Data-driven design of shape-programmable magnetic soft materials
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 sp...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58091-z |
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| Summary: | 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. |
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| ISSN: | 2041-1723 |