Mechanical Metamaterials

Mechanical metamaterials are artificial structures designed to exhibit unique mechanical properties beyond those of natural materials, primarily achieved through carefully engineered internal lattice structures. Current research heavily focuses on developing efficient inverse design methods, leveraging machine learning techniques like deep generative models (including diffusion models and variational autoencoders), graph neural networks, and Bayesian optimization to rapidly predict and optimize material properties based on structural geometry and material composition. This allows for the creation of metamaterials with tailored responses, such as auxetic behavior (negative Poisson's ratio), non-reciprocity, and tunable stiffness, impacting diverse fields including robotics, advanced manufacturing, and wave manipulation. The development of robust and efficient design frameworks is crucial for realizing the full potential of these materials in practical applications.