Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs
This addresses a fundamental conflict in engineering materials design, offering a blueprint for computational design across multiple research areas.
The paper tackled the problem of discovering microstructured composites with optimal stiffness-toughness trade-offs by introducing a pipeline integrating experiments, simulations, and neural networks, resulting in high sample efficiency and automatic identification of toughness enhancement mechanisms.
The conflict between stiffness and toughness is a fundamental problem in engineering materials design. However, the systematic discovery of microstructured composites with optimal stiffness-toughness trade-offs has never been demonstrated, hindered by the discrepancies between simulation and reality 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 both challenges. Without any prescribed expert knowledge of material design, our approach implements a nested-loop proposal-validation workflow to bridge the simulation-to-reality gap and discover microstructured composites that are stiff and tough with high sample efficiency. Further analysis of Pareto-optimal designs allows us to automatically identify existing toughness enhancement mechanisms, which were previously discovered through trial-and-error or biomimicry. On a broader scale, our method provides a blueprint for computational design in various research areas beyond solid mechanics, such as polymer chemistry, fluid dynamics, meteorology, and robotics.