Unsupervised machine learning for detection of phase transitions in off-lattice systems II. Applications

We outline how principal component analysis (PCA) can be applied to particle configuration data to detect a variety of phase transitions in off-lattice systems, both in and out of equilibrium. Specifically, we discuss its application to study 1) the nonequilibrium random organization (RandOrg) model...

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Bibliographic Details
Published inarXiv.org
Main Authors Jadrich, R B, Lindquist, B A, Pineros, W D, Banerjee, D, Truskett, T M
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 31.07.2018
Subjects
Demixing
Machine learning
Phase transitions
Physics - Computational Physics
Physics - Statistical Mechanics
Principal components analysis
Unsupervised learning
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Summary:We outline how principal component analysis (PCA) can be applied to particle configuration data to detect a variety of phase transitions in off-lattice systems, both in and out of equilibrium. Specifically, we discuss its application to study 1) the nonequilibrium random organization (RandOrg) model that exhibits a phase transition from quiescent to steady-state behavior as a function of density, 2) orientationally and positionally driven equilibrium phase transitions for hard ellipses, and 3) compositionally driven demixing transitions in the non-additive binary Widom-Rowlinson mixture.
ISSN:2331-8422
DOI:10.48550/arxiv.1808.00083