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, plastics have been much more difficult to design. The molecules that make them up, called polymers, are extremely chemically complex.

Researchers from the Pritzker School of Molecular Engineering at the University of Chicago, however, announced they have finally found a way to design 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 to understand which polymer properties arise from different molecular sequences.

“We show that the problem is tractable,” said Juan de Pablo, the Liew Family Professor of Molecular Engineering, who led the research. “Now that we have established this foundation and have shown that it can be done, we can really move forward in using this framework to design polymers with specific properties.”

The results were published Oct. 21 in Science Advances.

Read more at UChicago News.

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