arXiv 0811.18292008-11-12cond-mat.stat-mech

用于计算大系统态密度和增强采样的快速自适应平坦直方图增强法

Fast Adaptive Flat-histogram Ensemble for Calculating Density of States and Enhanced Sampling in Large Systems

Xin Zhou, Yi Jiang

备注：: 5 pages, 4 figures

摘要：

提出了一种快速自适应平坦直方图集成（FAFE）算法，用于估计大系统的态密度（DOS）和增强采样。FAFE计算广义系综中任意扩展变量$U$的平均值，以形成曲线$β_{s}(U) \equiv \frac{\partial S(U)}{\partial U}$上的点，即对数DOS的导数。FAFE方法不同于Wang-Landau-like（WLL）方法，它在整个仿真过程中满足详细平衡条件，并自动生成非均匀的$(β_{i}, U_{i})$数据点，以跟踪不同$U$区域和不同系统中$β_{s}(U)$的真实变化率。结合$U-$压缩转换，FAFE将所需的仿真步骤从WLL中的$O(N^{3/2})$减少到$O(N^{1/2})$，其中$N$是系统大小。我们证明了FAFE在Lennard-Jones液体中的效率，具有几个$N$值。更重要的是，我们展示了它在发现和识别不同的宏观状态，包括亚稳态相共存区域的能力。

英文摘要：

We presented an efficient algorithm, fast adaptive flat-histogram ensemble (FAFE), to estimate the density of states (DOS) and to enhance sampling in large systems. FAFE calculates the means of an arbitrary extensive variable $U$ in generalized ensembles to form points on the curve $β_{s}(U) \equiv \frac{\partial S(U)}{\partial U}$, the derivative of the logarithmic DOS. Unlike the popular Wang-Landau-like (WLL) methods, FAFE satisfies the detailed-balance condition through out the simulation and automatically generates non-uniform $(β_{i}, U_{i})$ data points to follow the real change rate of $β_{s}(U)$ in different $U$ regions and in different systems. Combined with a $U-$compression transformation, FAFE reduces the required simulation steps from $O(N^{3/2})$ in WLL to $O(N^{1/2})$, where $N$ is the system size. We demonstrate the efficiency of FAFE in Lennard-Jones liquids with several $N$ values. More importantly, we show its abilities in finding and identifying different macroscopic states including meta-stable states in phase co-existing regions.

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英文摘要：

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