# Machine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterials

arXiv: https://arxiv.org/abs/2404.13215 · HTML: https://mlsanity.com/paper/2404.13215/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 87 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 24 of 24)

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- [MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities](https://mlsanity.com/paper/2310.10445/) (2023)
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- [Towards out of distribution generalization for problems in mechanics](https://mlsanity.com/paper/2206.14917/) (2022)
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- [Bayesian Optimization in Materials Science: A Survey](https://mlsanity.com/paper/2108.00002/) (2021)
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- [Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset](https://mlsanity.com/paper/2203.04183/) (2022)
- [Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs](https://mlsanity.com/paper/2302.01078/) (2023)
- [Nonreciprocal Phased-Array Antennas](https://mlsanity.com/paper/1911.02059/) (2019)
- [Investigating Deep Learning Model Calibration for Classification Problems in Mechanics](https://mlsanity.com/paper/2212.00881/) (2022)
- [Segmenting mechanically heterogeneous domains via unsupervised learning](https://mlsanity.com/paper/2308.15697/) (2023)

## Abstract (arXiv)

There has been significant recent interest in the mechanics community to design structures that can either violate reciprocity, or exhibit elastic asymmetry or odd elasticity. While these properties are highly desirable to enable mechanical metamaterials to exhibit novel wave propagation phenomena, it remains an open question as to how to design passive structures that exhibit both significant non-reciprocity and elastic asymmetry. In this paper, we first define several design spaces for chiral metamaterials leveraging specific design parameters, including the ligament contact angles, the ligament shape, and circle radius. Having defined the design spaces, we then leverage machine learning approaches, and specifically Bayesian optimization, to determine optimally performing designs within each design space satisfying maximal non-reciprocity or stiffness asymmetry. Finally, we perform multi-objective optimization by determining the Pareto optimum and find chiral metamaterials that simultaneously exhibit high non-reciprocity and stiffness asymmetry. Our analysis of the underlying mechanisms reveals that chiral metamaterials that can display multiple different contact states under loading in different directions are able to simultaneously exhibit both high non-reciprocity and stiffness asymmetry. Overall, this work demonstrates the effectiveness of employing ML to bring insights to a novel domain with limited prior information, and more generally will pave the way for metamaterials with unique properties and functionality in directing and guiding mechanical wave energy.