  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.
