Chen Desai, Skouras Mélina, Zhu Bo, Matusik Wojciech
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Sci Adv. 2018 Jan 19;4(1):eaao7005. doi: 10.1126/sciadv.aao7005. eCollection 2018 Jan.
Modern fabrication techniques, such as additive manufacturing, can be used to create materials 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 as metamaterials or microstructures. Although metamaterials with extraordinary properties have many applications, designing them is very difficult and is generally done by hand. We propose a computational approach to discover families of microstructures 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 elastic material properties. Our study opens the way for the completely automated discovery of extremal microstructures across multiple domains of physics, including applications reliant on thermal, electrical, and magnetic properties.
现代制造技术，如增材制造，可用于制造具有复杂定制内部结构的材料。这些工程材料展现出比其基础材料更为广泛的整体性能范围，通常被称为超材料或微结构。尽管具有非凡特性的超材料有许多应用，但设计它们非常困难，通常是手工完成。我们提出一种计算方法，以自动发现具有极值宏观尺度特性的微结构族。利用高效的模拟和采样技术，我们计算出物理上可实现的微结构所涵盖的力学性能空间。然后，我们的系统将具有共同拓扑结构的微结构聚类为族。最终从这些族中提取参数化模板，以生成新的微结构设计。我们在机械超材料的计算设计上展示了这些能力，并呈现了五个具有极值弹性材料特性的负泊松比微结构族。我们的研究为跨多个物理领域完全自动化发现极值微结构开辟了道路，包括依赖热学、电学和磁学特性的应用。
