Application of lightweight threading techniques to computational chemistry

The recent advent of inexpensive commodity multiprocessor computers with standardized operating system support for lightweight threads provides computational chemists and other scientists with an exciting opportunity to develop sophisticated new approaches to materials simulation. We contrast the flexible performance characteristics of lightweight threading with the restrictions of traditional scientific supercomputing, based on our experiences with multithreaded molecular dynamics simulation. Motivated by the results of our molecular dynamics experiments, we propose an approach to multi-scale materials simulation using highly dynamic thread creation and synchronization within and between concurrent simulations at many different scales. This approach will enable extremely realistic simulations, with computing resources dynamically directed to areas where they are needed. Multi-scale simulations of this kind require large amounts of processing power, but are too sophisticated to be expressed using traditional supercomputing programming models. As a result, we have developed a high-level programming system called Sthreads that allows highly dynamic, nested multithreaded algorithms to be expressed. Program development is simplified through the use of innovative synchronization operations that allow multithreaded programs to be tested and debugged using standard sequential methods and tools. For this reason, Sthreads is very well suited to the complex multi-scale simulation applications that we are developing. This revised version was published online in June 2006 with corrections to the Cover Date.     

Grid computing and biomolecular simulation

Biomolecular computer simulations are now widely used not only in an academic setting to understand the fundamental role of molecular dynamics on biological function, but also in the industrial context to assist in drug design. In this paper, two applications of Grid computing to this area will be outlined. The first, involving the coupling of distributed computing resources to dedicated Beowulf clusters, is targeted at simulating protein conformational change using the Replica Exchange methodology. In the second, the rationale and design of a database of biomolecular simulation trajectories is described. Both applications illustrate the increasingly important role modern computational methods are playing in the life sciences.     

A grid-enabled lightweight computational steering client: a .NET PDA implementation

The grid has been developed to support large-scale computer simulations in a diverse range of scientific and engineering fields. Consequently, the increasing availability of powerful distributed computing resources is changing how scientists undertake large-scale modelling/simulation. Instead of being limited to local computing resources, scientists are now able to make use of supercomputing facilities around the world. These grid resources comprise specialized distributed three-dimensional visualization environments through to massive computational systems. The scientist usually accesses these resources from reasonably high-end desktop computers. Even though most modern desktop computers are provided with reasonably powerful three-dimensional graphical hardware, not all scientific applications require high-end three-dimensional visualization because the data of interest is essentially numerical or two-dimensional graphical data. For these applications, a much simpler two-dimensional graphical displays can be used. Since large jobs can take many hours to complete the scientist needs access to a technology that will allow them to still monitor and control their job while away from their desks. This paper describes an effective method of monitoring and controlling a set of chained computer simulations by means of a lightweight steering client based on a small personal digital assistant (PDA). The concept of using a PDA to steer a series of computational jobs across a supercomputing resource may seem strange at first but when scientists realize they can use these devices to connect to their computation wherever there is a wireless network (or cellular phone network) the concept becomes very compelling. Apart from providing a much needed easy-to-use interface, the PDA-based steering client has the benefit of freeing the scientist from the desktop. It is during this monitoring stage that the hand-held PDA client is of particular value as it gives the application scientist greater freedom to leave his or her desk but still communicate with their simulation, with the proviso that they remain within the range of a wireless network.     

On the performance of molecular dynamics applications on current high-end systems

The effective exploitation of current high performance computing (HPC) platforms in molecular simulation relies on the ability of the present generation of parallel molecular dynamics code to make effective utilisation of these platforms and their components, including CPUs and memory. In this paper, we investigate the efficiency and scaling of a series of popular molecular dynamics codes on the UK's national HPC resources, an IBM p690+ cluster and an SGI Altix 3700. Focusing primarily on the AMBER, DL_POLY and NAMD simulation codes, we demonstrate the major performance and scalability advantages that arise through a distributed, rather than a replicated data approach.     

Learning differential equation models from stochastic agent-based model simulations

Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth–death–migration model commonly used to explore cell biology experiments and a susceptible–infected–recovered model of infectious disease spread.     

应用Monte Carlo算法研究大分子蠕动阻力的影响因素

采用一种基于高分子Monte Carlo模拟算法分析分子蠕动阻力的软件,借助于二维空间中的8配位点键长涨落的格子链模型,对不同温度、分子量、共混小分子时的大分子蠕动行为进行了模拟分析,考察了温度、分子长、共混小分子等因素对蠕动阻力的影响,得到了随着温度增加、分子长减小和共混浓度增加,大分子蠕动阻力减小的结果,这与传统经验理论相符合。这些结果为选择大分子量添加剂黄原胶作为抗结块剂,以及选择β-环糊抑制玻璃化转变等提供了理论依据。     

Probabilistic Analysis and Design of HCP Nanowires:An Efficient Surrogate Based Molecular Dynamics Simulation Approach

We investigate the dependency of strain rate,temperature and size on yield strength of hexagonal close packed(HCP) nanowires based on large-scale molecular dynamics(MD) simulation.A variance-based analysis has been proposed to quantify relative sensitivity of the three controlling factors on the yield strength of the material.One of the major drawbacks of conventional MD simulation based studies is that the simulations are computationally very intensive and economically expensive.Large scale molecular dynamics simulation needs supercomputing access and the larger the number of atoms,the longer it takes time and computational resources.For this reason it becomes practically impossible to perform a robust and comprehensive analysis that requires multiple simulations such as sensitivity analysis,uncertainty quantification and optimization.We propose a novel surrogate based molecular dynamics(SBMD)simulation approach that enables us to carry out thousands of virtual simulations for different combinations of the controlling factors in a computationally efficient way by performing only few MD simulations.Following the SBMD simulation approach an efficient optimum design scheme has been developed to predict optimized size of the nanowire to maximize the yield strength.Subsequently the effect of inevitable uncertainty associated with the controlling factors has been quantified using Monte Carlo simulation.Though we have confined our analyses in this article for Magnesium nanowires only,the proposed approach can be extended to other materials for computationally intensive nano-scale investigation involving multiple factors of influence.     

A Simulation Study of the Self-Assembly of Coarse-Grained Skin Lipids

Computer simulations are an attractive means by which to probe the self-assembly and molecular level organization of lipids in biological membranes. In this work, we study a simple skin lipid system to demonstrate the ability of the coarse-grained models used for fatty acids, cholesterol, and water to self-assemble, thus validating the models for use in further studies of the complex lipid mixtures found in the outermost layer of the skin. Specifically, the ability of the models to predict the correct self-assembled structures from molecular dynamics simulations is compared against those seen experimentally and from all-atom simulations of preassembled bilayers. The nature of the molecular interactions and their roles in the self-assembly process is elucidated and heuristics for self-assembly established. Additionally, the coarse-grained models have been used to characterize the effect of varying cholesterol composition on bilayer properties and the mechanism of bilayer destabilization by short and long chain fatty acids in the presence of cholesterol.     

Artificial Synapses Emulated by an Electrolyte‐Gated Tungsten‐Oxide Transistor

Considering that the human brain uses ≈10¹⁵ synapses to operate, the development of effective artificial synapses is essential to build brain‐inspired computing systems. In biological synapses, the voltage‐gated ion channels are very important for regulating the action‐potential firing. Here, an electrolyte‐gated transistor using WO₃ with a unique tunnel structure, which can emulate the ionic modulation process of biological synapses, is proposed. The transistor successfully realizes synaptic functions of both short‐term and long‐term plasticity. Short‐term plasticity is mimicked with the help of electrolyte ion dynamics under low electrical bias, whereas the long‐term plasticity is realized using proton insertion in WO₃ under high electrical bias. This is a new working approach to control the transition from short‐term memory to long‐term memory using different gate voltage amplitude for artificial synapses. Other essential synaptic behaviors, such as paired pulse facilitation, the depression and potentiation of synaptic weight, as well as spike‐timing‐dependent plasticity are also implemented in this artificial synapse. These results provide a new recipe for designing synaptic electrolyte‐gated transistors through the electrostatic and electrochemical effects.     

A novel combined molecular dynamics–micromechanics method for modeling of stiffness of graphene/epoxy nanocomposites with randomly distributed graphene

In this paper, by combining molecular dynamics and micromechanics methods, a new approach for prediction of the stiffness of the nanocomposites with randomly distributed nanoparticles in the macro level is presented. The molecular dynamics method is used to model the stiffness of the graphene/epoxy nanocomposites containing one layer of an aligned nano graphene embedded in epoxy resin. By considering the large sizes of the length and width of the nano graphene in comparison with its thickness and the shortcomings of the available hardware and software for simulation purposes, a new approach for modeling is also developed. This new approach, by using the moduli of different graphene sheets with different sizes embedded in a representative volume element, can predict the moduli of a real size graphene embedded in the matrix along the longitudinal, transverse and normal directions in the nano-scale. In order to consider the effect of the random distribution of graphene sheets in epoxy resin, a micromechanical approach is used. The results obtained by the molecular dynamics method are used by the micromechanics approach and the stiffness of graphene/epoxy nanocomposites with randomly distributed graphene in the macro-scale is predicted. An experimental program is conducted to evaluate the capability of the model. The result of the modeling is in a very good agreement with the experimental data.     

