Deep Learning‐Assisted Design of Mechanical Metamaterials

Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have emerged as a frontier in material science and engineering. However, conventional MM design methods, which heavily depend on expert intuition and iterative trial‐and‐error processes, are often inefficient and computationally intensive. Nowadays, the fusion of deep learning (DL) into MM design has been reshaping this landscape, enabling rapid material discovery, performance‐driven optimization, and the realization of previously unattainable functionalities/features. This review provides a comprehensive overview of data‐driven DL methodologies for MM design, encompassing dataset preparation, forward design, inverse design, generative design, and multiscale modeling. We further explore the wide‐ranging applications of DL‐enabled MMs in biomedical implants and intelligent wearables, programable and high‐performance metamaterials, and MM intelligence. Despite remarkable progress, challenges, such as data scarcity and bias, limited interpretability, and the multiobjective optimization problem persist. Thus, we conclude by discussing future directions for the field, including generalizable and flexible design space, multiphysics‐aware modeling, multiobjective and adaptive design strategies, and the emerging role of multimodal large language models in automating and enhancing DL‐driven material innovation.