Multi-institute team awarded NSF grant to develop dynamic architected materials


Note: A version of this story was originally published by the University of California Los Angeles.

UNIVERSITY PARK, Pa. — A multi-institute collaboration including Yun Jing, associate professor of acoustics and biomedical engineering at Penn State, has received a $1.8 million grant from the National Science Foundation to design architected materials that are fine-tuned with dynamic behaviors.

The project is funded through NSF’s Designing Materials to Revolutionize and Engineer our Future program, which aims to support the accelerated deployment of advanced materials. The bulk of the funding will support contributing engineers and computer scientists at the University of California, Los Angeles, including principal investigator Xiouyu “Rayne” Zheng, associate professor of civil and environmental engineering. About 20% of the grant will support the contributions of Jing and his team at Penn State.

In architected materials, the shapes and patterns of the materials’ basic microstructures play a significant role in determining what they do and how they respond to forces. According to the research team, advances in the design of these materials could open a broad range of potential applications, such as energy and shock absorption, stretchable and flexible electronics, acoustic wave filtering and materials with more than one function.

For example, such materials could be used in a helmet that blocks out most sounds except those at specific frequencies. They could also be used throughout a vehicle to better absorb and instantly redistribute the shockwaves from a collision or a projectile, protecting the driver and passengers.

These behaviors are called “dynamic fingerprints” because the desired absorption behaviors can be encoded architected materials through the design of their topological features.

However, according to the researchers, there has been no practical way to design such materials with those dynamic properties.

To demonstrate a simpler way to create architected materials with those characteristics, the researchers will incorporate machine learning, graph network theory and high-speed 3D printing techniques. They will start out with predetermined performance metrics, then design and make custom-tailored materials using machine learning to analyze a large quantity of training data and create graphical representations of their findings.

The grant’s co-principal investigators are UCLA’s Wei Wang, Leonard Kleinrock Professor of Computer Science; Yizhou Sun, associate professor of computer science; and Mathieu Bauchy, associate professor of civil and environmental engineering.

The project will also provide unique training opportunities for the next generation of computer scientists and advanced manufacturing engineers at both UCLA and Penn State, according to the researchers.


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