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 | S E P T E M B E R 2 0 2 2 6 MACHINE LEARNING | AI DEEP LEARNING EXPLORES HIGH-ENTROPY ALLOYS Supercomputer simulations are helping scientists discover new types of high-entropy alloys (HEAs). Researchers recently used the Stampede2 supercomputer of the Texas Advanced Computing Center located at The University of Texas at Austin for newwork on these special alloys. The approach could be applied to finding new materials for batteries, catalysts, and more without the need for expensive metals such as platinum or cobalt. “High-entropy alloys represent a totally different design concept. In this case we try to mix multiple principal elements together,” says Wei Chen, an associate professor at the Illinois Institute of Technology. For the study, Chen and his colleagues surveyed 14 ele- ments and the combinations that yiel- ded HEAs. They then performed high- throughput quantum mechanical calculations to determine the stability and elastic properties of more than 7000 of these HEAs. Next, the team took this large dataset and applied a “Deep Sets” architecture—an advanced deep learning architecture that generates predictive models regarding the properties of new HEAs. The Deep Sets approach uses the elemental properties of individual HEAs to build predictive models that can predict the properties of a new alloy system. “We developed a new machine learning model and predicted the properties for more than 370,000 high-entropy alloy compositions,” says Chen. utexas.edu. AI HELPS ROBOTS NAVIGATE ENVIRONMENT Researchers at MIT, Cambridge, Mass., developed a method to 3D-print materials with tunable mechanical properties that sense how they are moving and interacting with the environment. The team creates these sensing structures using just one material and a single run on a 3D printer. To accomplish this, the researchers began with 3D-printed lattice materials and incorporated networks of air-filled channels into the structure during the printing process. By measuring how the pressure changes within these channels when the structure is squeezed, bent, or stretched, engineers can receive feedback on how the material is moving. The new method opens opportunities for embedding sensors within architected materials, a class of materials whose mechanical properties are programmed through form and composition. Controlling the geometry of features in architected materials alters their mechanical properties, such as stiffness or toughness. For example, in cellular structures such as the lattices printed by the team, a denser network of cells makes a stiffer structure. This technique could someday be used to create flexible soft robots with embedded sensors that enable the robots to understand their posture and movements. It might also be used to produce wearable smart devices that provide feedback on how a person is moving or interacting with their environment. “The idea with this work is that we can take any material that can be 3D-printed and have a simple way to route channels throughout it so we can get sensorization with structure. And if you use really complex materials, then you can have motion, perception, and structure all in one,” explains MIT graduate student Lillian Chin. mit.edu. Data-driven workflow used to map the elastic properties of high-entropy alloys. Courtesy of Chen et al. 3D-printed crystalline lattice structures with embedded fluidic sensors. The air channels enable researchers to measure howmuch force the lattices experience when they are compressed or flattened.
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