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Title: Unsupervised machine learning for detection of phase transitions in off-lattice systems. I. Foundations
We demonstrate the utility of an unsupervised machine learning tool for the detection of phase transitions in off-lattice systems. We focus on the application of principal component analysis (PCA) to detect the freezing transitions of two-dimensional hard-disk and three-dimensional hard-sphere systems as well as liquid-gas phase separation in a patchy colloid model. As we demonstrate, PCA autonomously discovers order-parameter-like quantities that report on phase transitions, mitigating the need for a priori construction or identification of a suitable order parameter—thus streamlining the routine analysis of phase behavior. In a companion paper, we further develop the method established here to explore the detection of phase transitions in various model systems controlled by compositional demixing, liquid crystalline ordering, and non-equilibrium active forces.  more » « less
Award ID(s):
1720595
PAR ID:
10476192
Author(s) / Creator(s):
; ;
Publisher / Repository:
AIP Publishing
Date Published:
Journal Name:
The Journal of Chemical Physics
Volume:
149
Issue:
19
ISSN:
0021-9606
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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