PhaseXplorer Creates High-Dimensional Phase Diagrams with Closed-Loop Active Learning.

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 mi

SUBMITTER: Jansen SAH 

PROVIDER: S-EPMC12632172 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature