论文标题
用于分子动力学的微麦克罗马尔可夫链蒙特卡洛法的效率和参数选择
Efficiency and Parameter Selection of a micro-macro Markov chain Monte Carlo method for molecular dynamics
论文作者
论文摘要
最近，我们引入了一种MM-MCMC方案，该方案能够在完整的分子动力学与低维反应坐标的慢速动力学之间存在时间尺度分离时，能够加速Gibbs分布的采样。 MM-MCMC Markov链分为三个步骤：1）计算与当前分子状态相关的反应坐标值； 2）使用一些近似宏观分布产生一个新的宏观建议； 3）重建与新采样的宏观值一致的分子构型。有许多影响MM-MCMC方法效率的方法参数。在宏观级别上，提案和近似宏观分布很重要，而在显微镜水平上，重建分布非常重要。在本手稿中，我们将研究这些参数对MM-MCMC方法效率的影响，对两个分子：一个简单的三原子分子和丁烷。
We recently introduced a mM-MCMC scheme that is able to accelerate the sampling of Gibbs distributions when there is a time-scale separation between the complete molecular dynamics and the slow dynamics of a low dimensional reaction coordinate. The mM-MCMC Markov chain works in three steps: 1) compute the reaction coordinate value associated to the current molecular state; 2) generate a new macroscopic proposal using some approximate macroscopic distribution; 3) reconstruct a molecular configuration that is consistent with the newly sampled macroscopic value. There are a number of method parameters that impact the efficiency of the mM-MCMC method. On the macroscopic level, the proposal- and approximate macroscopic distributions are important, while on the microscopic level the reconstruction distribution is of significant importance. In this manuscript, we will investigate the impact of these parameters on the efficiency of the mM-MCMC method on two molecules: a simple three-atom molecule and butane.
