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Crystallography methods improved with Big Red II

Many materials such as salts, metals, minerals, and biological molecules can form crystals. Understanding the structures and properties of crystals aids in not only a better understanding of nature, it is crucial for the development of new materials and medicines, for example. IU researchers Alberto Leonardi and David Bish, Department of Geological Sciences, have been using Big Red II to do model simulations and develop more sophisticated algorithms to enable the investigation of large collections of microstructures. Leonardi further explained, “Working on the fence, between experimental and theoretical science, my research is oriented to provide more accurate and reliable tools for the development of new applied technologies for applications spanning from business to environmental (i.e., electronics, structural engineering, sustainable energy, environment protection). As the advancement in applied technology and science relay in the accurate understanding of the fundamental behavior of materials so to exploit in the most efficient and environmentally compatible way the availability of these, my research focuses to the study of mechanisms at the atomistic scale that affects the macroscopic behavior of nanostructured materials used for chemical and mechanical applications (i.e., catalysis of chemical reactions in fuel cells, strength and fatigue of engine and vehicle components). In particular, by exploiting the power of computational techniques, my research has allowed an accurate and reliable interpretation of experimental data. The results explored limitations on the current materials characterization techniques, leading to a more comprehensive understanding/knowledge of the role played by the disorder to the directly observable materials behavior.”

Applied Crystallography

When asked about the advantages of using high performance computers such as Big Red II to do his research, Leonardi said, “Big Red II provides a very flexible computing environment where to perform highly distributed parallel simulations or solve a large collection of independent computations on CPU, GPU or hybrid CPU+GPU hardware environment. The flexible and roomy memory hardware provided by the Data Capacitor allows one to collect large amount of information suitable for post processing, avoiding annoying in advance selection of subset of output data. The easy (from in- and out-campus) and open access (without application for defined limited computing time) allow both researchers and students to develop their applications in a significantly more comfortable manner and exploiting the specific hardware architecture provided by the cluster computing resources.”