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 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 beh

SUBMITTER: Karacakol AC 

PROVIDER: S-EPMC11947188 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature