通过分子模拟和机器学习的混合研究确定Gardner转变的非平衡临界性
Determining the nonequilibrium criticality of a Gardner transition via a hybrid study of molecular simulations and machine learning
摘要：
表观临界现象，通常由增长的相关长度和动力学慢化表示，在非平衡系统中普遍存在，例如过冷液体、非晶态固体、活性物质和自旋玻璃。确定这样的观察结果是否与平衡情况下的真正二阶相变相关，或者仅仅是交叉，并且测量相关的临界指数甚至更具有挑战性。在这里，我们表明，在三维硬球玻璃的模拟结果，是与最近的理论预测的加德纳转变，连续的非平衡相变是一致的。使用混合分子模拟-机器学习方法，我们获得了有限尺寸和老化效应的标度律，并确定了传统方法无法估计的临界指数。我们的研究提供了一种新的方法，是有用的，以了解玻璃化转变的性质，并可以推广到分析其他非平衡相变。
英文摘要：
Apparent critical phenomena, typically indicated by growing correlation lengths and dynamical slowing-down, are ubiquitous in non-equilibrium systems such as supercooled liquids, amorphous solids, active matter and spin glasses. It is often challenging to determine if such observations are related to a true second-order phase transition as in the equilibrium case, or simply a crossover, and even more so to measure the associated critical exponents. Here, we show that the simulation results of a hard-sphere glass in three dimensions, are consistent with the recent theoretical prediction of a Gardner transition, a continuous non-equilibrium phase transition. Using a hybrid molecular simulation-machine learning approach, we obtain scaling laws for both finite-size and aging effects, and determine the critical exponents that traditional methods fail to estimate. Our study provides a novel approach that is useful to understand the nature of glass transitions, and can be generalized to analyze other non-equilibrium phase transitions.
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