Exploring the potential energy surface (PES) of molecular systems is important for comprehending their complex behaviors, particularly through the identification of various metastable states. However, the transition between these states is often hindered by substantial energy barriers, demanding prolonged molecular simulations that consume considerable computational resources. Our study introduces the gradient-based navigation (GradNav) algorithm, which accelerates the exploration of the energy surface and enables proper reconstruction of the PES. This algorithm employs a strategy of initiating short simulation runs from updated starting points derived from prior observations to effectively navigate across potential barriers and explore new regions. To evaluate GradNav’s performance, we introduce two metrics: the deepest well escape frame (DWEF) and the search success initialization ratio (SSIR). Through applications on Langevin dynamics within Müller-type PESs and molecular dynamics simulations of the Fs-peptide protein, these metrics demonstrate GradNav’s enhanced ability to escape deep energy wells and its reduced reliance on initial conditions, as denoted by the reduced DWEF values and increased SSIR values, respectively. Consequently, this improved exploration capability enables more precise energy estimations from simulation trajectories.

中文翻译：

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GradNav：通过基于梯度的导航加速潜在能量面的探索


探索分子系统的势能面（PES）对于理解其复杂行为非常重要，特别是通过识别各种亚稳态。然而，这些状态之间的转变常常受到巨大的能量障​​碍的阻碍，需要长时间的分子模拟，从而消耗大量的计算资源。我们的研究引入了基于梯度的导航（GradNav）算法，该算法加速了能量表面的探索并能够正确重建 PES。该算法采用从先前观察得出的更新起点启动短模拟运行的策略，以有效地跨越潜在障碍并探索新区域。为了评估 GradNav 的性能，我们引入了两个指标：最深井逃逸框架（DWEF）和搜索成功初始化比率（SSIR）。通过在 Müller 型 PES 中 Langevin 动力学的应用以及 Fs 肽蛋白的分子动力学模拟，这些指标证明了 GradNav 逃逸深能井的能力增强，并且减少了对初始条件的依赖，如 DWEF 值降低和 SSIR 增加所示值，分别。因此，这种改进的探索能力可以根据模拟轨迹进行更精确的能量估计。