利用无监督机器学习检测脱钉和非平衡转变
Detecting Depinning and Nonequilibrium Transitions with Unsupervised Machine Learning
AI总结：
通过机器学习序参量检测模型圆盘系统中的脱钉转变及不同动态流动相，并识别传统方法难以区分的液固相分离，结果适用于广泛粒子系统。
摘要：
通过对模型圆盘系统的数值模拟，我们证明机器学习生成的序参量能够检测远非平衡驱动系统中的脱钉转变和不同的动态流动相。我们特别考虑了具有短程相互作用的单分散被动圆盘，在淬火无序驱动下经历脱钉相变。机器学习推导的序参量识别了脱钉转变以及不同的动力学区域，例如从流动液体到相分离的液固态的转变，这一转变难以通过传统度量如速度-力曲线或Voronoi镶嵌来轻易区分。该序参量在高密度极限下也表现出显著不同的行为，此时发生堵塞效应。我们的结果应适用于表现出脱钉转变和非平衡相变的广泛粒子基系统类别。
英文摘要：
Using numerical simulations of a model disk system, we demonstrate that a machine learning generated order parameter can detect depinning transitions and different dynamic flow phases in systems driven far from equilibrium. We specifically consider monodisperse passive disks with short range interactions undergoing a depinning phase transition when driven over quenched disorder. The machine learning derived order parameter identifies the depinning transition as well as different dynamical regimes, such as the transition from a flowing liquid to a phase separated liquid-solid state that is not readily distinguished with traditional measures such as velocity-force curves or Voronoi tessellation. The order parameter also shows markedly distinct behavior in the limit of high density where jamming effects occur. Our results should be general to the broad class of particle-based systems that exhibit depinning transitions and nonequilibrium phase transitions.
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