探索平坦直方图方法中并行偏置的潜力
Exploring the Potential of Parallel Biasing in Flat Histogram Methods
摘要：
Metadaptics是高级采样算法的“平坦直方图”类的成员，已广泛用于分子模拟中，以驱动对由高自由能势垒分离的状态的探索，并促进对定义在集体变量（CV）上的自由能景观的全面采样，集体变量（CV）表征系统的状态。通常，当探索大量CV时，这些方法会遇到严重的限制。最近提出的一个变种，平行偏置元自洽（PBMetaD），承诺通过交换标准方法所需的$n$维自由能景观为$n$一维边际自由能景观，以帮助探索自由能景观以及多个重要的集体变量。在这项研究中，我们系统地研究了平行偏置如何影响自由能景观的收敛，以及相对于标准方法的每个变量，以及平行偏置策略在使用高级采样计算自由能时解决常见瓶颈的有效性。
英文摘要：
Metadynamics, a member of the `flat histogram' class of advanced sampling algorithms, has been widely used in molecular simulations to drive the exploration of states separated by high free energy barriers and promote comprehensive sampling of free energy landscapes defined on collective variables (CVs) which characterize the state of the system. Typically, the methods encounter severe limitations when exploring large numbers of CVs. A recently proposed variant, parallel bias metadynamics (PBMetaD), promises to aid in exploring free energy landscapes along with multiple important collective variables by exchanging the $n$-dimensional free energy landscape required by standard methods for $n$ one-dimensional marginal free energy landscapes. In this study, we systematically examine how parallel biasing affects the convergence of free energy landscapes along with each variable relative to standard methods and the effectiveness of the parallel biasing strategy for addressing common bottlenecks in the use of advanced sampling to calculate free energies.
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