Graph-based metamaterials: Deep learning of structure-property relations

Abstract

The effective properties of metamaterials are tailored through the design of their internal structures. According to their main building block, the family of porous three-dimensional metamaterials is divided into truss-, plate- and shell-lattices. The exploration of their full design-space is hampered in practice by a lack of a systematic method to represent their topologies. Here, we demonstrate for the first time that graph models provide an effective representation of shell-lattices. This new graph representation is then leveraged to obtain deep learning-based structure–property models. Using finite element simulations, the stiffness and heat conductivity tensors are established for more than 40,000 microstructural configurations. We find that a modified crystal graph convolutional neural network model provides an accurate description of the structure–property relations. We anticipate the proposed graph-based modeling framework to be applicable to any man-made periodic microstructure, thereby enabling the design and discovery of new materials exhibiting exceptional mechanical, thermal, electrical or magnetic properties.