Generalized-ensemble algorithms for molecular dynamics simulations
Generalized-ensemble algorithms for molecular dynamics simulations
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DOI：
10.1080/08927020601096812
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发表时间：
2007-01
影响因子：
2.1
通讯作者：
S. Itoh;H. Okumura;Y. Okamoto
中科院分区：
文献类型：
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作者：
S. Itoh;H. Okumura;Y. Okamoto
在具有多个自由度的复杂系统中，例如生物分子系统，正则系综或等压等温系综中的传统蒙特卡罗和分子动力学模拟会遇到多重极小值问题，导致陷入能量局部极小值状态。广义系综中的模拟在指定变量中执行随机游走并克服了这一困难。在本文中，我们回顾了广义集成算法。首先描述多规范算法。该方法实现了势能空间中的随机游走，模拟可以避免多重极小问题。然后，我们提出了两种新的广义集成算法，即多重重叠算法和多元-多热算法，它们是多规范算法的多变量/多维扩展。前一种方法实现了重叠空间中的随机游走，后一种方法实现了势能空间和体积空间中的随机游走。重点在于这些算法的分子动力学版本的描述。
In complex systems with many degrees of freedom such as biomolecular systems, conventional Monte Carlo and molecular dynamics simulations in canonical ensemble or isobaric–isothermal ensemble suffer from the multiple-minima problem, resulting in entrapment in states of energy local minima. A simulation in generalized ensemble performs a random walk in specified variables and overcomes this difficulty. In this article we review the generalized-ensemble algorithms. Multicanonical algorithm is described first. In this method, a random walk in potential energy space is realized and the simulation can avoid the multiple-minima problem. We then present two new generalized-ensemble algorithms, namely multioverlap algorithm and multibaric–multithermal algorithm, which are multi-variable/multi-dimensional extensions of the multicanonical algorithm. In the former method, a random walk in overlap space is realized, and in the latter that in both potential energy space and volume space is obtained. Emphasis is laid in the description of the molecular dynamics versions of these algorithms.