Department of Chemistry, Brown University, Providence, Rhode Island 02912, USA.
J Chem Phys. 2011 Oct 7;135(13):134111. doi: 10.1063/1.3643325.
We describe a new approach to the rare-event Monte Carlo sampling problem. This technique utilizes a symmetrization strategy to create probability distributions that are more highly connected and, thus, more easily sampled than their original, potentially sparse counterparts. After discussing the formal outline of the approach and devising techniques for its practical implementation, we illustrate the utility of the technique with a series of numerical applications to Lennard-Jones clusters of varying complexity and rare-event character.
我们描述了一种新的稀有事件蒙特卡罗抽样问题的方法。该技术利用对称化策略来创建更高度连接的概率分布，从而比原始的、潜在稀疏的分布更容易抽样。在讨论了该方法的正式框架并设计了其实践实现技术之后，我们通过一系列数值应用程序说明了该技术在不同复杂程度和稀有事件特征的 Lennard-Jones 团簇中的实用性。
