ArticleNature communications2023
Machine learning-enabled constrained multi-objective design of architected materials.
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 31 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.
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Who cites it
31 citing papers in PubMed.
- Machine learning-driven optimization of high-velocity impact resistance for three-dimensional biphasic composites.Nature communications · 2026Article
- Nature-inspired biomaterial innovations to counter medical device-associated infections.Biomaterials science · 2026Review
- Spatiotemporal immunomodulation with programmable biomaterials to promote musculoskeletal tissue regeneration.Bioactive materials · 2026Review
- Generative adversarial networks enable biomimetic topology fusion with balanced mechanical performance and aesthetic quality.Scientific reports · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Lattice Genome Framework for Regionally Tailored Component-Level Multi-Objective Design in Additive Manufacturing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Data-driven multiscale design of composite biomaterials: Integrating experiments, imaging, and computational modeling for biomedical engineering.Materials today. Bio · 2026Review
- Machine Learning-Assisted Bio-Interfacial Engineering Resolves Structural-Functional Conflicts in Nanocomposites.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Experimental mechanician for plate lattice metamaterial discovery.Nature communications · 2026Article
- Exosome-enabled bone defect repair: mechanistic foundations, bioengineered delivery, and artificial intelligence-driven translation.Journal of nanobiotechnology · 2026Review
- Data-driven inverse design of multifunctional bicontinuous multiscale structures.Nature communications · 2026Article
- Self-supervised AI for decoding and designing disordered metamaterials.Science advances · 2026Article
- Highly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Transforming Dental Care, Practice and Education with Additive Manufacturing and 3D Printing: Innovations in Materials, Technologies, and Future Pathways.Dentistry journal · 2025Review
- Mechanotransduction-Epigenetic Coupling in Pulmonary Regeneration: Multifunctional Bioscaffolds as Emerging Tools.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Machine Learning-Driven Grayscale Digital Light Processing for Mechanically Robust 3D-Printed Gradient Materials.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Deep active optimization for complex systems.Nature computational science · 2025Article
- Biomimetic structural design in 3D-printed scaffolds for bone tissue engineering.Materials today. Bio · 2025Review
- Ultrahigh Specific Strength by Bayesian Optimization of Carbon Nanolattices.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Bolstered bone regeneration by multiscale customized magnesium scaffolds with hierarchical structures and tempered degradation.Bioactive materials · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Architected materials that consist of multiple subelements arranged in particular orders can demonstrate a much broader range of properties than their constituent materials. However, the rational design of these materials generally relies on experts' prior knowledge and requires painstaking effort. Here, we present a data-efficient method for the high-dimensional multi-property optimization of 3D-printed architected materials utilizing a machine learning (ML) cycle consisting of the finite element method (FEM) and 3D neural networks. Specifically, we apply our method to orthopedic implant design. Compared to uniform designs, our experience-free method designs microscale heterogeneous architectures with a biocompatible elastic modulus and higher strength. Furthermore, inspired by the knowledge learned from the neural networks, we develop machine-human synergy, adapting the ML-designed architecture to fix a macroscale, irregularly shaped animal bone defect. Such adaptation exhibits 20% higher experimental load-bearing capacity than the uniform design. Thus, our method provides a data-efficient paradigm for the fast and intelligent design of architected materials with tailored mechanical, physical, and chemical properties.
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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.