Data-driven inverse design of growth-based Voronoi meta-materials

Metamaterials derive their properties from microstructure rather than from bulk material properties. This opens property spaces that are difficult, or impossible, to access with traditional methods. However, exploring this vast design space remains challenging because classical techniques can be computationally inefficient. Recent years has seen...

AI-Based Inverse Design of Random Network Metamaterials

Mechanical metamaterials are materials that appear to have unusual elastic mechanical properties, with the most prominent being a negative Poisson’s ratio. The key to their unique properties lies in the microarchitecture of the material rather than the material itself. For advanced applications in the realm of prosthetics, such as form matching,...