Data-driven criterion for the solid-liquid transition of two-dimensional self-propelled colloidal particles far from equilibrium

TL;DR

This paper introduces a hybrid machine learning method to accurately identify the solid-liquid transition in two-dimensional self-propelled colloidal particles far from equilibrium, surpassing traditional empirical criteria.

Contribution

It develops a data-driven criterion and evaluation function that improve phase transition predictions in nonequilibrium systems using combined unsupervised and supervised learning.

Findings

Identified a new nonequilibrium threshold for diffusion coefficient.

Enhanced accuracy of empirical criteria in far-from-equilibrium regimes.

Provided a generic tool for phase transition analysis in complex systems.

Abstract

We establish an explicit data-driven criterion for identifying the solid-liquid transition of two-dimensional self-propelled colloidal particles in the far from equilibrium parameter regime, where the transition points predicted by different conventional empirical criteria for melting and freezing diverge. This is achieved by applying a hybrid machine learning approach that combines unsupervised learning with supervised learning to analyze over one million of the system's configurations in the nonequilibrium parameter regime. Furthermore, we establish a generic data-driven evaluation function, according to which the performance of different empirical criteria can be systematically evaluated and improved. In particular, by applying this evaluation function, we identify a new nonequilibrium threshold value for the long-time diffusion coefficient, based on which the predictions of the corresponding empirical criterion are greatly improved in the far from equilibrium parameter regime. These data-driven approaches provide a generic tool for investigating phase transitions in complex systems where conventional empirical ones face difficulties.