A Review of Machine Learning Applications in Mechanical Metamaterial Design
Galymzhan Turysbekov, Ulanbek Auyeskhan, Andrei Yankin, Asma Perveen, Didier Talamona · MDPI AG · 2026
The review summarizes how machine learning models and end-to-end workflows can accelerate mechanical metamaterial design and property prediction using simulation-based validation.
Plain English summary
Why this matters
Key findings
- Mechanical metamaterial design can be supported by ML workflows spanning dataset preparation to iterative, simulation-based validation.
- The review categorizes metamaterial architectures such as strut-based lattices and triply periodic minimal surfaces.
- Model comparisons include deep neural networks, fully connected and convolutional neural networks, graph neural networks, and generative models (GANs, diffusion models).
- Highlighted ML applications include mechanical property prediction and inverse design using finite element simulations and generative design models.
- A structured workflow and comparative summary are provided to guide future ML framework development for metamaterial design.
Limitations
The abstract does not specify quantitative performance results, experimental validation, or real-world deployment; it emphasizes simulation-based validation and provides a comparative summary rather than new experimental demonstrations.
Publication
- Publisher
- MDPI AG
- Publication date
- June 30, 2026
- Research type
- Paper
- License
- https://creativecommons.org/licenses/by/4.0/
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