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AI helps chemists develop tougher plastics
A new strategy for strengthening polymer materials could lead to more durable plastics and cut down on plastic waste, MIT and Duke University researchers report.
MIT postdoc Ilia Kevlishvili is the lead author of the open-access paper, which appeared Friday in ACS Central Science. Realizing that a machine-learning approach could dramatically speed up the characterization of these molecules, the MIT and Duke team decided to use a neural network to identify ferrocenes that could be promising mechanophores. The researchers now hope to use their machine-learning approach to identify mechanophores with other desirable properties, such as the ability to change color or become catalytically active in response to force.
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