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Exploring and modeling 21st Century materials

Computational materials scientists at ORNL are using the high-performance computing infrastructure at CCS to explore superconductivity and magnetic nanostructures

Using a CCS supercomputer, researchers calculated the magnetic structure of a quantum corral nanostructure, which consists of magnetic iron atoms deposited on a copper surface that "corral" copper electrons.

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The 1986 discovery of high-temperature superconductivity sparked the quest for room-temperature superconductors that could transmit electrical current without heat losses and without the need for an expensive coolant such as liquid helium. Room-temperature superconductors could make possible ultra-efficient power transmission lines, practical electric cars, and superconducting magnets that could bring high-speed levitated trains and smaller, more efficient, and less costly rotating machinery, appliances, particle accelerators, electric generators, and medical imaging devices.

High-temperature superconductors are being used commercially. A few urban utility companies have tripled their capacity to carry power simply by replacing existing underground cables with liquid-nitrogen-cooled superconducting cables. Cellular telephone towers have extended their reception range and call-handling ability with superconducting signal filters.

Understanding Superconductivity

No one understands why certain copper-oxide materials exhibit high-tem-perature superconductivity, says ORNL Corporate Fellow Malcolm Stocks, co-leader of the Materials Research Institute (MRI) at DOE's Center for Computational Sciences at ORNL. Recently, MRI co-leader Thomas Schulthess has been collaborating with Thomas Maier, Eugene P. Wigner Fellow at CCS, and solid-state physicist Mark Jarrell of the University of Cincinnati.

Thomas Maier, recently named a Eugene P. Wigner Fellow at ORNL, used the Cray X1 supercomputer at CCS to confirm his suspicion that a widely used computer model was insufficient to describe high-temperature superconductivity.

Thomas Maier, recently named a Eugene P. Wigner Fellow at ORNL, used the Cray X1 supercomputer at CCS to confirm his suspicion that a widely used computer model was insufficient to describe high-temperature superconductivity.

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When Maier was a postdoctoral fellow at Cincinnati he, Jarrell, and other colleagues were working on a theoretical understanding of high-temperature superconductivity. Jarrell holds that a microscopic understanding of why the current layered oxide (high-Tc) materials are superconducting will lead to the design and synthesis of new room-temperature superconductors.

Jarrell is a co-developer of a new theoretical approach, called the Dynamical Cluster Approximation (DCA). Here, a cluster of atoms, within which the complex quantum mechanical interaction between electrons are treated essentially exactly, is embedded into an effective medium that accounts for the effects of the rest of the material in a computationally feasible way without compromising the mathematical rigor of the theory.

Using a massively parallel implementation on the supercomputers at CCS and at Pittsburgh Supercomputing Center, Jarrell and Maier studied the two-dimen-sional (2-D) Hubbard model. This model has been widely accepted as the theoretical framework for capturing the physics underlying high-T c superconductivity. Because structurally the high-T c materials are a series of copper-oxide planes, with no apparent interactions between them, researchers believed they could be modeled as 2-D systems.

However, based on their work, Jarrell and Maier began to suspect that the 2-D Hubbard model may not provide a complete description of high-T C superconductivity. When Maier came to ORNL as a Wigner Fellow, he and his CCS colleagues were able to pursue this suspicion using the power of the newly installed Cray X1, a machine ideally suited to performing the complex DCA calculations. The most recent calculations are confirming Maier's suspicions that the strictly 2-D Hubbard model is insufficient, a conclusion that runs counter to most conventional wisdom. If true it will be necessary to improve the underlying model by, for example, introducing some 3-D features such as coupling between the 2-D copper oxide planes.

The CCS team was able to carry out this work because the Cray X1 makes the DCA calculations up to 35 times faster than the computers used previously. This improved performance allows the modeling of larger DCA clusters containing up to 64 (8 x 8) sites, rather than only 4 (2 x 2) sites previously possible. According to Schulthess, if the Cray X1 had 4 to 10 times its current capability, the ORNL/Cin-cinnati collaboration would likely have a model that describes the physics of high-temperature superconductors, which eventually will enable computational design and prediction of new materials that are superconducting at room temperature.

Nanomagnetic Modeling

The revolution in magnetic storage that has occurred over the past 40 years has always depended on the timely introduction of new materials that allow researchers to cram increasing amounts of digital data into smaller and smaller regions. In the very near future, whole new classes of materials will be needed if the magnetic storage industry is to maintain its current rate of increase in storage density for computers and digital media. Increasingly, basic research is turning to nanoscience for solutions. Nanoscience covers materials whose size is a few thousand to a few million atoms. At the nanoscale, materials frequently exhibit new and unexpected phenomena. Understanding and harnessing these "emergent" phenomena could have untold future benefits to science and technology.

The computed molecular structure of a nanoscale assembly of six polymer nanoparticles.

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For some time Stocks, Schulthess, and others at CCS have been modeling magnetism and electrical transport in magnetic materials as part of the effort to develop new and improved magnetic materials. Now, the group has also turned to studying the new frontier of the properties of magnetic nanostructures. Recently, using the IBM supercomputers at CCS, the group performed the first fully self-consistent ab initio calculations of the magnetic structure of scientifically fascinating nanostructures called quantum corrals.

A quantum corral is a magnetic nanostructure in which magnetic atoms, are positioned in the shape of a stadium on a copper surface. In the mid-1990s Don Eigler of IBM used a scanning tunneling microscope (STM) not only to make rings of magnetic atoms but also to measure the electron density, the concentration of the copper surface's electrons that are corralled by the magnetic atoms--hence, the name "quantum corral." On a (111)-facet of copper, some of the electrons form an effective two-dimensional electron gas just above the surface. When the magnetic atoms are deposited, the 2-D electron gas is perturbed, causing standing waves that are captured by STM images.

The CCS researchers have not only calculated the oscillations in the surface charge obtained experimentally but have also predicted oscillations in the magnetic density inside the corral as well as the state of the magnetic atoms themselves. These predictions present a challenge to next-genera-tion STMs that will measure the magnetic structure of nanostructures. This achievement was made possible by use of codes developed by the University of Tennessee's Balazs Ujfalussy and his colleagues in Budapest and Vienna.

Materials Institute

The rapid pace at which new complicated materials must be introduced into technology, as well as the drive to miniaturize devices, increasingly dictates a more systematic approach to design and fabrication that is built on a firm theoretical underpinning. Here computation can be expected to play a central role. Because simulation can study features close to the atomic limit that are difficult to access experimentally, computational materials science can accelerate the development of new materials.

To facilitate these goals, the Materials Research Institute has been created within CCS. MRI's missions are to build and maintain a community of leading computational materials scientists and to involve them in the development of computational methods, algorithms, and simulation software, as well as in the early evaluation of new computer hardware at CCS. MRI's long-term goal is to enable the design of advanced materials by using leadership-class computing envisioned by DOE.



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