Computational Nanotechnology Research Articles

Cyber-Enabled Simulations in Nanoscale Science and Engineering

The article describes recent progress in atomic and molecular level modeling and simulation of nanoscale materials and processes, as well as efforts by the US National Science Foundation's Network for Computational Nanotechnology (NCN) to cyber-enable such simulation tools together with instructional materials and research seminars. We believe that making advanced simulation tools widely and easily available to the research and education community will significantly enhance the impact of modeling and simulation on nanoscience and nanotechnology To materialize this vision, NCN established nanoHUB.org, a next-generation Web portal or science gateway that lets users run live, interactive simulations, explore data, and learn-all though a simple Web browser without installing any software or providing compute cycles.

Multifunctional Nanoscale Platforms for Targeting of the Cancer Cell Immortality Spectrum

In the post-genomic era, "omics" platforms and cancer systems biology are greatly advancing our knowledge of the molecular and cellular underpinnings of cancer. In this article, we begin by outlining the factors governing the development of cancer (tumorigenesis) and use this framework to motivate the need for systems-approaches to cancer diagnostics and therapeutics. We review recent efforts to tap into the remarkable potential of nanotechnology for (i) systems-surveillance (or "sensing") of the molecular signatures of tumorigenesis, and (ii) spatiotemporally-regulated delivery (or "targeting") of combination therapeutics to cancer cells. Specifically, we highlight the salient role of polymeric biomaterials and describe the physicochemical characteristics that render them attractive for the design of such nanoscale platforms. We conclude with discussions on the emerging role of macromolecular biophysics and computational nanotechnology in engineering spatiotemporally-regulated anti-cancer systems.

IFHTSE Global 21: heat treatment and surface engineering in the twenty-first century: Part 9 – Influence of materials science on heat treatment and surface engineering

AbstractSignificant progresses have been made in the past decades in the discipline of materials science, which include, but not limit to, the synthesis of new materials, advancement in analytical and experimental techniques, sustainable and environmentally friendly processing technologies, computational material science and nanotechnology. These developments have major influences on the research and application of heat treatment and surface engineering, and provide new opportunities to engineer the surfaces of new and conventional materials using advanced technologies to meet the ever increasing demands in surface and subsurface related properties. This survey gives a brief review on some aspects of heat treatment and surface engineering which keep pace with new developments in materials science. The specific areas being examined include: (i) advanced analytical techniques; (ii) sustainable and environmentally friendly technologies; (iii) surface engineering of emerging new materials such as intermetalli...

Quantitative analysis of hepatitis B virion stability upon single-site amino acid mutation based on computational nanotechnology

Computational biology is an interdisciplinary field that applies the techniques of computer science, functional mathematics and statistics to address problems inspired by biology. Numerous studies have shown that structure of proteins contribute towards their functionality in a direct or indirect way. The structure of viral capsid proteins are critical for hosting and shielding the genetic material and are also crucial for viral entry. In this work, molecular energetics of mutated hepatitis B capsid protein dimer was studied for determining the stability of the virus upon single-site amino acid mutation. Results provide structural information regarding the key residues contributing to hepatitis B virion stability and synthesis. This work illustrates the salience of computational nanotechnology and paves the way for future pharmaceutical applications aimed at the destabilisation of the capsid–surface protein interactions.

JINR CICC in computational chemistry and nanotechnology problems: DL_POLY performance for different communication architectures

This report compares the performance of the DL POLY general-purpose molecular dynamics simulation package on the LIT JINR computing cluster CICC with various communication systems. The comparison involved two cluster architectures: Gigabit Ethernet and InniBand technologies, respectively. The code performance tests include some comparison of the CICC cluster with the special-purpose computer MDGRAPE-3 developed at RIKEN for a high-speed acceleration of the MD (molecular dynamics) without axed cutoff. The DL POLY benchmark covers a set of typical MD system simulations detailed below.

Modeling Molecules: Computational Nanotechnology as a Knowledge Community

I propose that a sociological and historical examination of nanotechnologists can contribute more to an understanding of nanotechnology than an ontological definition. Nanotechnology emerged from the convergent evolution of numerous “technical knowledge communities”—networks of tightly-interconnected people who operate between disciplines and individual research groups. I demonstrate this proposition by sketching the co-evolution of computational chemistry and computational nanotechnology. Computational chemistry arose in the 1950s but eventually segregated into an ab initio, basic research, physics-oriented flavor and an industry-oriented, molecular modeling and visualization, biochemical flavor. Computational nanotechnology arose in the 1990s as a synthesis of these two subgroups, infused by people and practices from computational materials science, engineering, computer science, and elsewhere. I show that to understand the aims and outcomes of computational nanotechnology—and nanotechnology more genera...

NanoHUB.org: Advancing Education and Research in Nanotechnology

In 2002, the National Science Foundation established the Network for Computational Nanotechnology (NCN), a network of universities supporting the National Nanotechnology Initiative by bringing computational tools online, making the tools easy to use, and supporting the tools with educational materials. Along the way, NCN created a unique cyberinfrastructure to support its Web site, nanoHUB.org, where researchers, educators, and professionals collaborate, share resources, and solve real nanotechnology problems. In 2007, nanoHUB.org served more than 56,000 users from 172 countries. In this article, the authors share their experiences in developing this cyberinfrastructure and using it, particularly in an educational context.

