Unsupervised machine learning for detection of phase transitions in off-lattice systems. II. Applications.
J Chem Phys
; 149(19): 194110, 2018 Nov 21.
Article
em En
| MEDLINE
| ID: mdl-30466276
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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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article