Form follows function in materials design

When it comes to finding the right materials, ASU researchers are turning the typical trial-and-error process on its head

July 17, 2017

Suppose you want to build something — a skyscraper or a spacecraft, a computer or a car, a water bottle or a wristwatch.

You’ll need materials to make that product, and you’ll need to choose the best materials for the job. For instance, you’ll want lightweight materials for a spacecraft so that you can afford to propel it out of Earth’s gravity. But if it will come back to Earth, it also needs to withstand the heat of re-entry.  Form follows function in materials design How do you find the right material to build the best product? ASU engineers are turning the typical trial-and-error process on its head. Download Full Image

How do you find the right material to suit your needs? Generally, it takes a lot of trial and error. You make a material and you test it in a lab. Crush it. Cook it. Stretch it. Chill it. Then do it over and over and over again. 

It is a time-consuming and expensive process. And although you’ll probably find a material that suits your purpose, it might not be the best possible material for the job.

That’s why a group of researchers at Arizona State University are working to flip the process on its head. Instead of making a material and then finding out what it can do, they are starting from the end — deciding what properties a material should have and then designing the material to have those traits. These could range from strength to flexibility to electrical conductivity and more.

A need for structure

“We already have performance in mind. So we need to ask ourselves, what are the things you can change, what can you play with to give you this performance? The thing that we play with is the material microstructure,” said Yang Jiao, an assistant professor in ASU’s School for Engineering of Matter, Transport and Energy (SEMTE).   

For example, if you look at a piece of metal, it appears to be one uniform material. But most metals are alloys made from two or more elements, such as copper and aluminum.

“You melt them together, stir and cool again. As you cool it, the distribution of the components will be different in different parts of the metal,” Jiao said. 

At high temperatures, copper and aluminum mix together evenly, but as they cool the copper forms clusters. The differences are not visible to the naked eye, but they can be seen under a microscope. This structure, with its clusters of copper, affects the properties of the material. 

Think about building a house out of bricks. If you just stack bricks on top of each other in side-by-side columns, your house will not be very strong. But if you stagger the bricks, the structure will be much stronger.

Brick walls are stronger with stagger bricks compared to columns of bricks
A metal alloy that forms clumps of one of its elements after heating and cooling is like a wall made of bricks stacked directly over one another, in columns (left). This results in a wall that is structurally weaker than one in which the bricks are staggered.

It’s easy to see how staggered bricks make a stronger wall. It’s much harder to figure out how the structure of a titanium alloy helps it withstand repeated stresses without breaking — also known as fatigue strength. This is the challenge that Jiao is confronting with his colleagues in SEMTE, Yi “Max” Ren and Yongming Liu. 

“You have a target property — I want a fracture strength as high as possible. Then what kind of microstructure will achieve that? Instead of evaluating existing materials, I want to find the structure that will match the property,” said Ren, an assistant professor who studies design optimization.

Making a connection

Scientists can figure out the microstructure of a material that already exists. Jiao develops computer simulations that can analyze that microstructure and determine what properties it will have. The simulations are based on data from experimental testing.

What he can’t do, yet, is go in reverse. The computer simulation can say, “This structure will give you this property, and that structure will give you that property.” But it doesn’t explain why. So the researchers don’t know what specific characteristics of a structure cause it to have the properties it does. 

This is where Ren’s expertise comes in. He is developing software to help the computer identify which features of a microstructure are important to specific properties. He is drawing on deep learning technologies, a branch of artificial intelligence in which software learns to recognize patterns. This is how Facebook recognizes your friends in a photograph and how Amazon makes personalized recommendations.

“We want the machine to learn by itself what structural features influence a material property,” he said.

The computer will analyze images of different microstructures pixel by pixel. Pulling data from Jiao’s simulations, the software will look for patterns in the images that correlate with specific properties.

“The machine sees several images and tries to make puzzle pieces of these images. You want to come up with a small set of pieces that can be used to reconstruct all of these images. Then you are free to recombine the pieces to get new images,” he said.

alloy images
Across the top, images of original samples of titanium alloy, lead-tin alloy, Fontainebleau sandstone and spherical colloids (from left to right). The computer learned from these samples and created the reconstructions shown across the bottom. Image by Ruijin Cang, Yang Jiao and Yongming Liu

The team is also working to reduce the number of pixels the computer needs to analyze. This is similar to compressing a high-resolution photo as a JPG file. Photo compression removes pixels to reduce the file size and the computing power needed to work with it, while still leaving a recognizable image. The ASU researchers want to reduce the computer’s workload in analyzing microstructures. But they don’t want to lose key information about which structures create useful properties. 

Ultimately, the process will make materials development less costly and time-consuming, potentially reducing the development cycle from about 10 years to about two.

“Another benefit is that when we do this type of study, we generate a huge database which is shared among the community. Anyone can access these,” Jiao said. 

Putting materials to the test

Once the team has identified the best structure, they’ll need to produce a sample of the material and test it out in the lab. In this case, that means pulling and releasing the material over and over to find out how quickly it gets fatigued.

Material fatigue is what caused the fuselage of a Southwest Airlines jet to rupture mid-flight in 2011, ripping a 5-foot gash in the upper cabin. (Fortunately, the pilot was able to make an emergency landing without any fatalities.)

Every time an airplane takes off, the cabin inside is pressurized so that passengers can survive at high altitude. This puts force on the body of the aircraft. With every landing, the cabin is depressurized, removing that force. After thousands of these flight cycles, the metal of the fuselage gives out and begins to develop cracks, which are often microscopic and require special tools to detect.

