired by a traditional CPU-based code, LAMMPS (Plimpton, J. Comp. Phys. 117, 1995), but is implemented within a code that was designed for execution on GPUs from the start (Anderson et al., J. Comp. Phys. 227, 2008). The software supports short-ranged pair force and bond force fields and achieves optimal GPU performance using an autotuning algorithm. We are able to demonstrate equivalent or superior scaling on up to 3,375 GPUs in Lennard-Jones and dissipative particle dynamics (DPD) simulations of up to 108 million particles. GPUDirect RDMA capabilities in recent GPU generations provide better performance in full double precision calculations. For a representative polymer physics application, HOOMD-blue 1.0 provides an effective GPU vs. CPU node speed-up of 12.5x.Comment: 30 pages, 14 figure

New methods for the computer simulation of macromolecular liquid crystals

Molecular simulation of macromolecular liquid crystal (LC) systems has so far been limited by a number of factors: the large size of the molecules themselves and the fact that mesophase formation takes place on length and timescales that are not reasonable to simulate. The work in this thesis develops three methods that can be used to assist in the computer simulation of macromolecular LC systems. Coarse-graining is a technique where instead of representing every atom within a molecule as a single site, a number of atoms are grouped into interaction centres. This coarse-graining procedure has been applied to a liquid crystal dendrimer to enable the bulk phase simulation of the molecule to be studied. The analysis of the results shows that the behaviour for the coarse-grained model closely matches that of a more detailed atomistic model. Phase behaviour in the bulk matches results from X-ray data. The parallel-tempering method (replica exchange method) uses a series of replicas of the same system at different temperatures to improve the sampling of phase space. This technique was applied to two different systems, a bulk phase simulation of an alkane chain and the gas phase simulation of a silsesquioxane liquid crystal dendrimer. The method was then extended to work with a set of replicas which used different potentials. The Tsallis potential was used to soften potentials and allow replicas to sample a greater area of phase space. A third simulation method was applied which used soft-core potentials. This attempted to address the problem of the long timescales needed to see the formation of mesophases in macromolecular systems. Three different anisotropic single site soft-core models were developed and tested for liquid crystals. The results show that the time needed for mesophase formation is reduced for soft-core models and that these models are able to form multiple liquid crystal phases. In addition, the most promising of these soft-core models has been applied to the simulation of more complex liquid crystal systems, represented by multi-site models

The computer simulation of liquid crystals.

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN012490 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Lattice Vibration Study Of Silica Nanoparticle In Suspension

In recent years considerable research has been done in the area of nanofluids . Nanofluids are colloidal suspensions of nanometer size metallic or oxide particles in a base fluid such as water, ethylene glycol. Nanofluids show enhanced heat transfer characteristics compared to the base fluid. The thermal transport properties of nanofluids depend on various parameters e.g. interfacial resistance, Brownian motion of particles, liquid layering at the solid-liquid interface and clustering of nanoparticles. In this work atomic scale simulation has been used to study possible mechanisms affecting the heat transfer characteristics of nanofluids. Molecular dynamics simulation for a single silica nanoparticle surrounded by water molecules has been performed. Periodic boundary condition has been used in all three directions. The effect of nanoparticle size and temperature of system on the thermal conductivity of nanofluids has been studied. It was found that as the size of nanoparticle decreases thermal conductivity of nanofluid increases. This is partially due to the fact that as the diameter of nanoparticle decreases from micrometer to nanometer its surface area to volume ratio increases by a factor of 103. Since heat transfer between the fluid and the nanoparticle takes place at the surface this enhanced surface area gives higher thermal conductivity for smaller particles. Thermal conductivity enhancement is also due to the accumulation of water molecules near the particle surface and the lattice vibration of the nanoparticle. The phonon transfer through the second layer allows the nanofluid thermal conductivity to increase by 23%-27% compared to the base fluid water for 2% concentration of nanosilica

Advances and challenges in computational research of micro and nano flows

This paper presents an overview of past and current research in computational modelling of micro- and nanofluidic systems with particular focus on recent advances in multiscale modelling. Different mesoscale and hybrid molecular-continuum methods are presented. The contributions of these methods to a broad range of applications, as well as the physical and computational modelling challenges associated with the development of these methods, are also discussed

ML4Chem: A Machine Learning Package for Chemistry and Materials Science

ML4Chem is an open-source machine learning library for chemistry and materials science. It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users. ML4Chem follows user-experience design and offers the needed tools to go from data preparation to inference. Here we introduce its atomistic module for the implementation, deployment, and reproducibility of atom-centered models. This module is composed of six core building blocks: data, featurization, models, model optimization, inference, and visualization. We present their functionality and easiness of use with demonstrations utilizing neural networks and kernel ridge regression algorithms.Comment: 32 pages, 11 Figure

Anisotropic shock response of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB)

The thermo-mechanical response of shock-induced pore collapse has been studied using non-reactive all-atom molecular dynamics (MD) and Eulerian continuum simulations for the molecular crystal 1,3,5-triamino-2,4,6-trinitrobenzene (TATB). Three crystal orientations, bracketed by the limiting cases with respect to the crystal structure anisotropy in TATB, are considered in the MD simulations, while an isotropic constitutive model is used for the continuum simulations. Simulations with three impact speeds from 0.5 km s[superscript -1] to 2.0 km s[superscript -1] are investigated. Results from MD and continuum simulations are in agreement in terms of shock wave speeds, temperature distributions, and pore-collapse mechanisms. However, differences arise for other quantities that are also important in hotspot ignition and growth, for example, the skewness of high-temperature distributions and the local temperature field around the post-collapse hotspot, indicating the urgent need to incorporate anisotropic crystal plasticity and strength models into the continuum descriptions. The deformation mechanisms of TATB crystals in the shock-induced pore collapse MD simulations were studied using Strain Functional Analysis. This new approach maps discrete quantities from atomistic simulations onto continuous fields via a Gaussian kernel, from which a unique and complete set of rotationally invariant Strain Functional Descriptors (SFD) is obtained from the high-order central moments of local configurations, expressed in a Solid Harmonics polynomial basis by SO(3) decomposition. Coupled with unsupervised machine learning techniques, the SFD successfully identifies and distinguishes the deformations presented in the MD simulations of shock-compressed TATB crystals. It enables automated detection of disordered structures in the system and can be readily applied to materials with any symmetry class.Includes bibliographical references (pages 142-168)