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Cellular solids and micro-lattices are a class of lightweight architectedmaterials that have been established for their unique mechanical, thermal, andacoustic properties. It has been shown that by tuning material architecture, acombination of topology and solid(s) distribution, one can design new materialsystems, also known as metamaterials, with superior performance compared toconventional monolithic solids. Despite the continuously growing complexity ofsynthesized microstructures, mainly enabled by developments in additivemanufacturing, correlating their morphological characteristics to the resultingmaterial properties has not advanced equally. This work aims to develop asystematic data-driven framework that is capable of identifying all keymicrostructural characteristics and evaluating their effect on a targetmaterial property. The framework relies on integrating virtual structuregeneration and quantification algorithms with interpretable surrogate models.The effectiveness of the proposed approach is demonstrated by analyzing theeffective stiffness of a broad class of two-dimensional (2D) cellularmetamaterials with varying topological disorder. The results reveal the complexmanner in which well-known stiffness contributors, including nodalconnectivity, cooperate with often-overlooked microstructural features such asstrut orientation, to determine macroscopic material behavior. We furtherre-examine Maxwell's criteria regarding the rigidity of frame structures, asthey pertain to the effective stiffness of cellular solids and showcasemicrostructures that violate them. This framework can be used forstructure-property correlation in different classes of metamaterials as well asthe discovery of novel architectures with tailored combinations of materialproperties.

A data-driven framework for structure-property correlation in ordered and disordered cellular metamaterials