ArticleNature communications2025
Data-driven design of shape-programmable magnetic soft materials.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Vine-Inspired Soft Robotics: Materials, Actuations, and Emerging Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- 3D Printing of Magnetic Soft Materials for Functional Structures and Devices.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Machine learning-driven design of engineered cilia enables hybrid operations in acoustic microrobots.Nature communications · 2026Article
- Magnetically controlled multimodal motion for environmentally adaptive soft millirobots with transformable wheel-leg morphology.Innovation (Cambridge (Mass.)) · 2026Article
- Magneto-X Effects in Magnetic Soft Materials and Their Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Digital composites with reprogrammable phase architectures.Science advances · 2026Article
- Embodied Cross-Domain Intelligence in Biomedical Microrobots: A Review.Cyborg and bionic systems (Washington, D.C.) · 2026Review
- Co-design of programmable material systems.NPJ metamaterials · 2026Review
- Articulated Untethered Magnetic Actuators for Multimodal and Cross-Scale Operations.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Stable magnetic soft structures.Science advances · 2025Article
- Magnetically Controllable and Degradable Milliscale Swimmers as Intraocular Drug Implants.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.