Workshop Preliminary Program

(PDF)

Nov. 13, 2006

Location: Room Salon J (Marriott)

 

Abstract. The Network for Computational Nanotechnology (NCN) is a multi-university, NSF-funded initiative with a mission to lead in nanotechnology research and education as well as outreach to students and professionals by offering a set of cyber services. These services are at the core of a unique web-based infrastructure tailored to serve the nation’s National Nanotechnology Initiative. The primary NCN outreach vehicle is the nanoHUB (http://www.nanoHUB.org), which currently provides interactive online simulation and educational resources such as tutorials, seminars, and online courses packaged using e-learning standards. In the past 12 months, the educational and outreach services were accessed by over 16,200 users. More than 3,500 users performed over 94,000 online simulations thanks to the middleware supporting the nanoHUB. Over 30 applications are available online ranging from toy models to sophisticated simulation engines. The NCN provides the resources for modeling, simulating and computing without any software installation to users with access to a web browser. All the NCN services are freely open to the public. The NCN represents a community–based, service–oriented, science architecture that leads the way for new cyberinfrastructure-enabled science.

At the core, the NCN research mission is based on four research themes: nanoelectronics, nanoelectromechanics, nano-bio, and high performance computation (HPC). One facet of the NCN goals is the development of new “community codes” that provide the nanoscience research community with new capabilities and that lay a foundation for a new generation of CAD tools that will pave the way to ground breaking nanotechnology devices. The HPC theme is charged with “grand challenges” to enable the three nanotechnology research themes to study realistically complex systems that have previously been computationally impossible or too expensive to explore. The development of numerical techniques and their packaging into portable software, capable of a variety of high accuracy simulations of nano-electronic, nano-biological, and nano-mechanical systems is the overarching goal of this research theme. Several codes that stem from NCN research supported efforts have now been deployed or are in the last steps before deployment on the nanoHUB as parallel applications.

Large-scale usage of such parallel applications will be through a new TeraGrid initiative called “Science Gateway”. The nanoHUB and the TeraGrid are now partnering to provide state-of-the-art computing resources to the nanoHUB users. With the TeraGrid entering a new phase after its construction, the nanoHUB is getting connected to the TeraGrid to make TeraGrid resources available to nanoHUB users as transparent compute backends. Computationally intensive nanoHUB applications that solve problems across multiple computational scales are being deployed on TeraGrid resources, and NCN partners will be able to access these resources for their own research. Science Gateways represent a shift from the traditional high performance computing use by enabling entire communities of users associated with a common scientific goal to use the national resources through a common interface. Science Gateways are enabled by a community allocation whose goal is to delegate account management, accounting, certificates management and user support to the gateway developers.

Finally, the NCN recognizes that scientific codes are really only accessible to a community if they are truly usable. We are developing a framework that enables rapid deployment of legacy applications with appealing graphical user interfaces (GUIs). This framework, called Rappture, lets developers forego the creation of their own I/O routines, and it generates user friendly GUIs automatically. The nanoHUB middleware serves these GUI based applications through the user’s browsers and makes the connection to the compute backends through virtualization technology. A visualization server has been developed to deliver interactive data analysis to at least 100 users simultaneously with hardware accelerated graphics delivered to the end user without any local hardware.

In this talk, we will present the NCN, the nanoHUB middleware and its capabilities as well as the educational technology supporting our teaching strategy. Finally, we will show how the nanoHUB infrastructure leads to state-of-the-art research in nanotechnology by showing concrete examples of deployed tools.

Abstract. In nanoscience and technology, the process development of synthesizing real materials is one of important steps for industrial application, as well as the finding of novel properties of nanostructures. Large-scale simulations have been carried out so far for obtaining mechanical properties of carbon nanotubes ranged from simple nanotubes to the structured composite material, by using the tight-binding molecular dynamics on the Earth Simulator. As fundamental researches, elastic properties as stretching, compressing, bending and buckling have been investigated on single-walled, double-walled carbon nanotubes and compound ones, ‘peapod.’ Expanding the simulation of fundamental properties, we have also developed a process-simulation that enables us to design and build up carbon-nanotube complex with new applicability for increasing mechanical strength, transporting energy and transmitting information. Through large-scale simulation on bundling and welding carbon nanotubes without additive molecule, we have reached new carbon-nanotube complex structures that consist of sp2 and sp3 orbital interaction and are expected to be super tough materials with high conductivity and flexibility. Our research indicates that large-scale simulation is a quite effective methodology for nanomaterial design and its application.

Abstract. We propose a parallelization scheme for the conjugate gradient method by Teter et. al. and report a detailed analysis of its scalability. We use MPI collective operations exclusively to take advantage of optimized collective implementations with possible hardware support. Our parallel conjugate gradient calculation can be applied in addition to the already implemented parallelism in the application ABINIT. We propose distribution schemes for the band vectors and the 3D-FFT, and provide both a detailed runtime and scalability analysis and a model for the used collective operations. We use this model of collective communication to predict the parallel scaling and to show that the scalability is mostly limited by the communication. Our codes scales up to 52 processors for a small 43 atom system and up to 120 processors for a larger 86 atom system for a single k-point on our test cluster. Our results suggest that non blocking collective communication could be used to enhace the application running time especially for cluster computers.

