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

We outline how principal component analysis 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...

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Published inThe Journal of chemical physics Vol. 149; no. 19; p. 194110
Main Authors Jadrich, R B, Lindquist, B A, Piñeros, W D, Banerjee, D, Truskett, T M
Format Journal Article
LanguageEnglish
Published United States 21.11.2018
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Summary:We outline how principal component analysis 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) a compositionally driven demixing transition in the non-additive binary Widom-Rowlinson mixture.
ISSN:1089-7690
DOI:10.1063/1.5049850