Data-driven criterion for the solid-liquid transition of two-dimensional self-propelled colloidal particles far from equilibrium
| Autor: | Wei-chen Guo, Bao-quan Ai, Liang He |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Phase transition
Statistical Mechanics (cond-mat.stat-mech) Supervised learning Complex system Non-equilibrium thermodynamics FOS: Physical sciences Condensed Matter - Soft Condensed Matter Evaluation function Data-driven Unsupervised learning Soft Condensed Matter (cond-mat.soft) Statistical physics Diffusion (business) Condensed Matter - Statistical Mechanics Mathematics |
| DOI: | 10.48550/arxiv.2102.12106 |
| Popis: | 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. Comment: 9 pages, 6 figures |
| Databáze: | OpenAIRE |
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