ArticleScience advances2021
Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams.
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 21 papers.
What it found
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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
21 citing papers in PubMed.
- From Coprecipitation to Nanostructure: Formation, Morphology, and Reactive-Site Accessibility in Fe-Containing Binary Layered Double Hydroxides.Nanomaterials (Basel, Switzerland) · 2026Review
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Beyond Molecular Determinism: State-Convergent Polymerization as a Functional Design Principle Under Chemical Complexity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A user's guide to your first self-driving liquid handling lab.Digital discovery · 2026Article
- PhaseTransfer: A transfer learning framework for efficient phase diagram mapping.npj computational materials · 2026Article
- PhaseXplorer Creates High-Dimensional Phase Diagrams with Closed-Loop Active Learning.ACS nano · 2025Article
- Bayesian Optimization for Multicomponent Supramolecular Systems.Journal of the American Chemical Society · 2025Article
- AutoSAS: A new human-aside-the-loop paradigm for automated SAS fitting for high throughput and autonomous experimentation.APL machine learning · 2025Article
- Real-time experiment-theory closed-loop interaction for autonomous materials science.Science advances · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Frequency transfer and inverse design for metasurface under multi-physics coupling by Euler latent dynamic and data-analytical regularizations.Nature communications · 2025Article
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys.Materials (Basel, Switzerland) · 2024Article
- Review
- Emerging Trends in Machine Learning: A Polymer Perspective.ACS polymers Au · 2023Review
- Active learning-assisted neutron spectroscopy with log-Gaussian processes.Nature communications · 2023Article
- Review
- Upcoming Transformations in Integrated Energy/Chemicals Sectors: Some Challenges and Several Opportunities.The journal of physical chemistry. C, Nanomaterials and interfaces · 2022Review
- A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery. We demonstrate accelerated exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis using lateral gradient laser spike annealing and optical characterization along with a hierarchy of AI methods to map out processing phase diagrams. Efficient exploration of the multidimensional parameter space is achieved with nested active learning cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments and end-to-end uncertainty quantification. We demonstrate SARA’s performance by autonomously mapping synthesis phase boundaries for the Bi
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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.