论文标题
通过分子模拟和机器学习的混合研究确定Gardner过渡的非平衡关键性
Determining the nonequilibrium criticality of a Gardner transition via a hybrid study of molecular simulations and machine learning
论文作者
论文摘要
显而易见的关键现象通常由相关长度和动态减速降低表示，在非平衡系统（例如超冷液体，无定形固体，活跃物质和自旋玻璃）中无处不在。确定此类观察结果是否与均衡情况一样，还是简单的交叉，甚至更大的是测量相关的关键指数，通常是具有挑战性的。在这里，我们表明，在三个维度上硬球玻璃的仿真结果与Gardner Transition的最新理论预测是一致的，Gardner Transition是连续的非平衡相变。使用混合分子模拟机学习方法，我们获得了有限大小和衰老效应的缩放定律，并确定传统方法无法估算的关键指数。我们的研究提供了一种新颖的方法，可用于了解玻璃过渡的性质，并可以推广以分析其他非平衡相变。
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.