Abstract. In this talk, I will represent application of parallel processors for simulating very fast phenomena in materials like carbon nanotubes. In this simulation, real-time dynamics of electron and ions are simultaneously treated by solving the time-dependent Schrödinger equation for electron wave functions, and by solving the classical Newton’s equation for ions. These simulations can be done without any adjustable parameters on the basis of the Time-Dependent Density Functional Theory (TDDFT) within the Ehrenfest scheme for the molecular dynamics (MD) simulation. In the initial condition of the TDDFT-MD simulation, we can prepare solution of the time-independent Kohn-Sham equation with either ground state or (mimicked) excited state. In this presentation, I’ll show more detailed technique for the numerical simulation. I will also show some examples in nanotubes and nano-graphite; (1)fast relaxation of photo-induced hot-carriers in nanotubes, and (2)collision of highly charged Ar ion on multiple graphene layers and subsequent structural deformations. These simulations give understanding and predictions for performance of nano-scale device and for structure formations. All of the presented calculations are performed by using the Earth Simulator under collaboration with Profs. A. Rubio and D. Tománek. The author is also supported from Research organization for Information Science and Technology (RIST) in Tokyo.

Abstract. We use an evolutionary process for designing novel photonic structures
for the confinement of light. We show emergence of periodicity and 2 dimensional structures with previously unseen shapes. The evolved structure has sub diffraction limit light confinement and demonstrates periodicity as the principal condition for effective control of distribution of light. We discuss use of evolutionary algorithms and their scaling on large architectures and their application for inverse problems in photonics.

Abstract. Nanotechnology, and particularly the design of materials at the nanoscale, is increasingly dependent on High Performance Computing. In this talk, we will discuss some specific areas of nanoscale materials research at ASU that provide computational challenges, the techniques used to address them, and the challenges that remain in applying HPC to this research. In particular, the following three problems will be examined:

• The carbon sequestration group at ASU is developing advanced chemical and materials concepts to permanently bind the CO2 produced by stationary source fossil fuel combustion (e.g., power generation from coal). Common magnesium rich minerals are decomposed and carbonated in an aqueous high pressure CO2 process to produce a final mineral carbonate which is benign and stable on geological time scales. Recent research has established that a rate limiting step in this process is the formation of a glass-like protective layer on the dissolving mineral feedstock, which hinders the extraction of magnesium. The application of HPC to carry out large scale quantum and classical simulations of this passivating layer's structure and properties has led to important new insights. The performance and scaling of various dynamic liquid/solid process simulations will be discussed in the context of this problem.
• The quantitative simulation of chemical vapor deposition (CVD) processes used to grow new semiconductor systems requires a realistic treatment of gas-gas and gas-solid interactions. The use of new gas phase sources drives growth conditions into unfamiliar regimes, making the insight from simulation invaluable. We will describe large scale quantum molecular dynamics simulations aimed at elucidating the gas-surface interactions in the new poly-silyl-germane system, including hydrogen desorption, decomposition and clustering reactions. HPC requirements for simulation scale-up will be discussed. While many HPC-enabled codes exist for nanoscale simulation, each code works only in specific regimes. Misapplication of these codes is an increasing problem. A short survey will be presented detailing both the scaling properties and the capabilities of several of the primary first principles and classical simulation codes used in nanomaterials research.

Abstract. Nanotechnology is used to create materials, functional or multifunctional structures, and devices on a nanometer (10-9 m) scale. It has emerged from multidisciplinary research fields, one of which is materials science. In the research of nano- mechanics and materials science domain, molecular dynamics plays an important role, especially in the design of novel nanoscale materials such as nanocomposites. However, it has limitations on length and time scales. The material with a cubic volume of 1 ?m3 contains trillions of atoms, and a typical time-step in molecular dynamics simulations is about femtosecond (~10-15 s). Therefore, there is an urgent need for researchers and computational scientists to develop applicable methodology to efficiently simulate large nano systems. Recently, multiscale methods, especially concurrent multiscale methods have been of interest since they are expected to cover a range of physical domains of different length scales from atomic and microscopic/mesoscopic to macroscopic scales. Unfortunately, most multiscale methods still require intensive computation even with existing high-end computers for large nanoscale simulations although such limitations are much smaller in comparison with those in fully molecular dynamics simulations. The above issues spur us to develop an alternative approach – to conduct nanoscale computations with high-performance computing, including Grid computing. Two examples in computational nano-mechanics and materials science domain are given to demonstrate the need of high performance computing. The examples include reliability analysis of nanotubes and mechanics of nanotube/aluminum composites. We also proposed the framework of a Grid-based multiscale method.