Simulation of intense particle beams with regularly distributed Gaussian subbeams

Techniques for the simulation of intense particle beams are investigated with respect to the required number of simulation particles. It is shown that for nonchaotic systems it is advantageous if the particles initially are not distributed in a statistical manner but rather arranged in a regular pattern in phase space. This reduces the number of required simulation particles drastically. In the case of such an initially regular arrangement of particles the algorithm which assigns the charges of the particles to the computation mesh becomes of prime importance. The performances of different commonly used algorithms are investigated. The Gaussian assignment algorithm proved far superior to other more commonly used techniques, allowing simulations even at the theoretical limit of 1 particle per cell. Examples for very accurate simulations of beam dynamics with very few particles using an initially regular mesh of particles and Gaussian assignment are given.     

Rapid and accurate ranking of binding affinities of epidermal growth factor receptor sequences with selected lung cancer drugs

The epidermal growth factor receptor (EGFR) is a major target for drugs in treating lung carcinoma. Mutations in the tyrosine kinase domain of EGFR commonly arise in human cancers, which can cause drug sensitivity or resistance by influencing the relative strengths of drug and ATP-binding. In this study, we investigate the binding affinities of two tyrosine kinase inhibitors—AEE788 and Gefitinib—to EGFR using molecular dynamics simulation. The interactions between these inhibitors and the EGFR kinase domain are analysed using multiple short (ensemble) simulations and the molecular mechanics/Poisson–Boltzmann solvent area (MM/PBSA) method. Here, we show that ensemble simulations correctly rank the binding affinities for these systems: we report the successful ranking of each drug binding to a variety of EGFR sequences and of the two drugs binding to a given sequence, using petascale computing resources, within a few days.     

Patient-specific simulation as a basis for clinical decision-making

Patient-specific medical simulation holds the promise of determining tailored medical treatment based on the characteristics of an individual patient (for example, using a genotypic assay of a sequence of DNA). Decision-support systems based on patient-specific simulation can potentially revolutionize the way that clinicians plan courses of treatment for various conditions, ranging from viral infections to arterial abnormalities. Basing medical decisions on the results of simulations that use models derived from data specific to the patient in question means that the effectiveness of a range of potential treatments can be assessed before they are actually administered, preventing the patient from experiencing unnecessary or ineffective treatments. We illustrate the potential for patient-specific simulation by first discussing the scale of the evolving international grid infrastructure that is now available to underpin such applications. We then consider two case studies, one concerned with the treatment of patients with HIV/AIDS and the other addressing neuropathologies associated with the intracranial vasculature. Such patient-specific medical simulations require access to both appropriate patient data and the computational resources on which to perform potentially very large simulations. Computational infrastructure providers need to furnish access to a wide range of different types of resource, typically made available through heterogeneous computational grids, and to institute policies that facilitate the performance of patient-specific simulations on those resources. To support these kinds of simulations, where life and death decisions are being made, computational resource providers must give urgent priority to such jobs, for example by allowing them to pre-empt the queue on a machine and run straight away. We describe systems that enable such priority computing.     

Collective drag and sedimentation: comparison of simulation and experiment in two and three dimensions

We simulate systems of particles immersed in fluid at Reynolds numbers on the particle scale of 0.1 to 20. Our simulation method is based on a finite differencing multi-grid Navier-Stokes solver for the fluid and a molecular dynamics technique for the particle motion. The mismatch between the fixed rectangular grid and the spherical particle shape is taken into account by considering analytical series expansions of the pressure and velocity of the fluid in the vicinity of the particle surface. We give an expression for the force on a particle in terms of the expansion coefficients. At each time step these coefficients are determined from pressure and velocity values on the fluid grid. We demonstrate the validity of our approach by performing numerical simulations of flow through porous solid beds and of bulk sedimentation in two and three spatial dimensions. We compare our results to experimental data and analytical results. Quantitative agreement is found in situations where the volume fraction remains below approximately 0.25 both in two and three dimensions, provided that at the same time the Reynolds number remains below about 10. In contrast, e.g., to finite-element techniques the method remains fast enough to allow dynamical simulations of particle-fluid systems with several hundred spheres on workstations taking all inertial effects into account.     

Multiple time scale method for atomistic simulations

A novel multiple time scale approach is proposed which combines dynamic and static atomistic methods in one numerical simulation. The method is especially effective for modeling processes that consist of two distinct phases: the slow phase when atomic equilibrium positions barely change and the fast phase associated with a rapid change of the system’s configuration. In this case, the slow phase can be effectively modeled using static energy minimization while molecular dynamics (MD) can be applied when specific dynamic effects have to be captured. Compared to direct MD simulations, the new method allows for computational cost savings, and eventually simulation timescale extension, since the major part of the simulation can be modeled as static, without the need to follow vibrations of individual atoms and comply with the critical time step requirement of molecular dynamics. As a result, this approach may allow for modeling loading velocities and strain rates that are more realistic than those currently attainable through direct MD simulations. The fundamental issues in developing this method include the correlation between the MD time scale and quasi-static step-like procedure as well as finding effective criteria for switching between the static and dynamic regimes. The method was inspired by and is applied to simulations of atomic-scale stick-slip friction. Possible applications of the new method to other nano-mechanical problems are also discussed.     

