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Mechanical metamaterials are renowned for their ability to achieve high stiffness and strength at low densities, often at the expense of low ductility and stretchability-a persistent trade-off in materials. In contrast, materials such as double-network hydrogels feature interpenetrating compliant and stiff polymer networks, and exhibit unprecedented combinations of high stiffness and stretchability, resulting in exceptional toughness. Here, we present double-network-inspired (DNI) metamaterials by integrating monolithic truss (stiff) and woven (compliant) components into a metamaterial architecture, which achieve a tenfold increase in stiffness and stretchability compared to their pure woven and truss counterparts, respectively. Nonlinear computational mechanics models elucidate that enhanced energy dissipation in these DNI metamaterials stems from increased frictional dissipation due to entanglements between the two networks. Through introduction of internal defects, which typically degrade mechanical properties, we demonstrate an opposite effect of a threefold increase in energy dissipation for these metamaterials via failure delocalization. This work opens avenues for developing new classes of metamaterials in a high-compliance regime inspired by polymer network topologies. 

Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when designing mechanical metamaterials with extreme performance. While their elastic mechanical properties have been systematically determined, their large-deformation, nonlinear responses have been challenging to predict and design, in part due to limited data sets and the need for complex nonlinear simulations. This work presents a novel physics-enhanced machine learning (ML) and optimization framework tailored to address the challenges of designing intricate spinodal metamaterials with customized mechanical properties in large-deformation scenarios where computational modeling is restrictive and experimental data is sparse. By utilizing large-deformation experimental data directly, this approach facilitates the inverse design of spinodal structures with precise finite-strain mechanical responses. The framework sheds light on instability-induced pattern formation in spinodal metamaterialsobserved experimentally and in selected nonlinear simulations—leveraging physics-based inductive biases in the form of nonconvex energetic potentials. Altogether, this combined ML, experimental, and computational effort provides a route for efficient and accurate design of complex spinodal metamaterials for large-deformation scenarios where energy absorption and prediction of nonlinear failure mechanisms is essential. 

Mechanical metamaterials at the microscale exhibit exotic static properties owing to their en- gineered building blocks, but their dynamic properties have remained significantly less explored. Their design principles can target frequency-dependent properties and resilience upon high-strain-rate deformation, making them versatile materials for applications in lightweight impact resistance, acoustic waveguiding, or vibration damping. However, accessing dynamic properties at small scales has remained a challenge due to low-throughput and destructive characterization, or lack of existing testing protocols. Here we demonstrate a high-throughput non-contact framework that employs MHz-wave propagation signatures within a metamaterial to nondestructively extract dynamic linear properties, omnidirectional elastic information, damping properties, and defect quantification. Using rod-like tessellations of microscopic metamaterials, we report up to 94% direction- and rate-dependent dynamic stiffening at strain rates approaching 10^2 s^{−1}, in addition to damping properties 3 times higher than their constituent materials. We also show that frequency shifts in the vibrational response allow for characterization of invisible defects within the metamaterials, and that selective probing allows for construction of experimental elastic surfaces, previously only possible computationally. Our work provides a route for accelerated data-driven discovery of materials and microdevices for dynamic applications such as protective structures, medical ultrasound, or vibration isolation.