ArticleProceedings of the National Academy of Sciences of the United States of America2022
Inverting the structure-property map of truss metamaterials by deep learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Morphogenetic mechanical metamaterials: Emerging tensor properties from self-organized structures.Nature communications · 2026Article
- Remote disassembly of electronics-free modular structures.Nature communications · 2026Article
- Generative adversarial networks enable biomimetic topology fusion with balanced mechanical performance and aesthetic quality.Scientific reports · 2026Article
- Multifaceted mechanical responsive metamaterials: Mechanisms, fabrications, and applications.Innovation (Cambridge (Mass.)) · 2026Review
- Trusses beyond trusses: intertwined and straw-based metamaterials and beyond.NPJ metamaterials · 2026Review
- Highly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Wallpaper Group-Based Mechanical Metamaterials: Dataset Including Mechanical Responses.Scientific data · 2025Article
- Mining extreme properties from a large metamaterial database.Nature communications · 2025Article
- Enhanced Deformability Through Distributed Buckling in Stiff Quasicrystalline Architected Materials.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Intelligent design of mechanical metamaterials: a GCNN-based structural genome database approach.National science review · 2025Article
- Enabling three-dimensional architected materials across length scales and timescales.Nature materials · 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
- Multi-objective design of multi-material truss lattices utilizing graph neural networks.Scientific reports · 2025Article
- Piezoelectric truss metamaterials: data-driven design and additive manufacturing.NPJ metamaterials · 2025Article
- Inverse design of curved mechanical metamaterials with geometric AI: a generative diffusion operates in compact latent space of cellular structures.NPJ metamaterials · 2025Article
- Generative models struggle with kirigami metamaterials.Scientific reports · 2024Article
- Modulate stress distribution with bio-inspired irregular architected materials towards optimal tissue support.Nature communications · 2024Article
- De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Deep reinforcement learning for microstructural optimisation of silica aerogels.Scientific reports · 2024Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inspired by crystallography, the periodic assembly of trusses into architected materials has enjoyed popularity for more than a decade and produced countless cellular structures with beneficial mechanical properties. Despite the successful and steady enrichment of the truss design space, the inverse design has remained a challenge: While predicting effective truss properties is now commonplace, efficiently identifying architectures that have homogeneous or spatially varying target properties has remained a roadblock to applications from lightweight structures to biomimetic implants. To overcome this gap, we propose a deep-learning framework, which combines neural networks with enforced physical constraints, to predict truss architectures with fully tailored anisotropic stiffness. Trained on millions of unit cells, it covers an enormous design space of topologically distinct truss lattices and accurately identifies architectures matching previously unseen stiffness responses. We demonstrate the application to patient-specific bone implants matching clinical stiffness data, and we discuss the extension to spatially graded cellular structures with locally optimal properties.
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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.