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Oak Ridge National Laboratory researchers used an invertible neural network, a type of artificial intelligence that mimics the human brain, to select the most suitable materials for desired properties, such as flexibility or heat resistance, with high chemical accuracy. The study could lead to more customizable materials design for industry.

A study led by researchers at ORNL could help make materials design as customizable as point-and-click.

From left, Amit Naskar, Ngoc Nguyen and Christopher Bowland in ORNL’s Carbon and Composites Group bring a new capability—structural health monitoring—to strong, lightweight materials promising for transportation applications.

Carbon fiber composites—lightweight and strong—are great structural materials for automobiles, aircraft and other transportation vehicles. They consist of a polymer matrix, such as epoxy, into which reinforcing carbon fibers have been embedded. Because of differences in the mecha...