Open steffencruz opened 1 month ago
GROMACS, a molecular dynamics package primarily designed for simulating proteins, lipids, and nucleic acids, does not have a direct "early stopping" feature like those found in machine learning frameworks. However, it does support various ways to control and stop simulations based on certain conditions, which can be somewhat similar to early stopping. Here's a breakdown of how you can manage this:
Stopping Based on Performance or Stability Criteria:
.mdp
file).Custom Scripts or Extensions:
.log
or trajectory files (.xtc
, .trr
), then using system commands to stop GROMACS.Checkpointing and Resuming:
.cpt
) that can be used to resume a simulation. This feature can be used in conjunction with custom scripts to stop and later resume simulations based on analysis of intermediate results.Implementation and Default Settings:
Inspecting Output Files:
.log
file: Contains detailed logs of the simulation, including performance metrics and warnings..edr
file: Energy file that can be analyzed using gmx energy
to extract various thermodynamic properties..xtc
, .trr
): Contain the position (and optionally velocities) of atoms over time. Use tools like gmx traj
to analyze trajectories.Modifying the .mdp
File:
.mdp
file to adjust the output frequency of logs and energy data to monitor the simulation more closely, which can aid in deciding when to apply an early stopping criterion manually.To conclude, while GROMACS does not support automatic early stopping based on internal criteria like convergence, users can implement custom monitoring and stopping mechanisms using external scripts and checkpointing features. Checking and analyzing the output files regularly during the simulation will be crucial for making decisions about stopping early.
Instead of implementing ES logic externally we prefer to use GROMACS to do this. This should reduce bugginess, instability and complexity.