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arXiv 2603.09027cond-mat.soft
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黏性液体的动力学与随机障碍模型

Dynamics of viscous liquids and the Random Barrier Model

Roskilde University(罗斯基勒大学);Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris(巴黎高等师范学院物理实验室,ENS,巴黎文理研究大学,法国国家科学研究中心,索邦大学,巴黎大学)

Thomas B. Schrøder, Jeppe C. Dyre, Camille Scalliet

AI总结:

本文利用粒子交换蒙特卡洛算法与长GPU分子动力学模拟,研究三元Lennard-Jones玻璃形成液体在极端粘性区域的动力学,比较随机障碍模型与von Schweidler定律对内在均方位移的拟合效果。

摘要:

本文结合粒子交换蒙特卡洛算法与长GPU分子动力学模拟,分析三元Lennard-Jones玻璃形成液体在极端粘性区域的动力学。重点研究通过将配置空间轨迹淬火得到的内在动力学。比较两种函数形式,即在极端无序极限下von Schweidler定律和随机障碍模型(RBM)对时间函数内在均方位移数据的拟合情况。发现RBM在无无量纲自由参数的情况下,比具有一个无量纲自由参数的von Schweidler定律更准确地拟合数据。特别是,这表明RBM能更准确地从短时间模拟数据预测扩散系数。仍是一个开放问题,为何RBM能良好拟合内在数据,尽管该模型(不现实)假设所有能量最小值相同。

英文摘要:

This paper combines the particle-swap Monte Carlo algorithm with long GPU molecular dynamics simulations to analyze the dynamics of a ternary Lennard-Jones glass-forming liquid in the extremely viscous regime. The focus is on the inherent dynamics, obtained by quenching configurations along the configuration-space trajectory into their inherent state. We compare how two functional forms, the von Schweidler law and the random barrier model (RBM) prediction in the extreme disorder limit, fit data for the inherent mean-squared displacement as a function of time. We find that the RBM, which has no dimensionless free parameters, generally fits the data better than the von Schweidler law, despite the latters one dimensionless free parameter. In particular, this implies that the RBM predicts the value of the diffusion coefficient from short-time simulation data more accurately than does the von Schweidler expression. It remains an open question why the RBM reproduces well the inherent data despite this models (unrealistic) assumption of identical energy minima.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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