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Unsupervised machine learning for detection of phase transitions in off-lattice systems. I. Foundations.
- Source :
-
Journal of Chemical Physics . 11/21/2018, Vol. 149 Issue 19, pN.PAG-N.PAG. 9p. 6 Graphs. - Publication Year :
- 2018
-
Abstract
- We demonstrate the utility of an unsupervised machine learning tool for the detection of phase transitions in off-lattice systems. We focus on the application of principal component analysis (PCA) to detect the freezing transitions of two-dimensional hard-disk and three-dimensional hard-sphere systems as well as liquid-gas phase separation in a patchy colloid model. As we demonstrate, PCA autonomously discovers order-parameter-like quantities that report on phase transitions, mitigating the need for a priori construction or identification of a suitable order parameter—thus streamlining the routine analysis of phase behavior. In a companion paper, we further develop the method established here to explore the detection of phase transitions in various model systems controlled by compositional demixing, liquid crystalline ordering, and non-equilibrium active forces. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00219606
- Volume :
- 149
- Issue :
- 19
- Database :
- Academic Search Index
- Journal :
- Journal of Chemical Physics
- Publication Type :
- Academic Journal
- Accession number :
- 133149076
- Full Text :
- https://doi.org/10.1063/1.5049849