Unsupervised machine learning for detection of phase transitions in off-lattice systems. I. Foundations
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 syste...
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| Published in | The Journal of chemical physics Vol. 149; no. 19; p. 194109 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
21.11.2018
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| Online Access | Get more information |
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| Summary: | 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
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. |
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| ISSN: | 1089-7690 |
| DOI: | 10.1063/1.5049849 |