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The conflict between stiffness and toughness is a fundamental problem inengineering materials design. However, the systematic discovery ofmicrostructured composites with optimal stiffness-toughness trade-offs hasnever been demonstrated, hindered by the discrepancies between simulation andreality and the lack of data-efficient exploration of the entire Pareto front.We introduce a generalizable pipeline that integrates physical experiments,numerical simulations, and artificial neural networks to address bothchallenges. Without any prescribed expert knowledge of material design, ourapproach implements a nested-loop proposal-validation workflow to bridge thesimulation-to-reality gap and discover microstructured composites that arestiff and tough with high sample efficiency. Further analysis of Pareto-optimaldesigns allows us to automatically identify existing toughness enhancementmechanisms, which were previously discovered through trial-and-error orbiomimicry. On a broader scale, our method provides a blueprint forcomputational design in various research areas beyond solid mechanics, such aspolymer chemistry, fluid dynamics, meteorology, and robotics.

Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs