91����

The world of polymers is messy. These long, string-like molecules make up many of our materials, from the plastic for our water bottles to the tissues in our bodies. But while we get resulting materials that can be stretchy, soft, or stiff, how exactly the molecular structure of these building blocks contributes to the mechanical properties we can see — and vice versa — can get lost in translation.

Assistant Professor Robert Wagner, however, recently won a that may allow researchers to finally fill in those gaps. This is the NSF’s most prestigious award, given to early-career faculty who could become pioneers as researchers and educators.

Wagner — a faculty member at the Thomas J. Watson College of Engineering and Applied Science’s Department of Mechanical Engineering — will receive $569,573 to fund research that could close the gap between what’s going on molecularly and what we can observe, incorporating machine learning with both simulated and real-world experiments to analyze the behavior of polymer chains.

“Our group is all about bridging those scales. It’s really important, because not only will it give us a better understanding of how materials work — like what physics drives their mechanical properties — but it will also help us with predictive design,” Wagner said. “If we want to design a stiffer system, for example, how do we tweak the synthesis and processing?”

Entangled polymers

There are a couple of ways in which polymers can link and form materials. They could have chemical bonds, or they could loop and wind up around one another to become physically entangled.

When entanglements outnumber these chemical cross-links, the resulting material can become much tougher, by up to three orders of magnitude. Wagner hypothesizes this is because in chemically linked polymers, the stress from a break in a single chain can ripple out to the rest of the neighboring strains that “have to pick up the slack, so to speak.”

But if a chain snaps in an entangled network, it’s so long and knotted up around so many others that it wouldn’t matter so much. In other words, it’s tougher and will take a lot more energy to break.

“When it breaks, it actually dissipates the stress across a much bigger area of the network. And because of that, the stress is not concentrated anymore, and it interrupts that crack propagation,” he said.

This kind of behavior has implications for enhancing all kinds of polymers, ranging from everyday rubbers and adhesives to cutting-edge biomedical devices and soft robotics components.

For example, entanglements may prove essential for designing effective biomimetic tissue implants. Our tissues are networked materials that need room for nutrients and waste products to move. Hydrogels – polymer networks with water in between the molecular chains – are great synthetic materials to mimic tissues because the water acts like a highway for these products. But our tissues are stiffer than you might think, and current stem-cell gels are too soft.

If these gels can’t adequately mimic the mechanical properties of our natural tissues, the stem cells will have trouble differentiating into the actual cells we need them to be.

Wagner posits that entanglements are a great design knob that’d give researchers the ability to fine-tune the stiffness, strength, and toughness of these gels. Entanglements make the network stiffer by transmitting load from chain-to-chain in more places,” he added, “but unlike chemical links, they won’t make the network more brittle.

His work will ultimately not only map how different synthesis and processing choices drive different entanglement structures, but also how and why those structures result in different, useful mechanical properties across length and time scales.

Testing and predicting entanglements

At the moment, it’s hard to study entanglements. Researchers can’t tag them because they’re just physical tangles, not distinct chemicals. Because they’re molecular features inside the bulk of materials, researchers can’t dissect or observe them even with the most advanced microscopes.

An alternative to real-world experimental studies is simulated experiments. Traditionally, this is done using molecular dynamics models, in which researchers represent polymer chains as a series of beads, connected by springs. Though these models can provide high-resolution insight into how chains physically entangle, this method is too computationally costly when considering the sheer length and time scale of networked polymer models that would be needed to make any meaningful predictions about mechanical properties.

Wagner has proposed a novel approach in his CAREER project. Instead, his research group is taking a more holistic route, using machine learning to recognize and characterize the patterns of entanglements. (Instead of each individual bead, they’re only looking at the places where those beads would tangle with another string.)

The result will be a type of graph neural network that researchers could theoretically train to rapidly predict how one entangled network will respond mechanically compared to another — shortcutting computational expenses and the need to make and test the materials physically.

Think of these graphs as a plot of your social network, in which every “entanglement” you have with another person is a node — including friends of friends, or friends of friends of friends. Machine learning could predict how your behavior will change depending on how many people comprise your immediate or more extended circle.

“That’s the idea. Polymers are networked materials, and therefore we should be able to represent them as graphs and use all the tools of machine learning developed for graphs,” Wagner said.

Wagner will be validating these fundamental experiments with physical tests on hydrogel models synthesized in-house as well.

“We want to use our model to first understand the ‘why’ and then tell engineers, including our own group, how to design materials to optimize those properties,” he said.

These new machine-learning models are just the foundation that Wagner hopes will result in a suite of resources for engineers and materials scientists of the future.

“How do we relate what is observed macroscopically in the mechanics of materials, and especially polymers, to what underlying physics is going on under the hood? The better engineers of materials science get at bridging those gaps, the better we’ll be able to predictively design new materials,” he said. “Inversely, it’ll help us understand the basic science. When we observe something macroscopically, what is the most likely cause?”

Educating future engineers

Beyond the lab, Wagner will also be taking his research to K-12 classrooms, from hands-on demonstrations making entangled networks using toys to visualizing machine learning through graphs. He will also share what he learns through his project with his students at the University.

A major emphasis in Wagner’s education plans, however, will also be on a particularly underserved population: incarcerated students.

Bringing STEM into correctional facilities can be challenging for a plethora of reasons, according to Wagner. Hands-on demonstrations aren’t as feasible, and students have differing educational backgrounds.

“But then the question becomes, as a STEM educator, how do you give them the background they need? How do you show them so that they understand the math and physics without just telling them?” he said.

That’s where Wagner will turn to the computer lab instead. By partnering with the company behind MATLAB, Wagner and his team will work on installing the program into the computer labs, starting with the Cayuga Correctional Facility.

“Instead of giving them the hands-on demonstrations that we can, very fortunately, take to K-12 environments, we’re going to do the same thing, but on a computer,” he said. “I’m really excited about that. I don’t think there are too many initiatives to bring STEM into these programs.”

Wagner considers himself a lifelong learner as well, having gone back to graduate school precisely because he fell down the rabbit hole of materials science.

“My uncle always says, ‘You’re either a student or a teacher, and you just have to know which one.’ But as a college professor and researcher, you get to be both at once,” Wagner said. “Every time I teach something, I feel like I understand it better. I’m not only an educator. I’m also learning while I do it.”

In pivoting to machine learning and tackling a rather elusive engineering problem, Wagner’s CAREER proposal is just another step in learning, both for him and his broader research group at 91����.

He added that it wasn’t a lone effort, with his students and fellow faculty’s support, as well as programs like the Office of Strategic Research Initiatives’ Commit to Submit program that helped him land his grant on his first try.

“Now we have to do the work,” he said. “To me, this is very much the first stepping stone to what will hopefully be a very productive body of work.”