Experimental study of quantum simulation for quantum chemistry with a nuclear magnetic resonance simulator

Quantum computers have been proved to be able to mimic quantum systems efficiently in polynomial time. Quantum chemistry problems, such as static molecular energy calculations and dynamical chemical reaction simulations, become very intractable on classical computers with scaling up of the system. Therefore, quantum simulation is a feasible and effective approach to tackle quantum chemistry problems. Proof-of-principle experiments have been implemented on the calculation of the hydrogen molecular energies and one-dimensional chemical isomerization reaction dynamics using nuclear magnetic resonance systems. We conclude that quantum simulation will surpass classical computers for quantum chemistry in the near future.     

Structure in cohesive powders studied with spin-echo small angle neutron scattering

Extracting structure and ordering information from the bulk of granular materials is a challenging task. Here we present Spin-Echo Small Angle Neutron Scattering Measurements in combination with computer simulations on a fine powder of silica, before and after uniaxial compression. The cohesive powder packing is modeled by using molecular dynamics simulations and the structure, in terms of the density–density correlation function, is calculated from the simulation and compared with experiment. In the dense case, both quantitative and qualitative agreement between measurement and simulations is observed, thus creating the desired link between experiment and computer simulation. Further simulations with appropriate attractive potentials and adequate preparation procedures are needed in order to capture the very loose-packed cohesive powders.     

Excluded volume effects in on‐ and off‐lattice reaction–diffusion models

Mathematical models are important tools to study the excluded volume effects on reaction–diffusion systems, which are known to play an important role inside living cells. Detailed microscopic simulations with off‐lattice Brownian dynamics become computationally expensive in crowded environments. In this study, the authors therefore investigate to which extent on‐lattice approximations, the so‐called cellular automata models, can be used to simulate reactions and diffusion in the presence of crowding molecules. They show that the diffusion is most severely slowed down in the off‐lattice model, since randomly distributed obstacles effectively exclude more volume than those ordered on an artificial grid. Crowded reaction rates can be both increased and decreased by the grid structure and it proves important to model the molecules with realistic sizes when excluded volume is taken into account. The grid artefacts increase with increasing crowder density and they conclude that the computationally more efficient on‐lattice simulations are accurate approximations only for low crowder densities.Inspec keywords: reaction‐diffusion systems, cellular biophysics, biodiffusion, Brownian motion, cellular automata, molecular biophysics, molecular configurationsOther keywords: crowder density, grid artefacts, grid structure, crowded reaction rates, artificial grid, randomly distributed obstacles, crowding molecules, cellular automata models, on‐lattice approximations, crowded environments, off‐lattice Brownian dynamics, detailed microscopic simulations, living cells, mathematical models, off‐lattice reaction‐diffusion models, on‐lattice reaction‐diffusion models, excluded volume effects     

Ab initio molecular dynamics simulation for the insertion process of Si and Ca atoms into C74

A large-scale ab initio molecular dynamics simulation for the insertion process of silicon and calcium atoms into C₇₄ is carried out for the first time by using the all-electron mixed-basis approach, where a one-electron wave function is expressed by superposing plane waves and numerical atomic orbitals. The present numerical results show that a silicon atom with more than a 40 eV kinetic energy can be inserted into C₇₄ through the center of a six-membered ring with a very short relaxation time of about 40 fs, and a calcium atom can be inserted with a 120 eV kinetic energy with a rather long relaxation time (>540 fs).     

Computational steering in Monte Carlo simulations of thin film polystyrene

High molecular weight polymer systems show very long relaxation times, of the order of milliseconds or more. This time-scale proves practically inaccessible for atomic-scale dynamical simulation such as molecular dynamics. Even with a Monte Carlo (MC) simulation, the generation of statistically independent configurations is non-trivial. Many moves have been proposed to enhance the efficiency of MC simulation of polymers. Each is described by a proposal density Q(x'; x): the probability of selecting the trial state x' given that the system is in the current state x. This proposal density must be parametrized for a particular chain length, chemistry and temperature. Choosing the correct set of parameters can greatly increase the rate at which the system explores its configuration space. Computational steering (CS) provides a new methodology for a systematic search to optimize the proposal densities for individual moves, and to combine groups of moves to greatly improve the equilibration of a model polymer system. We show that monitoring the correlation time of the system is an ideal single parameter for characterizing the efficiency of a proposal density function, and that this is best evaluated by a distributed network of replicas of the system, with the operator making decisions based on the averages generated over these replicas. We have developed an MC code for simulating an anisotropic atomistic bead model which implements the CS paradigm. We report simulations of thin film polystyrene.