New Research Aims to Predict Grain Growth



By Casey Moffitt

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The properties of all materials shift on a microstructural level as they are exposed to outside forces, whether it is a steel beam in a bridge weathered by natural elements, circuitry conducting electricity, or a river rock eroded by water. But developing a model that accurately predicts how these properties shift has been elusive.

Chun Liu, professor and chair of the Department of Applied Mathematics at Illinois Institute of Technology, has joined a team of researchers that aims to accomplish this task through a combination of mathematics and data analytics. The group has received funding from a National Science Foundation Designing Materials to Revolutionize and Engineer our Future (DMREF) grant.

The level of a given material’s functionality changes over time as its properties change. All materials are composed of a lattice structure of monocrystalline grains, with each grain fixed by boundaries. These boundaries are altered in a process called grain growth, and how that grain growth occurs affects the functionality of the material.

“Being able to predict grain growth is one of the crown jewels in materials science,” Liu says. “This will help both predict the changes in functionality of materials and create new materials.”

The challenge lies in the small scale of each grain compared to the larger scale of the properties they create. Individual grain growth is affected by almost any mechanical force, temperature changes, and natural aging. How each grain grows affects the lattice structure and the material’s overall property changes.

“Mathematical models have been successful in predicting a range of dynamic properties in materials,” Liu says, “such as we know that a steel beam in a bridge or a microchip in a computer is going to last half a century or more.”

By employing state-of-the-art mathematical modeling and analysis together with problem-specific analysis, Liu says the research team should be able to develop a computational platform that can accurately predict grain growth, especially if it can collaborate with other materials scientists.

Liu joins an interdisciplinary research team that includes Katayun Barmak, Philips Electronics Professor of Applied Physics and Applied Mathematics and Materials Science and Engineering at Columbia University; Yekaterina Epshteyn, associate professor of mathematics at the University of Utah; and Jeffrey Rickman, a professor of materials science and engineering at Lehigh University. The team was awarded a $1.8 million NSF grant from the DMREF program, with Liu receiving about $300,000, for “Microstructure by Design: Integrating Grain Growth Experiments, Data Analytics, Simulation, and Theory.”

DMREF is the NSF’s primary program that participates in the Materials Genome Initiative for Global Competitiveness, which aims to “deploy advanced materials at least twice as fast as possible today, at a fraction of the cost.”


The opinions, findings, and conclusions, or recommendations expressed are those of the researcher(s) and do not necessarily reflect the views of the National Science Foundation.

Photo: Professor of Applied Mathematics Chun Liu