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Data-driven design of shape-programmable magnetic soft materials

Author

Listed:
  • Alp C. Karacakol

    (Max Planck Institute for Intelligent Systems
    Carnegie Mellon University)

  • Yunus Alapan

    (Max Planck Institute for Intelligent Systems
    University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • Sinan O. Demir

    (Max Planck Institute for Intelligent Systems
    University of Stuttgart)

  • Metin Sitti

    (Max Planck Institute for Intelligent Systems
    University of Stuttgart
    Koç University)

Abstract

Magnetically responsive soft materials with spatially-encoded magnetic and material properties enable versatile shape morphing for applications ranging from soft medical robots to biointerfaces. Although high-resolution encoding of 3D magnetic and material properties create a vast design space, their intrinsic coupling makes trial-and-error based design exploration infeasible. Here, we introduce a data-driven strategy that uses stochastic design alterations guided by a predictive neural network, combined with cost-efficient simulations, to optimize distributed magnetization profile and morphology of magnetic soft materials for desired shape-morphing and robotic behaviors. Our approach uncovers non-intuitive 2D designs that morph into complex 2D/3D structures and optimizes morphological behaviors, such as maximizing rotation or minimizing volume. We further demonstrate enhanced jumping performance over an intuitive reference design and showcase fabrication- and scale-agnostic, inherently 3D, multi-material soft structures for robotic tasks including traversing and jumping. This generic, data-driven framework enables efficient exploration of design space of stimuli-responsive soft materials, providing functional shape morphing and behavior for the next generation of soft robots and devices.

Suggested Citation

  • Alp C. Karacakol & Yunus Alapan & Sinan O. Demir & Metin Sitti, 2025. "Data-driven design of shape-programmable magnetic soft materials," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58091-z
    DOI: 10.1038/s41467-025-58091-z
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    References listed on IDEAS

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    1. Hen-Wei Huang & Mahmut Selman Sakar & Andrew J. Petruska & Salvador Pané & Bradley J. Nelson, 2016. "Soft micromachines with programmable motility and morphology," Nature Communications, Nature, vol. 7(1), pages 1-10, November.
    2. Jizhai Cui & Tian-Yun Huang & Zhaochu Luo & Paolo Testa & Hongri Gu & Xiang-Zhong Chen & Bradley J. Nelson & Laura J. Heyderman, 2019. "Nanomagnetic encoding of shape-morphing micromachines," Nature, Nature, vol. 575(7781), pages 164-168, November.
    3. Yoonho Kim & Hyunwoo Yuk & Ruike Zhao & Shawn A. Chester & Xuanhe Zhao, 2018. "Printing ferromagnetic domains for untethered fast-transforming soft materials," Nature, Nature, vol. 558(7709), pages 274-279, June.
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