“Aircraft is safe, much safer than before,” noteed Liu, a professor in SEMTE. “But still fatigue is inevitable and a safety concern. We want it to be even safer. People still don’t really understand the very fundamental aspect of fatigue. The variability is huge. This could fail in one year or 100 years. People don’t really know why. One of the primary reasons comes from the microstructure.”

Liu is an expert in mechanical structures who works with a variety of tools at ASU to test and image materials. For example, ASU has a palm-size fatigue testing machine that can fit into a scanning electron microscope.

“If you don’t know what happens, you go in and see it,” he explained.

One major challenge the team faces is that it may not be possible to reproduce the ideal microstructure using current manufacturing techniques.

“It’s probably not possible at this stage to reproduce the design exactly,” Jiao said. “We can produce something similar and do the testing. We need to come up with a microstructure with a certain tolerance of uncertainty.”

Transformative ideas

Although computational materials design is a fairly new field, it’s a hot topic nationwide, spurred by the federal Materials Genome Initiative. The multi-agency initiative was launched in 2011 to support institutions in discovering, manufacturing and deploying the advanced materials that are essential to economic security and human well-being.

ASU’s work is supported through a National Science Foundation EAGER grant, which funds early-stage work on potentially transformative ideas.

“There is a growing interest in this area because people realize it will change how materials will be developed. It saves a lot of time and money. It will also bring a lot of benefit to fundamental physics and mathematics,” Liu said.

“We are lucky that we have complementary expertise,” he added. “We’re a strong team. I’m a computational person, so I know it’s extremely important for me to work with experimentalists. We have a very nice characterization group, testing group. And the microscopy facility here is world-class.”

Liu notes that ASU provides an excellent environment for collaboration, not just within a school but across disciplines. In fact, he recently won a $10 million grant from NASA to lead a multidisciplinary team in studying how to integrate the complex data sources that are driving the future of air traffic management systems.

“We’re addressing very large problems for complex systems. The problem is not too little data, it’s too much. How do you fuse this data to get what you want? You need to go beyond, working with very different schools, including human factors,” he explained. 

He added: “At ASU I don’t need to go outside. I get whatever I need from ASU.” 


Learn more about this research at: The School for Engineering of Matter, Transport and Energy is a unit of the Ira A. Fulton Schools of Engineering. This research is funded through the National Science Foundation EAGER award No. 1651147.


Director, Knowledge Enterprise Development


ASU's Cronkite School receives challenge grant for innovative virtual-reality project

July 17, 2017

Arizona State University’s Walter Cronkite School of Journalism and Mass Communication has received a grant to fund a new virtual-reality project through a news initiative from Google News Lab, the John S. and James L. Knight Foundation and the Online News Association.

Journalism 360 awarded the Cronkite School a $30,000 grant for “Data Real,” in which students in the New Media Innovation and Entrepreneurship Lab will develop a tool that easily enables journalists and content creators to add location-based data visualizations to virtual-reality content. Cronkite School building ASU's Cronkite School has received a grant to fund a virtual-reality project that would allow journalists to add statistics, data, pricing and other information on particular neighborhoods through data overlays on VR footage. Download Full Image

The Data Real tool would allow journalists to add statistics, data, pricing and other information on particular neighborhoods through data overlays on VR footage. Users would search neighborhoods by entering a ZIP code.

“This grant will help our students push the limits of storytelling through cutting-edge technologies,” said Kristin Gilger, Cronkite School senior associate dean. “We sincerely appreciate the support of Google News Lab, Knight Foundation and the Online News Association.”

The Cronkite School’s project was one of 11 challenge winners in the contest. The winning projects will help advance Journalism 360’s mission of developing an international network of journalists to explore and share knowledge about their work in immersive storytelling. This is the third time the Cronkite School has received a challenge grant with Knight Foundation support.

Cronkite’s New Media Innovation and Entrepreneurship Lab, which connects journalism, computer engineering, design and business students at ASU to create pioneering media products, will work on the Data Real project over the next several months. They are invited to the ONA conference in Washington, D.C., in October. They also will share their findings at a special Journalism 360 demo day in early 2018.

Retha Hill, director of the New Media Innovation and Entrepreneurship Lab, said the Data Real tool will enhance VR experiences by allowing users to more deeply interact with the content. Users could explore a neighborhood by wearing a VR headset and interact with data around them by clicking on 3-D visualizations with a controller that reveals information such as crime statistics, school data, dining information and more.

“The data visualization tool will help storytellers bring localized data alive,” Hill said. “I can’t wait to see what my colleagues in journalism will do with the tool once it is available. My students in the lab can’t wait to get started.”

The New Media Innovation and Entrepreneurship Lab was conceived by ASU President Michael M. Crow and the late Sue Clark-Johnson, who headed Gannett’s newspaper division. Over the past 10 years, students in the lab have created mobile and VR apps, interactive games and social-media sites for a variety of media companies and nonprofits.

Announced in September 2016, Journalism 360 was designed to help accelerate the use of immersive storytelling in the news through innovative technologies such as VR, augmented and mixed reality, and 360-degree video.

Other Journalism 360 projects ranged from the use of augmented reality and data visualization to document the building of a border wall between the United States and Mexico to a tool that allows users to create virtual-reality photo experiences from their smartphones. Grant recipients included media organizations, such as The Arizona Republic and NPR, as well as universities and multimedia companies.

“The overwhelming response to the open call demonstrated that journalists are seizing the opportunity to use immersive storytelling to engage people in new ways,” said Jennifer Preston, Knight Foundation vice president for journalism. “There is still much to learn, and the winners will help lead the way by identifying best practices and tools and expanding the Journalism 360 network.”

Communications manager, Walter Cronkite School of Journalism and Mass Communication