With transparent machine learning tool, engineers accelerate polymer discovery


Machine learning can be a powerful tool for discovering and designing new polymers, according to new research from the UW–Madison College of Engineering. Photo illustration by Xin (Zoe) Zou/UW–Madison College of Engineering


Using the power of prediction, University of Wisconsin–Madison mechanical engineers have quickly discovered several promising high-performance polymers out of a field of 8 million candidates.

The aerospace, automobile and electronics industries use these polymers, known as polyimides, for a wide variety of applications because they have excellent mechanical and thermal properties — including strength, stiffness and heat resistance.

Right now, there’s a limited number of existing polyimides because the process of designing them is costly and time-consuming.

However, with their data-driven design framework, the UW­–Madison engineers leverage machine learning predictions and molecular dynamics simulations to dramatically speed up the discovery of new polyimides with even better properties.

The team detailed its approach in a paper published this month in the Chemical Engineering Journal.

“Our findings have broad implications for the field of materials science and will inspire further research in the development of advanced data-driven techniques for materials discovery,” says Ying Li, an associate professor of mechanical engineering at UW–Madison who led the research. “Our design strategy is much more efficient compared to the conventional trial-and-error process and can also be applied to the molecular design of other polymeric materials.”

Read More