The Materials Genome Initiative, MGI, was announced in 2011 as a multi-agency initiative intended to increase the speed of advanced materials development and production. Since that time the federal government has invested more than $250 million in new research and innovation infrastructures to help achieve that goal. Assistant Professor of Industrial and Systems Engineering Hugh Medal hopes his StART project will also contribute to the goals of the MGI.
The Materials Project was announced as a key program of the MGI with the goal of providing open access to a registry of known and predicted materials. Since it’s inception, the Materials Project has amassed a database of hundreds of thousands of materials with their predicted properties, information that would normally require repeated experimentation to discover.
However, knowing the material can exist is not the same thing as successfully creating it. While the simulations contributed to the Materials Project may be able to point toward potential new materials, figuring out how best to grow those materials is left to experimentation.
“Making a material is a lot more complicated than just putting components together,” said Medal. “Think about steel. It’s not just a matter of adding different elements from the periodic table. It requires applying a lot of different processing actions in order to get the material to its final state, or phase.”
The question of how best to develop these predicted materials is a large one in material science. Medal is attempting to make inroads of this problem with his StART project. His team in collaborating with Haixuan Xu, associate professor of materials science and engineering and former Science Alliance JDRD awardee, to create a simulation to predict how to grow these materials.
“We’re working together to come up with a technique that can tell us how, given a predicted material that’s really interesting, what processing do we need to apply over time to be able to grow that material,” said Medal.
Leveraging Xu’s expertise in modeling the kinetic behavior of materials and Medal’s work with machine learning, the team hopes to develop a tool to serve as a guide for experimentalists as they work toward creating predicted materials.
“Our hope is that our tool that will simplify the process. Rather than having experimentalists sift through a large number of combinations of processing actions, we want our tool to point toward the processes that would most likely be successful,” said Medal.