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This review evaluates the role of machine learning in designing mechanical metamaterials with unique properties.

July 2, 2026MaterialsOpen Access

A Review of Machine Learning Applications in Mechanical Metamaterial Design

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Review highlights machine learning applications for designing unique metamaterials, suggesting innovative frameworks.

This review evaluates the role of machine learning in designing mechanical metamaterials with unique properties.
Categorized architectures like strut-based lattices and triply periodic minimal surfaces.
Discussed various machine learning models including deep neural networks and generative models.
Outlined an end-to-end design workflow from dataset preparation to simulation-based validation.
Highlighted successful applications in mechanical property prediction using machine learning.
Presented a comparative summary of recent studies to inform future research direction.
Showed examples of inverse design using finite element simulations and generative models.

Turysbekov et al. (2026) studied this question.

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