Inverse design of growth-inspired irregular architected materials for programmable properties

Abstract

Biomimetic metamaterials have gained increasing attention due to their exceptional characteristics such as high toughness, robust strength, and effective noise reduction. However, their complex and irregular nature presents challenges in tailoring their mechanical properties for specific applications. This study proposes a novel dual-network approach to overcome these challenges. The approach involves creating a forward model to accurately predict the mechanical properties and interconnectivity of the metamaterial without the need for growth and homogenization processes. Additionally, an inverse model is utilized to accurately predict designs for desired anisotropic stiffness. Compared to traditional bidirectional networks, our approach demonstrates superior accuracy in designing elastic properties. Our results also show that the metamaterial exhibits a broad low-frequency response while maintaining exceptional load-carrying capacity, making it a promising solution for designing low-frequency vibration suppression metamaterials.