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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| Format: | Article |
| Language: | English |
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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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| _version_ | 1849391033613287424 |
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| author | Alp C. Karacakol Yunus Alapan Sinan O. Demir Metin Sitti |
| author_facet | Alp C. Karacakol Yunus Alapan Sinan O. Demir Metin Sitti |
| author_sort | Alp C. Karacakol |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7d596e6cc32345a79d6e2396f2616be2 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-7d596e6cc32345a79d6e2396f2616be22025-08-20T03:41:14ZengNature PortfolioNature Communications2041-17232025-03-0116111310.1038/s41467-025-58091-zData-driven design of shape-programmable magnetic soft materialsAlp C. Karacakol0Yunus Alapan1Sinan O. Demir2Metin Sitti3Physical Intelligence Department, Max Planck Institute for Intelligent SystemsPhysical Intelligence Department, Max Planck Institute for Intelligent SystemsPhysical Intelligence Department, Max Planck Institute for Intelligent SystemsPhysical Intelligence Department, Max Planck Institute for Intelligent SystemsAbstract 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.https://doi.org/10.1038/s41467-025-58091-z |
| spellingShingle | Alp C. Karacakol Yunus Alapan Sinan O. Demir Metin Sitti Data-driven design of shape-programmable magnetic soft materials Nature Communications |
| title | Data-driven design of shape-programmable magnetic soft materials |
| title_full | Data-driven design of shape-programmable magnetic soft materials |
| title_fullStr | Data-driven design of shape-programmable magnetic soft materials |
| title_full_unstemmed | Data-driven design of shape-programmable magnetic soft materials |
| title_short | Data-driven design of shape-programmable magnetic soft materials |
| title_sort | data driven design of shape programmable magnetic soft materials |
| url | https://doi.org/10.1038/s41467-025-58091-z |
| work_keys_str_mv | AT alpckaracakol datadrivendesignofshapeprogrammablemagneticsoftmaterials AT yunusalapan datadrivendesignofshapeprogrammablemagneticsoftmaterials AT sinanodemir datadrivendesignofshapeprogrammablemagneticsoftmaterials AT metinsitti datadrivendesignofshapeprogrammablemagneticsoftmaterials |