#6955. Controllable inverse design of auxetic metamaterials using deep learning

December 2026publication date
Proposal available till 05-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Mechanical Engineering;
Mechanics of Materials;
Materials Science (all);
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Abstract:
As typical mechanical metamaterials with negative Poissons ratios, auxetic metamaterials exhibit counterintuitive auxetic behaviors that are highly dependent on their geometric arrangements. The realization of the geometric arrangement required to achieve a negative Poissons ratio relies considerably on the experience of designers and trial-and-error approaches. This report proposes an inverse design method for auxetic metamaterials using deep learning, in which a batch of auxetic metamaterials with a user-defined Poissons ratio and Youngs modulus can be generated by a conditional generative adversarial network without prior knowledge. The network was trained based on supervised learning using a large number of geometrical patterns generated by Voronoi tessellation. The performance of the network was demonstrated by verifying the mechanical properties of the generated patterns using finite element method simulations and uniaxial compression tests. The successful realization of user-desired properties can potentially accelerate the inverse design and development of mechanical metamaterials.
Keywords:
Additive manufacturing; Generative adversarial network; Metamaterial; Negative Poissons ratio; Voronoi tessellation

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