Imagine a plastic bag that can carry home your groceries, then quickly degrade, without harming the environment. Or a super-strong, lightweight plastic for airplanes, rockets, and satellites that can replace traditional structural metals in aerospace technologies.
Machine learning and artificial intelligence have accelerated the ability to design materials with specific properties like these. But while scientists have had success designing new metallic alloys, polymers—like the plastic used for bags—have been much more difficult to design.
Researchers at the Pritzker School of Molecular Engineering (PME) at the University of Chicago have found a way forward in designing polymers by combining modeling and machine learning.
By computationally constructing nearly 2,000 hypothetical polymers, they were able to create a large-enough database to train a neural network—a type of machine learning—to understand which polymer properties arise from different molecular sequences.
“We show that the problem is tractable,” said Juan de