Rare-earth-free Permanent Magnets
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The project goal is to advance the fundamental understanding of mechanisms underlying nanostructure formation in multicomponent Permanent Magnets (PMs) alloys during magnetic-field-assisted manufacturing. We achieve this goal through a synergistic integration of state-of-the-art physics-based modeling along with microstructural quantification via machine learning. This synergy will result in new knowledge enabling us to use magnetic-field-assisted manufacturing to control geometrical motifs, and size of structuring from nanoscale to microscale to develop a novel PM with predictable and controllable magnetic properties.
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Informatics Skunkworks
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In collaboration with University of Wisconsin-Madison, we are: (a) developing scalable resources to increase undergraduate experience and learning in research at the boundary of data science and science and engineering, (b) growing a community of mentors and undergraduate researchers engaged in authentic materials informatics research, (c) increasing the use of data science tools for solving critical problems in materials science and related fields through increased workforce with materials informatics training. More details are on the Informatics Skunkworks website.
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Materials Informatics for Chemistry-Process-Structure-Property Linkages Construction
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Materials data science and informatics have recently shown tremendous potential in accelerating novel materials development. At CMD lab we utilize state-of-the-art machine learning techniques such as Deep Learning to quantify the microstructures and construct the chemistry-process-structure-property linkages.
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Life Assessment of Transformable Materials
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One of the primary factors limiting shape memory materials application is their low durability. However, while an understanding of actuation-induced failure and fracture of these materials is essential in order to integrate durability into their design, researchers do not fully understand it. Traditionally, our knowledge of fatigue crack nucleation and growth comes from non-transformable materials; with shape memory materials, the microstructure and its interaction with phase transformation make a significant contribution to shape memory material fatigue behavior. At CMD lab we are developing models to expand the fundamental understanding of the interaction of phase transformation, plastic deformation, and crack growth in transformable materials.
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Shape Memory Ceramics
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Shape memory ceramics (SMCs) are promising actuators at small scales which can be used at high-temperature, high-pressure, and corrosive environments. Other applications include energy harvesting, drug delivery devices, and perhaps some dreamed-of applications such as self-healing in composites. At CMD lab we develop multiscale models to predict the inter-relationships between processing, structure, and property in SMCs. Doing so would enable us to predict the mechanical properties of SMCs from fundamental physics.
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