Wang, H., Cheng, J., Wu, Z., Chen, X., Liu, S., Niu, D., Zhang, J., Jin, K., & He, C. (2026). Machine learning guided resolution of mechanical trade-off in polymer composites via stress adaptive interface. Nature Communications, 17(1). https://doi.org/10.1038/s41467-026-69872-5
Abstract:
Developing polymer composites that simultaneously achieve high strength, toughness, and impact resistance remains a fundamental challenge due to inherent trade-offs and brittle interfacial failure. Here, we propose a universal toughening strategy that integrates a bone-inspired trabecular interlock architecture with a thermodynamically driven, stress-adaptive interface to enable efficient energy dissipation under mechanical loading. To address multi-objective optimization in composites design, we further develop a data-driven framework combining Pareto Set Learning and Active Learning, which systematically explores the composition–performance landscape to identify balanced, high-performance formulations. The optimized composites exhibit synergistic mechanical properties: strength up to 250 MPa, fracture toughness exceeding 14 MPa·m1/2, and impact resistance of nearly 4.8 J, surpassing most bioinspired and engineered polymer counterparts. The strategy is scalable, chemically versatile, and broadly applicable, offering a programmable route to next-generation lightweight composites for aerospace, transportation, and protection.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the National Research Foundation Singapore - Low Carbon Energy Research Phase 2
Grant Reference no. : U2307D4001