Unsupervised machine learning for detection of phase transitions in off-lattice systems I. Foundations

R. B. Jadrich; B. A. Lindquist; T. M. Truskett

doi:10.1063/1.5049849

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arXiv · 1808.00084

Unsupervised machine learning for detection of phase transitions in off-lattice systems I. Foundations

R. B. Jadrich·B. A. Lindquist·T. M. Truskett

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Abstract

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.

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R. B. Jadrich, B. A. Lindquist, T. M. Truskett. 2018-07-31. Unsupervised machine learning for detection of phase transitions in off-lattice systems I. Foundations. https://doi.org/10.1063/1.5049849

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