ArticleNature communications2023
Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Enhancing Specific Stiffness of Nano-Architected Materials Through Generative AI and Active Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Large-scale data-driven inverse EM design based on metacircuit-embedded surface.Nature communications · 2026Article
- From Prediction to Creation: Generative Plant Design.Plants (Basel, Switzerland) · 2026Review
- Modeling the interpretable geometric-performance relationship of metamaterials on small datasets using Kolmogorov-Arnold operator informed network.Scientific reports · 2026Article
- Active Metamaterials with Tunable Shear Nonreciprocity and Nonlinear Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Lattice Genome Framework for Regionally Tailored Component-Level Multi-Objective Design in Additive Manufacturing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Design framework for programmable three-dimensional woven metamaterials.Nature communications · 2026Article
- Inverse design of cellular structures with the targeted nonlinear mechanical response.Scientific reports · 2025Article
- Wallpaper Group-Based Mechanical Metamaterials: Dataset Including Mechanical Responses.Scientific data · 2025Article
- Volumetric 3D Printing and Melt-Electrowriting to Fabricate Implantable Reinforced Cardiac Tissue Patches.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Embracing nonlinearity and geometry: a dimensional analysis guided design of shock absorbing materials.Nature communications · 2025Article
- Active heterogeneous mode coupling in bi-level multi-physically architected metamaterials for temporal, on-demand and tunable programming.Communications engineering · 2025Article
- Customizable wave tailoring nonlinear materials enabled by bilevel inverse design.Nature communications · 2025Article
- Intelligent design of mechanical metamaterials: a GCNN-based structural genome database approach.National science review · 2025Article
- Review
- AI-Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Multi-objective design of multi-material truss lattices utilizing graph neural networks.Scientific reports · 2025Article
- Generative design-enabled exploration of wireframe DNA origami nanostructures.Nucleic acids research · 2025Article
- Inverse design of curved mechanical metamaterials with geometric AI: a generative diffusion operates in compact latent space of cellular structures.NPJ metamaterials · 2025Article
- Piezoelectric truss metamaterials: data-driven design and additive manufacturing.NPJ metamaterials · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rise of machine learning has fueled the discovery of new materials and, especially, metamaterials-truss lattices being their most prominent class. While their tailorable properties have been explored extensively, the design of truss-based metamaterials has remained highly limited and often heuristic, due to the vast, discrete design space and the lack of a comprehensive parameterization. We here present a graph-based deep learning generative framework, which combines a variational autoencoder and a property predictor, to construct a reduced, continuous latent representation covering an enormous range of trusses. This unified latent space allows for the fast generation of new designs through simple operations (e.g., traversing the latent space or interpolating between structures). We further demonstrate an optimization framework for the inverse design of trusses with customized mechanical properties in both the linear and nonlinear regimes, including designs exhibiting exceptionally stiff, auxetic, pentamode-like, and tailored nonlinear behaviors. This generative model can predict manufacturable (and counter-intuitive) designs with extreme target properties beyond the training domain.
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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.