Condensed Matter > Materials Science
[Submitted on 1 Feb 2023 (v1), last revised 3 Jan 2024 (this version, v2)]
Title:Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs
View PDF HTML (experimental)Abstract: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.
Submission history
From: Beichen Li [view email][v1] Wed, 1 Feb 2023 00:50:16 UTC (9,773 KB)
[v2] Wed, 3 Jan 2024 20:20:46 UTC (14,467 KB)
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