Researchers at TU Delft University in the Netherlands have created a new compressible material using artificial intelligence.

Thanks to the AI, the team was able to come up with the discovery without a single experimental test in the lab.

While it's not the first time that AI has helped materials scientists, this leap shows just how powerful artificial intelligence can be in research.

Perhaps the science textbooks of the future will add a new step to the scientific method: "consult with AI." That's because scientists are using artificial intelligence to help them design brand-new materials that solve human challenges like never before.

A team of materials scientists at TU Delft University in the Netherlands have created a new material that is super-compressible, yet durable..

This metamaterial created with artificial intelligence transforms a brittle material into a sponge-like material. Unlike a sponge, this metamaterial is stiff until a critical force is reached after which it becomes easily compressible. TU Delft University

In materials science, researchers are looking for novel geometries in design that are able to be created in real life, while also ensuring those solutions aren't just random. They have to be interesting in some way.

"If you change the geometry of the material, it will not do what you want it to do. It breaks or it bends in a different way, it doesn’t do what you want," Miguel Bessa, one of the authors of the new paper published in Advanced Materials, told Popular Mechanics.

But what's perhaps just as important as the new material, itself—which Bessa hopes will become real-life transformers in the coming years—is the fact that the AI made it possible to complete this research with exactly zero experimental tests in the lab.

“It takes a lot longer to do the tests in the lab. You have to set up the machine, set up the material...and it takes about a day or so," Bessa said. "What happens, in practice, is that scientists can only really do about 10 tests and some need about 100 tests. And that may not get you close enough when there are hundreds of thousands of possibilities."

Scientists use existing research to then use trial-and-error in the lab, he said. That works if you have a small number of possible designs that you're dealing with, but usually the space is larger. The whole process is time-consuming, even if you use a supercomputer.

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This is definitely not the first time that researchers have leaned on AI while experimenting with new materials—sure, the fact that no experimental tests were run is novel, but materials science, as a whole, has gotten a major upgrade thanks to the pure speed at which artificial intelligence has allowed scientists to create new designs.

You can see that in research on the AI methodology, itself. Researchers from the Massachusetts Institute of Technology, the University of Massachusetts at Amherst, and the University of California at Berkeley have even created a new AI system that can scour research papers to create new "recipes" for materials.

Bessa agreed that the importance of his discovery lies less in the new material and more in the way it was discovered. The data-driven approach behind AI in materials science points to a future where new designs are created through the use of computer models, where machine learning can use existing data to suggest new approaches.

The only requirements in this model are that there is a sufficient amount of data available on the subject in question and that the data is sufficiently accurate, Bessa said.

Bessa has made his research open-source so that others may experiment in the space. He hopes that this will lead to a real version of the material he discovered.



“I would like to be able to take this small material and realize the transformers," Bessa says, laughing. "The material would become active and reprogrammable...I think AI can do that [but] it’s a bit far-fetched—it will take a while.”

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