ArticleNature communications2016
Accelerated search for materials with targeted properties by adaptive design.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers.
What it found
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Who cites it
104 citing papers in PubMed.
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- TiZrHf-Based B2-B19'/B19-High Entropy Shape Memory Alloys: A Review and Recent Advances.Materials (Basel, Switzerland) · 2026Review
- High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength.Nature communications · 2026Article
- Noise-Aware Machine Learning Accelerates Development of High-Latent-Heat Cu-Al-Ni Shape Memory Alloys for Thermal Management.Materials (Basel, Switzerland) · 2026Article
- Optimization of print parameters for batch and continuous manufacturing of three-dimensional (3D) printed dosage forms using artificial intelligence and machine learning.Drug delivery and translational research · 2026Article
- Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Efficient quantum algorithm for the design of complex materials: quantum circuit learning.Scientific reports · 2026Article
- NSGA-II and Powell Optimized Inverse Design of MAO Coatings.ACS omega · 2026Article
- Modelling and estimation of chemical reaction yields from high-throughput experiments.Communications chemistry · 2026Article
- Active learning for the accelerated discovery of complex concentrated NiCoCr alloys in additive manufacturing.NPJ advanced manufacturing · 2026Article
- Applications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries.ACS omega · 2025Review
- Enhancing Reverse Design Ability of Functional Materials Based on Data Quality Management: Taking Biomedical Zinc Alloy as an Example.Materials (Basel, Switzerland) · 2025Article
- Active learning accelerates electrolyte solvent screening for anode-free lithium metal batteries.Nature communications · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Machine-Learning-Guided Design of Nanostructured Metal Oxide Photoanodes for Photoelectrochemical Water Splitting: From Material Discovery to Performance Optimization.Nanomaterials (Basel, Switzerland) · 2025Review
- Synergistic Machine Learning Guided Discovery of ABaAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material Designs.The journal of physical chemistry letters · 2025Article
- Application of Machine Learning in Amorphous Alloys.Materials (Basel, Switzerland) · 2025Review
44 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2 possessing the smallest ΔT (1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔT than any of the 22 in the original data set.
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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.