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Chemical Sciences and Engineering Division |
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A Catalyst Quest
Argonne's petascale computing capability will accelerate nanoscale experimental research. Argonne's advanced computing facilities have already provided materials scientists with a map to more efficient catalysts. These catalysts support the reactions involved in manufacturing, petrochemical processing and pollution abatement. In an office at Argonne's one-year-old Center for Nanoscale Materials, materials scientist Jeff Greeley is working to find a new chemical catalyst that can transform propylene into propylene oxide, an organic molecule needed for the production of many plastics. Greeley, along with materials scientists Larry Curtiss and Peter Zapol of Argonne's Materials Science and Chemical Sciences and Engineering divisions, compiles a list of suspects and enough physical evidence to run through some of the fastest computers in the country. The way that Greeley describes the investigation sounds almost as much like forensics as pure chemistry. “We use a few chemical parameters as a kind of ‘fingerprint' that gives clues as to how catalysts actually work,” Greeley said. “Our goal is to use advanced computing to run these ‘fingerprints,' so that we can help experimenters to more closely focus their synthesis and testing efforts on the most likely candidates.” Greeley's search for a new catalyst is a prime example of how basic scientific research plays a critical role in ensuring long-term economic growth. From plastics manufacturing to petrochemical processing to environmental cleanup and pollution abatement, catalysts play an essential role in many aspects of our everyday lives. According to R&D magazine, approximately 90 percent of commercially produced chemical products involve the use of catalysts at some point during their manufacture. Catalysts also form the core of many of the most promising future technologies, such as hydrogen fuel cells. Greeley's research is funded by the Basic Energy Sciences program in the U.S. Department of Energy's Office of Science to expand the scientific foundations for new and improved energy technologies. Most commercial catalysts consist of a transition metal bonded to a non-metal base. They work by reducing the amount of energy needed to initiate a chemical reaction. The best catalysts lower this quantity—known as a reaction's activation energy—dramatically, providing a shortcut from reactants to products that saves money and conserves energy. A catalyst's function also depends on its binding energy, a quantity that describes how strongly reactants attach to it. By harnessing the combined power of Argonne's high-performance computing resources, including the Jazz cluster at the Laboratory Computing Resource Center (LCRC), the IBM Blue Gene ® /P supercomputer at the Argonne Leadership Computing Facility and the new Nanoscience Computing Facility (NCF), Greeley and his colleagues attempt to predict which catalysts will have the lowest activation energies and the optimal binding energies. These two principal quantities are relatively easy to estimate, but ensuring that they produce accurate representations of actual catalysts requires a significant amount of computing power, Greeley said. “We need our models to have predictive power; that is, we want to be able to evaluate one set of parameters for each catalyst candidate and make meaningful predictions about how fast a particular reaction will proceed, an environmental contaminant will be cleaned up or a particular fuel cell reaction will be accelerated. Then we'd like to go through and evaluate that parameter on hundreds or thousands of different alloys.” To investigate the catalytic properties of a variety of materials, Curtiss and Zapol have used the Jazz cluster to more closely examine catalysts' fundamental atomic structures as well as the mechanisms of the reactions they facilitate. Many catalysts, including those used in fuel cells, consist of a metal nanoparticle that sits on a larger non-metal support—fuel cell electrocatalysts typically use platinum active sites on carbon-based structures. The particular geometric and electronic configuration of the catalyst on a support greatly influences its efficiency. As a result, finding the optimal atomic arrangement is as important as determining the correct component materials. In the case of propylene, for example, Argonne researchers found that small clusters of silver atoms on an aluminum oxide support helped to add oxygen to propylene's carbon-carbon double bond, converting it to propylene oxide. Later hands-on experimental research has validated that result. “For a computational materials scientist,” Greeley said, “that's the dream: to be able to make a prediction about a material that has improved properties, and then to actually test it in the lab and to find out that it works. But it takes a lot of computer power to go through all the different candidates.” The opening of the 10-teraflop NCF earlier this year has provided Argonne's scientists with enough computing power to do many more of these atomic-scale calculations than they could previously. “We used to run these calculations on smaller systems, where we'd have to represent catalysts with a smaller number of atoms,” Curtiss said. “But now we can include many more. In addition, in the past many of the reactions in which we were interested were just too complex, but now we have an opportunity to study many more reactions in more detail.” Argonne's materials scientists refer to these combined approaches as “computational screening,” a process somewhat like panning for gold. The computer models act as a sieve, allowing scientists to eliminate the vast majority of possible alloys or configurations from consideration without having to perform expensive and time-consuming tests on them in the laboratory. “Our goal,” Greeley said, “is to shorten the list of likely candidates so that we can give guidance to our experimentalist colleagues to help them focus their synthesis and testing efforts more closely on materials that we think have a good chance of success. Just in terms of manpower and computational expense, it would be quite costly to go through all those 750 materials and test them in a lab from the start.” Zapol agreed: “You can try different combinations much more rapidly on the computer than in the lab.” Typically, however, catalyst “gold” isn't gold at all, but rather a metal cluster, metal oxide or other composite. While precious metals like gold or silver do compose part of many of the most active catalysts, the costs of using them on a large scale provide another incentive for researchers to use computational screening to identify cheaper, but equally effective, alternatives. In the course of his more than 30 years at Argonne, Curtiss has become widely known for his development of new computational techniques, including the formulation of highly accurate quantum chemical methods for the calculation of binding and activation energies, the two most significant parameters that scientists must consider when assessing and modeling the catalytic properties of different materials. “The availability of faster computers and new methodologies provides materials scientists with an unprecedented opportunity for designing materials with improved properties,” Curtiss said. Although Argonne's scientists do most of their computational screening of catalysts on the Argonne NCF and LCRC computer facilities, Argonne's recent acquisition of the IBM Blue Gene/P supercomputer promises to facilitate additional breakthroughs in computational catalyst modeling. Argonne Now, Vol. 03 Issue 01, Spring 08 |
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