Efficiency Comparison of Single- and Multiple-Macrostate Grand Canonical Ensemble Transition-Matrix Monte Carlo Simulations
Efficiency Comparison of Single- and Multiple-Macrostate Grand Canonical Ensemble Transition-Matrix Monte Carlo Simulations
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DOI：
10.1021/acs.jpcb.3c00613
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发表时间：
2023-03-28
影响因子：
3.3
通讯作者：
Shen，Vincent K.
中科院分区：
文献类型：
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作者：
Hatch，Harold W.;Siderius，Daniel W.;Shen，Vincent K.
最近对大正则系综中的并行平面直方图过渡矩阵蒙特卡罗模拟的兴趣，由于其在研究相行为、自组装和吸附方面的有效性，导致了单宏观状态模拟的最极端情况，其中每个宏观状态都通过鬼粒子插入和删除进行独立模拟。尽管它们在多项研究中得到使用，但尚未对这些单宏观状态模拟与多宏观状态模拟进行效率比较。我们表明，多宏观状态模拟的效率比单宏观状态模拟高出 3 个数量级，这证明了平坦直方图偏置插入和删除的显着效率，即使接受概率较低。使用开源模拟工具包 FEASST，对超临界流体、块状 Lennard-Jones 和三位点水模型的气液平衡、自组装斑片三聚体颗粒以及限制在纯排斥性多孔网络中的 Lennard-Jones 流体的吸附进行了效率比较。通过直接与各种蒙特卡洛试验移动集进行比较，单宏观状态模拟中的这种效率损失归因于三个相关原因。首先，单宏观状态模拟中的幽灵粒子插入和删除会产生与多宏观状态模拟中的大规范系综试验相同的计算费用，但幽灵试验并不能从将马尔可夫链传播到新的微观状态中获得采样优势。其次，单一宏观状态模拟缺乏宏观状态变化试验，这些试验因自洽收敛的相对宏观状态概率而产生偏差，而这是平坦直方图模拟的主要组成部分。第三，将马尔可夫链限制为单个宏观状态会降低采样的可能性。在所有研究的系统中，用于多宏观状态平坦直方图模拟的现有并行化方法比并行单宏观状态模拟更有效，效率大约高一个数量级或更多。
Recent interest in parallelizing flat-histogram transition-matrix Monte Carlo simulations in the grand canonical ensemble, due to its demonstrated effectiveness in studying phase behavior, self-assembly and adsorption, has led to the most extreme case of single-macrostate simulations, where each macrostate is simulated independently with ghost particle insertions and deletions. Despite their use in several studies, no efficiency comparisons of these single-macrostate simulations have been made with multiple-macrostate simulations. We show that multiple-macrostate simulations are up to 3 orders of magnitude more efficient than single-macrostate simulations, which demonstrates the remarkable efficiency of flat-histogram biased insertions and deletions, even with low acceptance probabilities. Efficiency comparisons were made for supercritical fluids and vapor–liquid equilibrium of bulk Lennard-Jones and a three-site water model, self-assembling patchy trimer particles and adsorption of a Lennard-Jones fluid confined in a purely repulsive porous network, using the open source simulation toolkit FEASST. By directly comparing with a variety of Monte Carlo trial move sets, this efficiency loss in single-macrostate simulations is attributed to three related reasons. First, ghost particle insertions and deletions in single-macrostate simulations incur the same computational expense as grand canonical ensemble trials in multiple-macrostate simulations, yet ghost trials do not reap the sampling benefit from propagating the Markov chain to a new microstate. Second, single-macrostate simulations lack macrostate change trials that are biased by the self-consistently converging relative macrostate probability, which is a major component of flat histogram simulations. Third, limiting a Markov chain to a single macrostate reduces sampling possibilities. Existing parallelization methods for multiple-macrostate flat-histogram simulations are shown to be more efficient than parallel single-macrostate simulations by approximately an order of magnitude or more in all systems investigated.