.png){kind=icon}
返回
Optimization of expanded ensemble methods
.png){kind=icon}
PRE
AI
DOI：10.1063/1.2994717.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Expanded ensemble methods, designed to sample a range of an order parameter lambda of interest, can be optimized to overcome the difficulties associated with traversing large free-energy barriers or rugged landscapes. The optimization strategy of Trebst [Phys. Rev. E 70, 046701 (2004)] is based on finding suitable biasing weights for inter-lambda transitions that maximize the number of round trips that the system performs between the lower and upper lambda bounds. In this work, this optimized-ensemble methodology is extended by finding weights that minimize the mean round-trip time tau (between the lambda end states) for a Markovian walk. Applications are presented for an atomistically detailed model and for systems where one needs to sample a wide range of concentrations or compositions. A less rigorous method that implements a dual tau minimization (for both upward and downward trajectories) is found to be harder to converge but produce more round trips than a method based on a single tau minimization for all trajectories. While the proposed methods do not always minimize the true tau, they have performances that are either similar or better than those of the original optimized-ensemble method and provide useful information to characterize deviations from Markovian dynamics in the sampling of the lambda space. (C) 2008 American Institute of Physics. [DOI: 10.1063/1.2994717]
Keyword:
MONTE-CARLO-SIMULATION
FREE-ENERGY
EQUILIBRIA
EFFICIENT
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.1
论文数:
7.2W
被引数:
23.2W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容