ArticleNature communications2020
On-the-fly closed-loop materials discovery via Bayesian active learning.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 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.
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Who cites it
89 citing papers in PubMed.
- Artificial intelligence-assisted design of self-assembling peptide hydrogels for neural regeneration: Principles and opportunities.Bioactive materials · 2027Review
- Interpretable Bayesian optimization for catalyst discovery.Faraday discussions · 2026Article
- Induced pluripotent stem cells from discovery to translation.Nature medicine · 2026Review
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions.Materials (Basel, Switzerland) · 2026Review
- Interpretable self-driving sputtering epitaxy reveals human-usable growth rules for β-GaNature communications · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- High-Accuracy Machine Learning Projections of Composition-Dependent Thermal Stability in Halide Perovskites.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- The past, present and future of self-driving laboratories.Nature reviews. Chemistry · 2026Review
- ASCENT: an active transfer learning paradigm for efficient drug-target interaction prediction.Science China. Life sciences · 2026Article
- Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- Explicitly Incorporating Prior Knowledge into Bayesian Optimization for Materials Design.ACS omega · 2026Article
- Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design.Molecules (Basel, Switzerland) · 2026Review
- Noise-Aware Machine Learning Accelerates Development of High-Latent-Heat Cu-Al-Ni Shape Memory Alloys for Thermal Management.Materials (Basel, Switzerland) · 2026Article
- Deciphering key factors of active learning performance in biomolecular design.Bioinformatics (Oxford, England) · 2026Article
- PPO-GPR: A Custom Proximal Policy Optimization Tool for Active Reinforcement Learning.ACS engineering Au · 2026Article
- Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- Efficient material selection for training occluded mmWave radar-based gesture recognisers.Scientific reports · 2026Article
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- SPACESHIP: Autonomous Mapping of Hardware-Dependent Synthesizable Space in Solution-Phase Gold Nanomaterials.Journal of the American Chemical Society · 2026Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Active learning-the field of machine learning (ML) dedicated to optimal experiment design-has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.
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.