Machine learning guided resolution of mechanical trade-off in polymer composites via stress adaptive interface

This capsule provides a reproducible demonstration of a machine-learning–guided framework for resolving mechanical trade-offs in polymer composites with stress-adaptive interfacial architectures. The complete methodology integrates Gaussian process (GP) surrogate modeling, Pareto set learning (PSL), and active learning (AL) to explore multi-objective trade-offs among tensile strength, fracture toughness, and impact energy dissipation. The framework is designed to efficiently navigate high-dimensional composition spaces while minimizing experimental burden. In this capsule, we focus on the evaluation stage of the active learning pipeline, using an initial experimental dataset (n = 50) as a demonstration. The workflow includes GP model training, construction of an approximate Pareto set, and quantitative assessment of convergence and stability using hypervolume (HV) and stability (S) metrics. This capsule is intended to support transparency, reproducibility, and methodological clarity for the accompanying manuscript.