Deep Learning-Guided Toughening of Irregular Growth-Inspired Metamaterials

Achieving simultaneous enhancement of strength and toughness in composite design remains a long-standing challenge due to their intrinsic trade-off. This study presents a bioinspired, growth-like structural strategy to overcome this limitation in biphasic composite systems. By integrating phase-field fracture modeling with deep-learning-assisted candidate screening, we systematically investigate the influence of growth morphologies on crack propagation, stress distribution, and energy dissipation. Results demonstrate that growth-inspired metamaterials mimicking biological branching and adaptive evolution promote crack deflection and load redistribution without compromising stiffness or strength. A multimodal CNN surrogate, using a structural image and a seven-dimensional vector of building-block proportions as inputs, predicts the complete nominal stress–strain response, from which the elastic modulus, ultimate strength, and volumetric energy absorption are subsequently derived. Compared with the architecture exhibiting the highest volumetric energy absorption in the prescribed 40% soft-phase training subset, the selected architecture exhibits increases of 22.0% in volumetric energy absorption, 35.4% in Young’s modulus, and 39.2% in ultimate strength. These numerical results demonstrate the potential of the proposed framework for efficiently screening biphasic architected composites within the investigated design and loading conditions.