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Designing aperiodic metamaterials using mechanical neural networks.

Pietro Sainaghi1, Zhidi Yang1, Jonathan B Hopkins1

  • 1Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA, 90095, USA. hopkins@seas.ucla.edu.

Materials Horizons
|April 8, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces mechanical neural networks (MNNs) to design complex aperiodic metamaterials. MNNs enable precise control over material behavior by learning optimal passive beam arrangements for desired shape-morphing functions.

Area of Science:

  • Metamaterials Science
  • Mechanical Engineering
  • Artificial Intelligence

Background:

  • Designing aperiodic metamaterials for multiple behaviors is challenging due to complex interdependencies.
  • Existing methods struggle to account for all real-world factors in metamaterial design.

Purpose of the Study:

  • To develop a novel design approach for aperiodic metamaterials using mechanical neural networks (MNNs).
  • To enable the accurate achievement of desired multiple behaviors in metamaterials by considering all relevant factors.
  • To demonstrate the MNN approach by designing two distinct shape-morphing aperiodic metamaterials.

Main Methods:

  • Utilized mechanical neural networks (MNNs) to determine optimal arrangements of passive beam designs within aperiodic metamaterials.

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  • MNNs learned by tuning the stiffness of mechatronically controlled active beams to match passive beam states.
  • Iterative physical learning process continued until desired material behaviors were achieved.

    • Main Results:

    • Successfully designed two aperiodic metamaterials with distinct shape-morphing capabilities using the MNN approach.
    • Demonstrated the MNN's ability to navigate complex design spaces and achieve specific functional behaviors.
    • Validated the effectiveness of the MNN in considering all real-world factors for metamaterial design.

    Conclusions:

    • Mechanical neural networks offer a powerful and efficient method for designing complex aperiodic metamaterials.
    • The MNN approach overcomes limitations of traditional methods by integrating physical learning and comprehensive factor consideration.
    • This work paves the way for advanced metamaterials with tailored multi-functional shape-morphing properties.