Intelligent design of mechanical metamaterials: a GCNN-based structural genome database approach.

ABSTRACT: The reciprocal mapping between the geometry and properties of a unit cell is crucial for the intelligent and inverse design of advanced materials and structural systems. Beyond classical homogenization-based numerical methods, this paper presents an efficient and accurate mapping between the geometry and properties of a class of unit cells described by moving morphable components, achieved via a graph convolutional neural network. This leads to a structural genome database (SGD) approach for the intelligent design of mechanical metamaterials. Using the SGD approach, metamaterials exhibiting the Hashin-Shtrikman upper bound of bulk modulus, auxetic behavior and the unimodal property have been created, with design efficiency improved by 3-4 orders of magnitude. Additionally, transfer learnin

SUBMITTER: Hao W 

PROVIDER: S-EPMC11912873 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature