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Unsupervised machine learning for detection of phase transitions in off-lattice systems. II. Applications
R B Jadrich1, B A Lindquist1, W D Piñeros2
1McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA.
Abstract:
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.
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