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

arXiv: https://arxiv.org/abs/2302.01078 · HTML: https://mlsanity.com/paper/2302.01078/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 71 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Machine learning accelerated computational fluid dynamics](https://mlsanity.com/paper/2102.01010/) (2021)
- [DiffTaichi: Differentiable Programming for Physical Simulation](https://mlsanity.com/paper/1910.00935/) (2019)

## Built on by (most cited first, 6 of 6)

- [Inverse designing metamaterials with programmable nonlinear functional responses in graph space](https://mlsanity.com/paper/2408.06300/) (2024)
- [Amortized Multi-Objective Optimization Across Tasks with Generative Solution Modeling](https://mlsanity.com/paper/2511.09598/) (2025)
- [Machine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterials](https://mlsanity.com/paper/2404.13215/) (2024)
- [Data-Driven Nonlinear Deformation Design of 3D-Printable Shells](https://mlsanity.com/paper/2408.15097/) (2024)
- [AI-Enhanced Automatic Design of Efficient Underwater Gliders](https://mlsanity.com/paper/2505.00222/) (2025)
- [Learning from fracture: Teaching language models to design through physical experience](https://mlsanity.com/paper/2610.17804/) (2026)

## Abstract (arXiv)

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.