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Modeling magnetic materials

This section shows the final configuration of magnetic moments in five layers at the interface between iron-manganese (red and purple balls with yellow arrows showing magnetic moments) and cobalt (blue balls with yellow arrows.) The simulation of 2016 atoms had 15 iron-manganese and 6 cobalt layers.

For many years, information technology has been marked by an exponential growth in the ability to store and retrieve data—so much so that the industry is reaching its limits on further improvement in data storage and retrieval with the materials at hand. Developing new materials requires research at the molecular and atomic level to understand their properties. Such studies require calculations involving even the complexities of quantum physics. Somewhat ironically, the ability to advance this type of research has, until recently, required computational power that has been constrained by the very limits the research is trying to overcome.

Yet the demand to handle more data faster keeps growing—for instance, stuffing more data into smaller devices, such as digital cameras, and retrieving that information faster. Meeting this demand requires creating new materials when the demands on current materials are more than they can effectively meet. These are tough materials sciences problems whose complex solutions will depend on calculations made using the latest and most powerful supercomputers.

ORNL’s Malcolm Stocks, Thomas Schulthess, Xiaoguang Zhang, Don Nicholson, Bill Shelton, and the University of Tennessee’s Balazs Ujfalussy, along with other collaborators, are performing complex materials science calculations using ORNL’s growing power in supercomputing. “We have been developing computational methods to model magnetic states of various materials and to try to unravel the intricacies of magnetism,” says Stocks.

Stocks, Shelton, Schulthess, and Ujfalussy have been exploring the role of antiferromagnetic materials in contact with ferromagnetic materials in magnetic multilayer storage and read-head devices, or, simply put, the tiny yet complex components that make a computer’s hard drive tick. Data are stored, in binary fashion, through multitudes of yes/no or on/off commands captured in the magnetic orientation of small regions of magnetism, which are, in turn, made up of the magnetism of thousands of atoms. Individual atomic “magnetic moments” result from an imbalance between the number of spin “up” and spin “down” electrons associated with the atomic site. If the atomic magnetic moments all point in the same direction, that material is ferromagnetic. (“Ferro” connotes iron, the most common magnetic element.) It will stick to a refrigerator. It will make an electric motor run. Or it can be used to store data.

In antiferromagnetic materials, the individual atomic magnetic moments are oriented in the materials’ structure in such a way that the total magnetic moment is zero; for example, the magnetic moments associated with individual atoms in a crystal can point “up” and “down” in a regular manner, with as many “up” moments as “down” moments.

The read head in a computer disk drive is a multilayered component, known as a spin valve (because it functions similarly to the way a mechanical valve might turn water on or off). A spin-valve device consists of a ferromagnetic material, a nonmagnetic layer, and another ferromagnetic material that is held constant by an adjoining layer of antiferromagnetic material. Through an effect called “exchange bias” the magnetic orientation of the ferromagnetic layer closest to the antiferromagnetic layer is held constant, or “pinned,” enabling the other, outermost ferromagnetic layer to “switch” from one orientation to the other, depending on the bit of information that is stored on the disk. When this process cycles several million times, the word-processing document, spreadsheet, or digital photo stored a few days ago appears on a computer screen.

To develop lighter, smaller devices for faster access to and storage of more data, new and better materials are needed. Stocks, Schulthess, and colleagues at ORNL and across the country have been exploring the incredibly complex interactions at the atomic levels that can produce insights into the development of new, advanced materials for the next generation of information technology.

Using computer simulations performed at ORNL, Stocks, Schulthess, Shelton, and Ujfalussy have recently explored magnetism in iron manganese (FeMn), an alloy used as the anitferromagnet in spin-valve devices. FeMn is an antiferromagnetic material used to fix magnetic moments in those pinned layers of ferromagnetic material, to allow detection of differences in electrical resistance (between the pinned and moving layers) that represent 1’s and 0’s sensed on magnetic media. In addition, they are studying the magnetic structure of interfaces between FeMn—an antiferromagnet—and cobalt (Co)—a ferromagnet—to shed light on the mechanisms responsible for exchange bias.

In a collaboration with the Department of Energy’s National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory, the Pittsburgh Supercomputer Center (PSC), and DOE’s Center for Computational Sciences (CCS) at ORNL, the ORNL team has run simulations of FeMn and FeMn-Co interfaces at unprecedented computational speeds. Calculations involving 2016 atoms ran at a computational speed of 2.26 teraflops (2.26 trillion additions, subtractions, multiplications, and divisions per second). The calculations involved 126 sixteen-processor nodes on the NERSC IBM SP3 supercomputer. A subsequent job involving 2176 atoms ran at 2.46 teraflops on 136 nodes; this run was done at about 75% efficiency, a figure much higher than has been seen in typical large-scale production codes.

What have these computational experiments shown? First, the FeMn alloy has a different magnetic structure than previously thought. Second, at interfaces between ferromagnetic Co and antiferromagnetic FeMn, the magnetic structure of the antiferromagnetic material changes radically from that associated with bulk FeMn.

Stocks explains that, previously, the magnetic structure of FeMn was thought to be one of three states of antiferromagnetic ordering, called 1Q, 2Q, and 3Q. This system was studied experimentally by various techniques, including neutron scattering, but experiments were unable to unambiguously distinguish among these three possible magnetic structures. The 3Q structure was thought to have the lowest known energy and, therefore, to be the most stable, when compared with the 1Q and 2Q structures. However, Bill Shelton found, through hours of high-performance computer modeling using the 176-node IBM RS/6000 SP machine at ORNL, that a magnetic configuration exists that is even more stable than the 3Q structure.

“We did calculations using constrained density functional theory and spin dynamics,” Stocks says. “Our goal was to understand the alloy’s noncollinear, antiferromagnetic 3Q magnetic structure. We predicted there is a relaxed, even lower-energy, and therefore more stable, magnetic state that we call 3QR, in which R stands for relaxed. According to our simulation, in this state, the magnetic moment orientations are pointed in slightly different directions from those of the 3Q structure. We thought that this insight could be an important ingredient in the explanation of exchange bias.”

Schulthess explains that with read heads, for instance, issues of practicality stand in the way of improving their performance and further miniaturizing them. Improvements in the spin-valve function could be obtained by creating a larger magnetic field to fix the magnetic moments of the ferromagnetic layer. But exposing components to such a magnetic field would likely also wreck the disk and the data stored on it. The other solution is to increase the amount of antiferromagnetic material to enhance “the magic hand that holds”—or pins—the magnetic moments of the ferromagnetic material. However, doing so with currently available materials would require disproportionately more antiferromagnetic material, thwarting any further attempts at miniaturization.

The ORNL team believes the key to making an improved, miniaturized spin-valve device could lie in changes in the magnetic moments of the antiferromagnetic and ferromagnetic interfaces, which surfaced in the computer models. “Details of the magnetic structure appear to be very rich and could not have been predicted without the full machinery of first-principles spin dynamics and the use of massively parallel computing resources,” Stocks says. “Although the origins of exchange bias are currently not fully understood, it is known that the magnetic structure of the ferromagnetic-antiferromagnetic interface is a key ingredient. The ORNL group’s prediction of disorder in the magnetic moment orientation and the large changes that occur when the antiferromagnet is proximate to a ferromagnet have a number of interesting implications for the mechanism controlling exchange bias.”

“It’s all in how the magnetic moments arrange themselves,” says Schulthess.

Stocks, Shelton, Schulthess, and Ujfalussy performed their calculations using a program called the Locally Self-Consistent Multiple Scattering (LSMS) code, which was developed at ORNL on the Laboratory’s first supercomputer, the Intel Paragon. The 45,000-line LSMS code continues to be developed and extended to treat larger and more complex systems. Currently, the code is modeling magnetic materials on ORNL’s latest supercomputers, including the Lab’s new IBM Power4 machine at CCS. The researchers have run their models on the most powerful computers they can line up, including supercomputers at NERSC and PSC. Supercomputing high points came when they won the prestigious Gordon Bell Prize in 1998 and were the first to run a real application code at a speed of greater than 1 teraflop. Most recently, the code ran at a staggering 4.46 teraflops on a Compaq supercomputer at PSC.

Advances in supercomputing and advances in materials technology could develop into a symbiotic and self-perpetuating relationship. Strides in one area may very well lead to strides in the other. Supercomputers are helping materials researchers understand what’s happening with individual atoms, as well as the incredibly complex relationships that exist between those atoms and at the interfaces of the materials they compose. As both the researcher and the casual Web surfer can already attest, the sky may be the limit for what research into new materials for computers can produce and the speed and amount of information the resulting computers can process.—Bill Cabage, editor of the ORNL Reporter, an employee newsletter.



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