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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Bibliographic Details
Published inarXiv.org
Main Authors Jadrich, R B, Lindquist, B A, Truskett, T M
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 31.07.2018
Subjects
Artificial intelligence
Demixing
Freezing
Liquid crystals
Machine learning
Mathematical models
Order parameters
Parameter identification
Phase separation
Phase transitions
Physics - Computational Physics
Physics - Statistical Mechanics
Principal components analysis
Streamlining
Unsupervised learning
Vapor phases
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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 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.
ISSN:2331-8422
DOI:10.48550/arxiv.1808.00084