Data-driven design of biometric composite metamaterials with extremely recoverable and ultrahigh specific energy absorption

The existing mechanical metamaterials are often designed with periodic inter-connected structs with simple cylindrical or uniform hierarchical geometries, which relies on their parent materials to either have a good mechanical performance with low recoverability, or significantly sacrifices their mechanical performances to be highly recoverable. Biological fibrous structures are often evolved with a composition of different fibrous morphologies to possess a desired balance of mechanical performances and recovery. In this study, we developed digital design algorithms to generate the next-generation metamaterials with composite bio-inspired twisting fibrotic structs that are rubber-like recoverable without significant scarification of their mechanical perfor-mances. A machine learning predictive model is trained based on experimental data to reveal the resulted specific energy absorption (SEA) and SEA recoveries for such metamaterials with complicated fiber-composition mechanisms. To further understand the fundamental structural recovery mechanisms of the natural fibers, we derived the elastoplastic theories of the twisting fibrotic structs, and revealed that such structs possesses a rubber-like fracture strain with significantly improved specific energy absorption. Our studies combined the structural recovery mechanisms of the composite natural fibrous structures and mechanical metamaterials, liberates the design potential of materials with engineerable optimal balances of their mechanical performances and recoverability.