Computational Discovery of Extremal Microstructure Families


Science Advances


Desai Chen, Mélina Skouras, Bo Zhu and Wojciech Matusik


Modern fabrication techniques, such as additivemanufacturing, can be used to creatematerials with complex custom internal structures. These engineered materials exhibit a much broader range of bulk properties than their base materials and are typically referred to asmetamaterials or microstructures. Althoughmetamaterials with extraordinary properties have many applications, designing them is very difficult and is generally done by hand.Wepropose a computational approach to discover families ofmicrostructures with extremal macroscale properties automatically. Using efficient simulation and sampling techniques, we compute the space of mechanical properties covered by physically realizable microstructures. Our system then clusters microstructures with common topologies into families. Parameterized templates are eventually extracted from families to generate new microstructure designs. We demonstrate these capabilities on the computational design of mechanical metamaterials and present five auxetic microstructure families with extremal elasticmaterial properties. Our study opens the way for the completely automated discovery of extremal microstructures acrossmultiple domains of physics, including applications reliant on thermal, electrical, and magnetic properties.




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