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Combining smart darting with parallel tempering using Eckart space: Application to Lennard-Jones clusters

机译:使用Eckart空间将智能飞镖与平行回火相结合:应用于Lennard-Jones群集

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The smart-darting algorithm is a Monte Carlo based simulation method used,to overcome quasiergodicity problems associated with disconnected regions of configurations space separated by high energy barriers. As originally implemented, the smart-darting method works well for clusters at low temperatures with the angular-momentum restricted to zero and where there are no transitions to permutational isomers. If the rotational motion of the clusters is unrestricted or if permutational isomerization becomes important, the acceptance probability of darting moves in the original implementation of the method becomes vanishingly small. In this work the smart-darting algorithm is combined with the, parallel tempering method in a manner where both rotational motion and permutational isomerization events are important. To enable the-combination of parallel tempering with smart darting so that the smart-darting moves have a reasonable acceptance probability, the original algorithm is,modified by using a restricted space for the smart-darting moves. The restricted space uses a body-fixed coordinate system first introduced by Eckart, and moves in this Eckart space are coupled with local moves in the full 3N-dimensional space. The modified smart-darting method,is applied to the calculation of the heat capacity of a seven-atom Lennard-lones cluster. The smart-darting moves yield significant improvement in the statistical fluctuations of the calculated heat capacity in the region of temperatures where the system isomerizes. When the modified smart-darting algorithm is combined with parallel tempering, the statistical fluctuations of the heat capacity of a seven-atom Lennard-Jones cluster using the combined method are smaller than parallel tempering when used alone. (c) 2005 American Institute of Physics.
机译:智能飞镖算法是一种基于Monte Carlo的仿真方法,用于克服与由高能垒分隔的配置空间的不连续区域相关的拟整数问题。如最初实施的那样,智能飞镖方法适用于低温动量,其角动量限制为零,并且没有过渡到排列异构体的情况。如果团簇的旋转运动不受限制,或者如果排列异构化变得重要,则在该方法的原始实现中,飞镖运动的接受概率将变得很小。在这项工作中,智能飞镖算法与并行回火方法相结合,使得旋转运动和排列异构化事件都很重要。为了使并行回火与智能飞镖相结合,从而使智能飞镖运动具有合理的接受概率,通过对智能飞镖运动使用有限的空间来修改原始算法。受限空间使用的是由Eckart首次引入的身体固定坐标系,该Eckart空间中的移动与整个3N维空间中的局部移动耦合。将改进的智能飞镖方法应用于七原子伦纳德-朗涅斯簇的热容计算。在系统异构化的温度范围内,智能飞镖移动可显着改善计算出的热容量的统计波动。当将改进的智能飞镖算法与并行回火结合使用时,使用这种组合方法的七原子Lennard-Jones团簇的热容的统计波动小于单独使用时的并行回火。 (c)2005年美国物理研究所。

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