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.
翻译:我们最近引入了一个 mM-MCMC 计划, 能够加速对 Gibbs 分布进行取样, 当完整分子动态和低维反应坐标慢动态之间有时间尺度的分离时。 mM- MC Markov 链条分三步运行:1) 计算与当前分子状态相关的反应协调值;2) 利用某些近似宏观分布产生一个新的宏观建议;3) 重建与新取样的宏观分布值相一致的分子配置。 有一些方法参数影响 mM- MC 方法的效率。 在宏观层面, 提案和近似宏观分布很重要, 而对于微观层面, 重建分布非常重要 。 在这个手稿中, 我们将调查这些参数对 mM- MC 方法对两种分子的效率的影响: 一个简单的三原子分子和丁烷。