ArticleNature communications2024
Machine learning-enabled forward prediction and inverse design of 4D-printed active plates.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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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
21 citing papers in PubMed.
- Smart Bioinks for 4D Bioprinting: Requirements, Design, and 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
- Digintel metasurface of extensive reprogrammable morphing.Science advances · 2026Article
- Mechanochemical and machine-intelligent design of programmable materials: From molecular interactions to macroscale functionality.Materials today. Bio · 2026Review
- Body-responsive shape-memory polymers for biomedical applications.Bioactive materials · 2026Review
- Deformation Prediction of 4D-Printed Active Composite Structures Based on Data Mining.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- Co-design of programmable material systems.NPJ metamaterials · 2026Review
- GELATO: multi-material topology optimization of programmable gel-elastomer structures.Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization · 2026Article
- Inverse design of stochastic, voxelated thermo-viscoelastic digital materials.Nature communications · 2025Article
- Spatiotemporally programmed dielectric liquid crystal elastomer: Electro-reversible 3D morphing via inverse 4D printing.Science advances · 2025Article
- Machine learning-enabled inverse design of bioinspired layered composite structures with maximum auxetic performance.Communications engineering · 2025Article
- Halftone-encoded 4D printing of stimulus-reconfigurable binary domains for cephalopod-inspired synthetic smart skins.Nature communications · 2025Article
- Autonomous codesign and fabrication of multistimuli-responsive material systems.Science advances · 2025Article
- A Soft Reconfigurable Inverted Climbing Robot Based on Magneto-Elastica-Reinforced Elastomer.Micromachines · 2025Article
- A physics-informed and data-driven framework for robotic welding in manufacturing.Nature communications · 2025Article
- Digital light processing 3D printing of flexible devices: actuators, sensors and energy devices.Microsystems & nanoengineering · 2025Review
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Multi-Physical Lattice Metamaterials Enabled by Additive Manufacturing: Design Principles, Interaction Mechanisms, and Multifunctional Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Machine learning-enabled forward prediction and inverse design of 4D-printed active plates.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Shape transformations of active composites (ACs) depend on the spatial distribution of constituent materials. Voxel-level complex material distributions can be encoded by 3D printing, offering enormous freedom for possible shape-change 4D-printed ACs. However, efficiently designing the material distribution to achieve desired 3D shape changes is significantly challenging yet greatly needed. Here, we present an approach that combines machine learning (ML) with both gradient-descent (GD) and evolutionary algorithm (EA) to design AC plates with 3D shape changes. A residual network ML model is developed for the forward shape prediction. A global-subdomain design strategy with ML-GD and ML-EA is then used for the inverse material-distribution design. For a variety of numerically generated target shapes, both ML-GD and ML-EA demonstrate high efficiency. By further combining ML-EA with a normal distance-based loss function, optimized designs are achieved for multiple irregular target shapes. Our approach thus provides a highly efficient tool for the design of 4D-printed active composites.
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.