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，尤其是通过识别各种亚稳态。然而，这些状态之间的转变常因显著的能量势垒而受阻，需要长时间分子模拟，消耗大量计算资源。本研究提出了GradNav算法，该算法增强了对能量面的探索，加速了势能面（PES）的重建。该算法采用从更新起点（基于先前观测）启动短模拟运行的策略，以有效穿越势垒并探索新区域。为评估GradNav的性能，我们引入了两个指标：最深井逃逸帧（DWEF）和搜索成功初始化比率（SSIR）。通过在Mueller型势能面上的朗之万动力学以及Fs-Peptide蛋白质的分子动力学模拟中的应用，这些指标表明GradNav在逃逸深能量井方面具有增强能力（DWEF值降低），且对初始条件的依赖减少（SSIR值提高）。因此，这种改进的探索能力能够从模拟轨迹中实现更精确的能量估算。