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 in prototypical systems and show that it outperforms the popular metadynamics method.

中文翻译：

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自适应偏置增强采样中的探索与收敛速度

在自适应偏置增强采样方法中，将偏置电位添加到系统中以驱动亚稳态之间的转换。偏置势是几个集体变量的函数，并根据潜在的自由能表面逐渐修改。我们表明，当集体变量次优时，存在探索 - 收敛权衡，并且必须在快速收敛的偏差之间做出选择，这将导致更少的转换，或者更慢的收敛偏差，可以更有效地探索相空间，但可能需要产生准确的自由能估计的时间要长得多。最近提出的动态概率增强采样 (OPES) 方法侧重于快速收敛，但在某些情况下更倾向于快速探索。为此原因，我们引入了一种新的 OPES 方法变体，它专注于以牺牲收敛速度为代价快速逃离亚稳态。我们说明了这种方法在原型系统中的好处，并表明它优于流行的元动力学方法。