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A D V A N C E D M A T E R I A L S & P R O C E S S E S | N O V E M B E R / D E C E M B B E R 2 0 1 9 9 a triggering event—the introduction of a catalytic amount of the element ru- thenium—which causes an unzipping of the polymer. “We’ve really invested in leverag- ing fundamental thermodynamic prin- ciples in polymer science, and we use this to transform the molecules into a variety of possible shapes and chemis- tries,” the researchers say. “It’s a way to recycle these materials, but it’s also a way to get them to respond and change their architecture. There are a lot of fun possibilities with this.” fsu.edu . HARD AS CERAMIC, TOUGH AS STEEL Engineers at the University of Michigan, Ann Arbor, found a new way to calculate the interaction between a metal and its alloying material, contrib- uting to the search for a new material that combines the hardness of ceramic with the resilience of metal. The new method identifies two aspects of this interaction that can accurately predict how a particular alloy will behave—and with fewer demanding, from-scratch quantummechanical calculations. “Our findings may enable the use of machine learning algorithms for al- loy design, potentially accelerating the search for better alloys that could be used in turbine engines and nuclear re- actors,” says lead researcher Liang Qi. The search is on for a material that is very hard even at high temperatures but also resistant to cracking. Alloying elements combine with defects to cre- ate a network of disruptions in the lat- tice of the host metal, but it’s hard to predict how that network will affect the metal’s performance. The team limited their study to metals with just one alloying element at defects—still a considerable de- sign space with hundreds of material combinations and millions of defect structures. The researchers found they could predict how atoms of the alloying Two iterations of a metal lattice meet at a grain boundary defect, with atoms of an alloying element (gold) fitting into the defect. Courtesy of Liang Qi. element concentrated at various kinds of defects. However, they note that more de- scriptors must be discovered for predic- tions of how more complex alloys will behave, for instance those with two or more alloying elements at defects. And while these descriptors may feed into machine learning, humans will proba- bly identify them. umich.edu .