Abstract
The optimization of a material requires tuning a single macroscopic property to take on an extreme value by manipulating microscale features. Here we show how artificial evolution can be used as an engine to accomplish this task, even in the absence of explicit relationships between micro-scale physics and bulk properties. As a particular example, we consider the task of designing the bulk mechanical properties of a granular aggregate by way of the constituent particle shape. We use artificial evolution to identify the particle shapes, that when aggregated, produce the stiffest and softest packings plus a packing that stiffens when compressed. These extreme materials are constructed and tested in the real world by 3D printing particles. Finally, we examine how optimization can be a means to extraordinary materials, the starting point for new scientific insights, or both.
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Miskin, M.Z. (2016). Optimization. In: The Automated Design of Materials Far From Equilibrium. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-24621-5_3
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DOI: https://doi.org/10.1007/978-3-319-24621-5_3
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