ArticleACS nano2025
PhaseXplorer Creates High-Dimensional Phase Diagrams with Closed-Loop Active Learning.
Article in ACS nano, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Navigating Lipid Nanostructure Design Space Through Continuous Microfluidic Automation.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- AI-Powered, Temperature-Resolved Centrifugal Microfluidics for Rapid 3D Phase-Diagram Generation of Biomolecular Condensates.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Oppositely Charged Single Enzyme Nanogels Form Versatile Coacervates for Efficient Enzyme Cascade Catalysis.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- PhaseTransfer: A transfer learning framework for efficient phase diagram mapping.npj computational materials · 2026Article
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
Phase separation fundamentally governs material properties and cellular function on multiple organizational scales. Conventional approaches to studying this nonlinear phenomenon necessitate resource-intensive experiments. As such, investigations were limited to low-dimensional space. We present PhaseXplorer, a platform that combines microfluidics, microscopy, and machine learning to efficiently study phase separation systems. PhaseXplorer autonomously designs, generates, and analyzes samples in a closed-loop active learning workflow until an accurate phase diagram is obtained. Using an acquisition function that balances exploration and exploitation, all the phase boundaries are located with minimal sampling. A convolutional neural network executes real-time image recognition to identify microfluidic droplets and phase separation within them in less than 1 ms per droplet. PhaseXplorer standardizes analysis across experiments and does not require calibration nor extensive postexperiment analysis. We demonstrate PhaseXplorer's capabilities using a poly rA model system by creating a four-dimensional phase diagram 100 times faster than traditional methods while simultaneously consuming 10,000 times less material.
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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.