Non-symmetric Plate-Lattices: Recurrent Neural Network-based Design of Optimal Metamaterials

The elastic response of plate-lattices with cubic symmetry can reach the upper, isotropic Hashin-Shtrikman bound. Here, our primary objective is the design of stiff lattices with tailored properties beyond any symmetry or isotropy. We propose a general construction method of plate-lattices, defined as a sequence of plates. We present the large elastic property space, accessible via the geometric control offered by our construction method. Building upon this general formulation, we provide a large-scale comparison to the property spaces of truss- and shell-lattices. Additionally, we observe a distinct correlation between plate orientation and stiffness. To tailor the properties of plate-lattice, we employ a recurrent neural network mapping from a given property to the corresponding structure. Our results highlight the effectiveness of this inverse model in creating plate-lattices with desired properties. We believe that this work holds a paradigm shift in field of inverse design by enabling the efficient customization of the stiffest family of metamaterials.