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The $8.6-million Center for Autonomous Materials Design uses advanced data-mining techniques to accelerate discovery and development of theoretical new materials, sharing new knowledge through an open-access repository at aflowlib.org
In the search for new materials, researchers are abandoning hunches and intuition for theoretical models and pure computing power. The research is part of the White House Materials Genome Initiative, launched in 2011 to accelerate the pace of discovery and deployment of advanced material systems crucial to achieving global competitiveness in the 21st century.
At Duke, this effort takes the shape of the AFLOW Library built and maintained by Stefano Curtarolo, director of the Center for Autonomous Materials Design (previously the Center for Materials Genomics), which is funded by an $8.6 million grant from the Department of Defense’s Multidisciplinary University Research Initiative (MURI) program. The library contains experimental data of known binary and tertiary compounds and allows users to predict properties of theoretical new materials by building models of similar compounds atom-by-atom.
“Physically going through potential combinations would take tens of thousands of hours,” said Curtarolo. By employing data analytics, “We help identify targets for new compounds much faster and more cheaply.”
For example, Curtarolo used the approach to identify several dozen theoretical materials that could potentially replace the expensive and rare platinum found in applications such as catalytic converters and cancer therapies. He also recently discovered a way to predict which alloys will form metallic glasses found in electrical applications, nuclear reactor engineering, medical industries, structural reinforcement and razor blades.
“Experimentalists are constantly using our data to guide them in new experiments,” said Curtarolo. “We’re planning to develop a common format so that we can compare our theoretical calculations with actual experimental data.”