ArticleScience advances2021
Accelerated discovery of 3D printing materials using data-driven multiobjective optimization.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Accelerated discovery of thermostable mRNA-lipid nanoparticle vaccines using data-efficient AI.Nature biotechnology · 2026Article
- Accelerated Discovery of 3D Printing Calcium Sulphoaluminate Cement Composites Using Data-Driven Multi-Objective Optimization.Materials (Basel, Switzerland) · 2026Article
- AI-Guided 4D Printing of Carnivorous Plants-Inspired Microneedles for Accelerated Wound Healing.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Machine learning guided formulation design of digital light processing printable elastomers beyond viscosity stretchability tradeoff.Nature communications · 2026Article
- Machine Learning-Assisted Bio-Interfacial Engineering Resolves Structural-Functional Conflicts in Nanocomposites.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes.Chemical science · 2026Article
- Integrating Decision Trees and Clustering for Efficient Optimization of Bioink Rheology and 3D Bioprinted Construct Microenvironments.Journal of manufacturing science and engineering · 2025Article
- Deep Learning-Based Super Resolution Applied to Finite Element Analysis of Fused Deposition Modeling 3D Printing.3D printing and additive manufacturing · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- NeuroDISK: An AI Approach to Automate Continuous Inquiry-Driven Discoveries in Neuroimaging Genetics.bioRxiv : the preprint server for biology · 2025Article
- Characterization and Machine Learning-Driven Property Prediction of a Novel Hybrid Hydrogel Bioink Considering Extrusion-Based 3D Bioprinting.Gels (Basel, Switzerland) · 2025Article
- Artificial intelligence-assisted organoid construction in congenital heart disease: current applications and future prospects.Frontiers in bioengineering and biotechnology · 2025Review
- Hybrid diacrylate resin-gelatin methacryloyl composite with bone-to-brain stiffness range.Communications materials · 2025Article
- Recent advances and applications of artificial intelligence in 3D bioprinting.Biophysics reviews · 2024Review
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research.Micromachines · 2024Review
- Intelligent Optimization Method of Piezoelectric Ejection System Design Based on Finite Element Simulation and Neural Network.3D printing and additive manufacturing · 2024Article
- Machine-Guided Discovery of Acrylate Photopolymer Compositions.ACS applied materials & interfaces · 2024Article
- Machine learning-enabled constrained multi-objective design of architected materials.Nature communications · 2023Article
- "Freedom of design" in chemical compound space: towards rationalChemical science · 2023Article
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
No grant is acknowledged in the PubMed record.
Abstract
Additive manufacturing has become one of the forefront technologies in fabrication, enabling products impossible to manufacture before. Although many materials exist for additive manufacturing, most suffer from performance trade-offs. Current materials are designed with inefficient human-driven intuition-based methods, leaving them short of optimal solutions. We propose a machine learning approach to accelerating the discovery of additive manufacturing materials with optimal trade-offs in mechanical performance. A multiobjective optimization algorithm automatically guides the experimental design by proposing how to mix primary formulations to create better performing materials. The algorithm is coupled with a semiautonomous fabrication platform to substantially reduce the number of performed experiments and overall time to solution. Without prior knowledge of the primary formulations, the proposed methodology autonomously uncovers 12 optimal formulations and enlarges the discovered performance space 288 times after only 30 experimental iterations. This methodology could be easily generalized to other material design systems and enable automated discovery.
Identifiers
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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.