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Purdue Research Center Focuses on Computational Materials Development

Posted on: 10/17/2013
Purdue University has launched the Purdue Center for Predictive Materials and Devices (c-PRIMED), led by Gerhard Klimeck, professor of electrical and computer engineering, and Alejandro Strachan, professor of materials engineering and TMS member. The center’s focus on modeling for materials engineering dovetails with the U.S. Materials Genome Initiative (MGI), announced by President Barack Obama in 2011. The MGI concentrates on developing methods to double the speed and halve the cost of creating new, advanced materials.

c-PRIMED is part of $12 million in new investment by Purdue's research infrastructure. Its efforts will encompass ways to accelerate the time it takes to introduce advanced materials to the marketplace for everything from airplane wings, solar cells, and electronic devices to packaging that keeps food fresher.

Strachan is a leader in developing and validating computational methods for predicting the behavior of materials and their application in technology. "Today I require a hundred experiments to learn something important about a material," he said. "The question is: Can I do 10 experiments, 10 very well designed experiments instead of a hundred? That requires powerful computers, new simulation capabilities and a framework for decision-making that combines simulations with experiments."

This approach hinges on a fundamental change in how products are created by introducing a procedure known as material-product co-design.

"Now, when we develop a product, the materials are fixed," Strachan continued. "You take material A, material B and material C. I know the properties and I am going to pick from those. However, in material-product co-design, at the same time I am developing the airplane and the shape of the wings, I am also changing the material. Instead of saying polymer 1, polymer 2, polymer 3, I'm optimizing everything at the same time."

Co-design requires the ability to "quantify the uncertainties" in a material's simulation.

"Knowing the uncertainties allows you to predict not just the mean behavior of a material but its across-the-board performance with enough confidence to make decisions about which designs to concentrate on," Strachan said. "You want to be able to make decisions based on these predictions, and that means you have to be able to guarantee the prediction."

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