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 learning and a small amount of training data allow the SGD to predict non-local behaviors beyond a unit cell, such as optimized unit cells with critical buckling strength enhanced by nearly 200% and a bandgap metamaterial with a relative bandgap width of 51%. Experimentally validated optimized metamaterials demonstrate auxetic behavior and superior buckling resistance. The proposed SGD approach holds promise for the advanced design of multi-scale and multi-physics systems.