Berthier Ludovic, Ghimenti Federico, van Wijland Frédéric
Laboratoire Charles Coulomb (L2C), Université de Montpellier & CNRS (UMR 5221), 34095 Montpellier, France.
Gulliver, UMR CNRS 7083, ESPCI Paris, PSL Research University, 75005 Paris, France.
J Chem Phys. 2024 Sep 21;161(11). doi: 10.1063/5.0225978.
Monte Carlo simulations are widely employed to measure the physical properties of glass-forming liquids in thermal equilibrium. Combined with local Monte Carlo moves, the Metropolis algorithm can also be used to simulate the relaxation dynamics, thus offering an efficient alternative to molecular dynamics. Monte Carlo simulations are, however, more versatile because carefully designed Monte Carlo algorithms can more efficiently sample the rugged free energy landscape characteristic of glassy systems. After a brief overview of Monte Carlo studies of glass-formers, we define and implement a series of Monte Carlo algorithms in a three-dimensional model of polydisperse hard spheres. We show that the standard local Metropolis algorithm is the slowest and that implementing collective moves or breaking detailed balance enhances the efficiency of the Monte Carlo sampling. We use time correlation functions to provide a microscopic interpretation of these observations. Seventy years after its invention, the Monte Carlo method remains the most efficient and versatile tool to compute low-temperature properties in supercooled liquids.
蒙特卡罗模拟被广泛用于测量处于热平衡状态的玻璃形成液体的物理性质。结合局部蒙特卡罗移动， metropolis算法也可用于模拟弛豫动力学，从而为分子动力学提供了一种有效的替代方法。然而，蒙特卡罗模拟更具通用性，因为精心设计的蒙特卡罗算法可以更有效地对玻璃态系统特有的崎岖自由能景观进行采样。在简要概述了玻璃形成体的蒙特卡罗研究之后，我们在多分散硬球的三维模型中定义并实现了一系列蒙特卡罗算法。我们表明，标准的局部 metropolis算法是最慢的，而实施集体移动或打破细致平衡可提高蒙特卡罗采样的效率。我们使用时间关联函数对这些观察结果进行微观解释。在发明七十年后，蒙特卡罗方法仍然是计算过冷液体低温性质的最有效和最通用的工具。
