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Machine Learning-Assisted Phase Diagram Determination in Aqueous Two-Phase Systems
Nidhin Thomas1, Adam Witmer2, Hyung Kae Lee3
1Theoretical Division, Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
Langmuir : the ACS Journal of Surfaces and Colloids
|April 30, 2026
Summary
This study introduces an automated method using microscopy and machine learning for accurate phase diagram construction in aqueous two-phase systems (ATPSs). This overcomes limitations of traditional methods, enabling efficient characterization of phase separation.
Area of Science:
- Physical Chemistry
- Materials Science
- Biotechnology
Background:
- Accurate phase diagrams for aqueous two-phase systems (ATPSs) are crucial for downstream applications.
- Conventional turbidimetric titration methods for ATPS phase diagram construction are labor-intensive and prone to inaccuracies.
Purpose of the Study:
- To develop an automated methodology for precise phase diagram construction in ATPSs.
- To overcome the limitations of traditional experimental techniques for characterizing phase separation.
Main Methods:
- Integration of optical microscopy with machine learning for automated phase separation detection.
- Utilizing a segmentation model (SAM 2) to analyze thousands of images and quantify emulsion droplets.
- Employing supervised machine learning on droplet features for robust classification of one- or two-phase regions.
Main Results:
- Successfully identified two-phase regions for a challenging ATPS (poly(ethylene glycol)/dextran) with slow phase separation kinetics.
- Demonstrated accurate phase boundary definition, overcoming issues with dim or occluded droplet edges.
- Validated the efficiency and robustness of the automated platform for phase diagram construction.
Conclusions:
- The developed automated strategy offers a significant advancement over conventional methods for ATPS phase diagram construction.
- This approach provides a reliable and efficient platform for characterizing phase-separating systems.
- Potential for broad applications in various fields requiring precise phase behavior analysis.
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