论文标题
探索与自适应偏置增强采样的收敛速度
Exploration vs Convergence Speed in Adaptive-bias Enhanced Sampling
论文作者
论文摘要
在自适应偏置增强的采样方法中，添加了偏置电位，以驱动亚稳态状态之间的过渡。偏差电位是一些集体变量的函数，并且根据基础自由能表面逐渐修改。我们表明，当集体变量次优时，就会存在一个探索权交易折衷，并且必须在快速融合的偏见之间进行选择，该偏见会导致更少的过渡，或者可以更慢的偏见，以更有效地探索相位空间，但可能需要更长的时间才能产生准确的免费免费能源估算。最近提出的即时概率增强了采样（OPES）方法的重点是快速收敛，但在某些情况下，优选快速探索。因此，我们引入了OPES方法的新变体，该变体的重点是快速逃脱亚稳态状态，而牺牲了收敛速度。我们说明了这种方法在原型系统上的好处，并表明它表现优于流行的元动力学方法。
In adaptive-bias enhanced sampling methods, a bias potential is added to the system to drive transitions between metastable states. The bias potential is a function of a few collective variables and is gradually modified according to the underlying free energy surface. We show that when the collective variables are suboptimal, there is an exploration-convergence tradeoff, and one must choose between a quickly converging bias that will lead to fewer transitions, or a slower to converge bias that can explore the phase space more efficiently but might require a much longer time to produce an accurate free energy estimate. The recently proposed On-the-fly Probability Enhanced Sampling (OPES) method focuses on fast convergence, but there are cases where fast exploration is preferred instead. For this reason, we introduce a new variant of the OPES method that focuses on quickly escaping metastable states, at the expense of convergence speed. We illustrate the benefits of this approach on prototypical systems and show that it outperforms the popular metadynamics method.
