ArticleScience advances2025
Real-time experiment-theory closed-loop interaction for autonomous materials science.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Materials for liver-on-a-chip: modules, properties, and future directions.Mikrochimica acta · 2026Review
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception.Scientific reports · 2026Article
- PhaseTransfer: A transfer learning framework for efficient phase diagram mapping.npj computational materials · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Iterative cycles of theoretical prediction and experimental validation are the cornerstone of the modern scientific method. However, the proverbial "closing of the loop" in experiment-theory cycles in practice is usually ad hoc and often inherently difficult, beset by the scale or time constraint of computation or phenomena. Here, we demonstrate autonomous materials search engine (AMASE), where self-driving continuous cyclical interaction of experiments and computational predictions is performed for materials exploration. We have applied this formalism to rapid mapping of a temperature-composition phase diagram. Experimental determination of phase boundaries in thin films is autonomously interspersed with real-time updating of phase diagram prediction using CALPHAD. AMASE was able to accurately determine the eutectic phase diagram of the Sn-Bi thin-film system from a self-guided campaign covering just a small fraction of the phase space, translating to a sixfold reduction in the number of experiments. This study demonstrates real-time, autonomous, and iterative interactions of experiments and theory carried out without any human intervention.
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Registered trials
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