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Unsupervised machine learning for detection of phase transitions in off-lattice systems. I. Foundations
R B Jadrich1, B A Lindquist1, T M Truskett1
1McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA.
The Journal of Chemical Physics
|November 24, 2018
Summary
We show how unsupervised machine learning, specifically principal component analysis (PCA), can automatically detect phase transitions in complex systems. This method eliminates the need to pre-define order parameters for analyzing material behavior.
Area of Science:
- Computational physics
- Materials science
- Machine learning
Background:
- Phase transitions are critical phenomena in materials science.
- Identifying phase transitions often requires defining specific order parameters.
- Analyzing complex off-lattice systems can be challenging.
Purpose of the Study:
- To demonstrate an unsupervised machine learning tool for detecting phase transitions.
- To apply principal component analysis (PCA) to various off-lattice systems.
- To streamline the analysis of phase behavior by removing the need for a priori order parameter identification.
Main Methods:
- Utilized unsupervised machine learning, focusing on principal component analysis (PCA).
- Applied PCA to detect freezing transitions in 2D hard-disk and 3D hard-sphere systems.
- Analyzed liquid-gas phase separation in a patchy colloid model.
Main Results:
- PCA autonomously discovered order-parameter-like quantities.
- These quantities effectively reported on the occurrence of phase transitions.
- The method proved effective for systems without pre-defined order parameters.
Conclusions:
- Unsupervised machine learning, particularly PCA, is a powerful tool for phase transition detection.
- This approach simplifies the analysis of phase behavior in diverse systems.
- The method paves the way for analyzing more complex phase transitions in future studies.
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