Unsupervised machine learning for detection of phase transitions in off-lattice systems II. Applications
Computational Physics
2018-12-07 v1 Statistical Mechanics
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.
Keywords
Cite
@article{arxiv.1808.00083,
title = {Unsupervised machine learning for detection of phase transitions in off-lattice systems II. Applications},
author = {R. B. Jadrich and B. A. Lindquist and W. D. Pineros and D. Banerjee and T. M. Truskett},
journal= {arXiv preprint arXiv:1808.00083},
year = {2018}
}