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

TL;DR

This paper demonstrates how principal component analysis (PCA) can be used to detect various phase transitions in off-lattice systems, including nonequilibrium and equilibrium cases, by analyzing particle configuration data.

Contribution

It extends PCA application to off-lattice systems, showing its effectiveness in identifying diverse phase transitions in both equilibrium and nonequilibrium conditions.

Findings

PCA detects phase transitions in the RandOrg model.

PCA identifies orientational and positional transitions in hard ellipses.

PCA reveals demixing transitions in binary mixtures.

Abstract

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.