The exploration of molecular systems' potential energy surface is important for comprehending their complex behaviors, particularly through identifying various metastable states. However, the transition between these states is often hindered by substantial energy barriers, demanding prolonged molecular simulations that consume considerable computational efforts. Our study introduces the GradNav algorithm, which enhances the exploration of the energy surface, accelerating the reconstruction of the potential energy surface (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 Mueller-type potential energy surfaces and molecular dynamics simulations of the Fs-Peptide protein, these metrics demonstrate GradNav's enhanced ability to escape deep energy wells, as shown by reduced DWEF values, and its reduced reliance on initial conditions, highlighted by increased SSIR values. Consequently, this improved exploration capability enables more precise energy estimations from simulation trajectories.

翻译：分子系统势能面的探索对于理解其复杂行为至关重要，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显著能垒的阻碍，需要长期分子模拟并消耗大量计算资源。本研究提出GradNav算法，通过增强能量面的探索能力，加速势能面的重构。该算法采用从更新起点启动短时模拟的策略——这些起点基于先前观测结果生成——从而有效跨越势垒并探索新区域。为评估GradNav的性能，我们引入两个指标：最深势阱逃逸帧数（DWEF）与搜索成功初始化比率（SSIR）。在Mueller型势能面的朗之万动力学及Fs-Peptide蛋白质分子动力学模拟中的应用表明：通过降低的DWEF值验证了GradNav增强的深势阱逃逸能力，通过升高的SSIR值凸显了其对初始条件依赖性的降低。这种探索能力的提升最终使模拟轨迹能够实现更精确的能量估计。