A new super-compressible material developed through AI by researchers at TU Delft can transform many of our everyday objects while still staying strong. The researchers did not conduct any experimental tests, and they created the material using only artificial intelligence and machine learning.
Miguel Bessa is the first author of the publication which appeared in Advanced Materials on October 14.
“AI gives you a treasure map, and the scientist needs to find the treasure,” he said.
Transforming Everyday Objects
Miguel Bessa, an assistant professor in materials science at TU Delft, got the inspiration to create this material after spending time at the California Institute of Technology. It was there, at the Space Structures Lab, where he observed a satellite structure that was able to open long solar sails from a small package.
After seeing this, Bessa wanted to know if it was possible to design a super-compressible yet strong material and compress it into a small fraction of its volume.
“If this was possible, everyday objects such as bicycles, dinner tables and umbrellas could be folded into your pocket,” he said.
The Next Generation of Materials
Bessa believes it is important that the next generation of materials be adaptive and multi-purpose with the capability to be altered. The way to do this is through structure-dominated materials, which are metamaterials that are able to exploit new geometries. This will allow the materials to have certain properties and functionalities that did not exist before.
“However, metamaterial design has relied on extensive experimentation and a trial-and-error approach,” Bessa says. “We argue in favor of inverting the process by using machine learning for exploring new design possibilities, while reducing experimentation to an absolute minimum.”
“We follow a computational data-driven approach for exploring a new metamaterial concept and adapting it to different target properties, choice of base materials, length scales and manufacturing processes.“
Using machine learning, Bessa developed two designs that were different length scales for the super-compressible material developed through AI. They transformed brittle polymers into metamaterials which were a lot more lightweight and recoverable. The most important and impressive aspect of these new metamaterials is that they are super-compressible. The macro-scale design focuses on maximum compressibility, while the micro-scale is best for high strength and stiffness.
Bessa argues that the most important part of the work is not the actual developed material, but it's the new way of designing through the use of machine learning and artificial intelligence. This could open up possibilities that were unknown before.
“The important thing is that machine learning creates an opportunity to invert the design process by shifting from experimentally guided investigations to computationally data-driven ones, even if the computer models are missing some information. The essential requisites are that ‘enough' data about the problem of interest is available, and that the data is sufﬁciently accurate.”
Bessa believes in data-driven research in materials science and its ability to revolutionize and transform our way of life.
“Data-driven science will revolutionize the way we reach new discoveries, and I can't wait to see what the future will bring us.”
Taking Over From Start to Finish
These new developments show that there are areas that can be transformed by AI and machine learning that are not well-known. While it is proven that artificial intelligence will revolutionize machines, technologies, and almost every other aspect of society, it is not often acknowledged that it can also develop these completely on their own. There will be a point at which machine learning and AI will take over the design and development process from start to finish. It will be up to humans to instill certain mechanisms in these technologies so that they are compatible with our ways of life.
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