Modeling Ion Channels Using Poisson–Nernst–Planck Theory as an Integrated Approach To Introducing Nanotechnology Concepts: The PNP Cyclic Peptide Ion Channel Model

One of the most fundamental challenges in biochemistry and biophysics is describing the function of ion channels, proteins that control ion transport across cell membranes. One approach to this problem is the application of Poisson–Nernst–Planck (PNP) theory. In practice, applying PNP theory involves creating a computational model of the ion channel and generating a numerical solution of differential equations that describes ion transport and electrostatic interactions. The PNP Cyclic Peptide Ion Channel Model is a simulation tool designed to offer an integrated approach to this important problem in bionanotechnology by demonstrating and explaining the application of PNP theory in a way that is accessible to students at an upper undergraduate level. The program interface was made using the free Rapid Application Infrastructure (Rappture) software made available by the Network for Computational Nanotechnology (NCN). Rappture is designed specifically for Web-based applications. Here we introduce a free version of the PNP Cyclic Peptide Ion Channel Model for use in the simulation of ion transport properties for a model ion channel structure that has been the subject of recent experiments.

HUB is where the heart is

The Network for Computational Nanotechnology (NCN) is a six-university initiative that was established in 2002 to connect those who develop simulation tools with those who use them. The NCN currently addresses three science themes, nanoelectronics, nano-electrical-mechanical systems (NEMS) and nanofluidics, and nanomedicine, but is expanding its coverage into other areas of nanotechnology. The NCN's strategy to serve and engage the nanotechnology community centers on a unique, science gateway, www.nanoHUB.org, offering online simulation services for research, education, and collaboration and a new way to publish research and instructional materials. The NCN's primary goal is to lower barriers for the use of simulations in emerging fields of study.

Atomistic Simulation of Realistically Sized Nanodevices Using NEMO 3-D—Part I: Models and Benchmarks

Device physics and material science meet at the atomic scale of novel nanostructured semiconductors, and the distinction between new device or new material is blurred. Not only the quantum-mechanical effects in the electronic states of the device but also the granular atomistic representation of the underlying material are important. Approaches based on a continuum representation of the underlying material typically used by device engineers and physicists become invalid. Ab initio methods used by material scientists typically do not represent the band gaps and masses precisely enough for device design, or they do not scale to realistically large device sizes. The plethora of geometry, material, and doping configurations in semiconductor devices at the nanoscale suggests that a general nanoelectronic modeling tool is needed. The 3-D NanoElectronic MOdeling (NEMO 3-D) tool has been developed to address these needs. Based on the atomistic valence force field and a variety of nearest neighbor tight-binding models (e.g., s, sp <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s*, and sp <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> s*), NEMO 3-D enables the computation of strain and electronic structure for more than 64 and 52 million atoms, corresponding to volumes of (110 nm) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and (101 nm) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , respectively. The physical problem may involve very large scale computations, and NEMO 3-D has been designed and optimized to be scalable from single central processing units to large numbers of processors on commodity clusters and supercomputers. NEMO 3-D has been released with an open-source license in 2003 and is continually developed by the Network for Computational Nanotechnology (NCN). A web-based online interactive version for educational purposes is freely available on the NCN portal ( http://www.nanoHUB.org). In this paper, theoretical models and essential algorithmic and computational components that have been used in the development and successful deployment of NEMO 3-D are discussed.

Hub-based Simulation and Graphics Hardware Accelerated Visualization for Nanotechnology Applications

The Network for Computational Nanotechnology (NCN) has developed a science gateway at nanoHUB.org for nanotechnology education and research. Remote users can browse through online seminars and courses, and launch sophisticated nanotechnology simulation tools, all within their web browser. Simulations are supported by a middleware that can route complex jobs to grid supercomputing resources. But what is truly unique about the middleware is the way that it uses hardware accelerated graphics to support both problem setup and result visualization. This paper describes the design and integration of a remote visualization framework into the nanoHUB for interactive visual analytics of nanotechnology simulations. Our services flexibly handle a variety of nanoscience simulations, render them utilizing graphics hardware acceleration in a scalable manner, and deliver them seamlessly through the middleware to the user. Rendering is done only on-demand, as needed, so each graphics hardware unit can simultaneously support many user sessions. Additionally, a novel node distribution scheme further improves our system's scalability. Our approach is not only efficient but also cost-effective. Only a half-dozen render nodes are anticipated to support hundreds of active tool sessions on the nanoHUB. Moreover, this architecture and visual analytics environment provides capabilities that can serve many areas of scientific simulation and analysis beyond nanotechnology with its ability to interactively analyze and visualize multivariate scalar and vector fields.

EDUCATION: School of the Small

A slew of nanotechnology products has already hit the market, and future advances might someday give us quantum computers or allow doctors to rehabilitate cancer cells rather than kill them. Students and researchers can plug into the fast-expanding field at nanoHUB from the Network for Computational Nanotechnology, a consortium of scientists at seven U.S. universities. The site's centerpiece is a collection of simulators for exploring the physics behind nanotech. One model lets users design quantum dots, blobs of electrons that might eventually replace conventional semiconductors. The site also offers audio lectures at undergraduate and advanced levels. Visitors must complete the free registration.

Institutions for Simulations

Computational nanotechnology is a simulation science; that is, a way of producing scientific knowledge dependent upon computer simulations because, for a variety of reasons, current experimental set-ups do not answer crucial questions. The resource needs of simulation science have often been obscured by two assumptions – that simulations are a ‘cheap’ alternative to experiment and that they are closely connected to theory – though not simply synonymous with or simple extensions of theory. This paper challenges both notions by exploring the resources - human, financial, and computational – needed to perform computational nanotechnology and by showing the close coupling between empirical data and the construction of simulations. I look specifically at three U.S. computational nanotechnology sites and projects: the NASA-Ames Research Center, the Network for Computational Nanotechnology at Purdue University, and the Chemical Industry’s Roadmap for Nanostructured Materials as a protocol for nanotechnological development that specifically addresses the role of simulations.

Computational Soft Nanotechnology with Mesodyn

The simulation of microstructures on a scale 1–1000 nm is a typical problem in colloid and polymer science, and this is also the realm of modern computational “soft nanotechnology”. Accordingly, computational methods rely heavily on time-honoured approaches for calculating the thermodynamical stability of complex mixtures. We describe such approaches in the framework of MesoDyn, a general purpose software package for field-based simulations methods, such as the polymer mean-field model for microphase formation and the Poisson–Boltzmann model for electrostatic interactions. The paper concludes with a small review of examples of application: the formation of microscopic structures in block copolymer bulk solutions, block copolymer melt structures on surfaces (thin films) and structure formation in tiny polymer surfactant droplets (polymersomes). The method works quite well in all cases where a mean-field model is appropriate, but it is a challenge to extend the simulations to systems in which specific correlations are important.

Branched Carbon Nanotube Junctions Predicted by Computational Nanotechnology and Fabricated through Nanowelding

Examples of progress in fabricating branched multi-terminal multi-wall and single-wall carbon nanotube junctions as predicted by nanotechnology simulations, using template growth and nanowelding techniques, respectively, have been briefly reviewed in this report. It is argued that similar general progress in computational nanotechnology-driven fabrication of applications in other nanomaterials such as nanotubes, fullerenes, nanowires, quantum dots, DNA molecules, and nanoparticle-based systems and devices are also feasible. This is because, at nanometer length scale, system sizes have shrunk sufficiently small such that it is feasible to simulate the structural, stability, and physical and chemical characteristics with very high accuracy predictive simulations.

Computational nanotechnology with carbon nanotubes and fullerenes

The authors envision computational nanotechnology's role in developing the next generation of multifunctional materials and molecular-scale electronic and computing devices, sensors, actuators, and machines. They briefly review computational techniques and provide a few recent examples derived from computer simulations of carbon nanotube-based molecular nanotechnology. The four core areas are: molecular-scale, ultralightweight, extremely strong, functional or smart materials; molecular-scale or nanoscale electronics with possibilities for quantum computing; molecular-scale sensors or actuators; and molecular machines or motors with synthetic materials. The underlying molecular-scale building blocks in all four areas are fullerenes and carbon nanotube-based molecular materials. Only the different aspects of their physical, chemical, mechanical, and electronic properties create the many applications possible with these materials in vastly different areas.

Petaflops computing and computational nanotechnology

One of the biggest challenges in nanotechnology is the need to simulate entire molecular assemblies of up to 100 million atoms or more. This requires about one million to one billion fold more computing power than is available today. For nanotechnology to be successful, it will be essential to develop Petaflops computing systems and associated software. We will survey the various enabling technologies, such as semiconductors, superconducting, photonic, and exotic (quantum integrated circuits, molecular computers, DNA computers) that are expected to be available in the next 20 to 25 years for making Petaflops computing and nanotechnology feasible. It will be shown that progress in nanotechnology and progress in future high-performance computer systems are mutually interdependent.

Advances in molecular CAD

New molecular CAD tools are needed for the design of diamondoid nanostructures. They are needed to produce detailed designs for general discussion, to produce designs for analysis by computational chemistry packages, to allow search of the design space for promising paths to molecular manufacturing, and to generally reduce the time required to develop molecular manufacturing capabilities. When molecular manufacturing capabilities emerge, molecular CAD software will be required to control molecular manufacturing systems. Crystal Sketchpad is a molecular CAD program for the design of diamondoid molecular structures. It was begun by the author as part of the computational nanotechnology project whilst visiting at Xerox PARC. In this paper a number of fundamental improvements which have been made are presented: the ability to define and duplicate templates, the ability to create separate instances from templates, the ability to bond across instances and the ability to duplicate instances. We illustrate the use of these abilities, coupled with the previous ability to instantiate a block of diamond of arbitrary size and orientation, to define and use selection lattices and to apply a warp operator to an instance, to design a conveyor system of around 2500 atoms.