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molecular_dynamics() -- optimize atoms given restraints, with MD

edat = <energy_data>   objective function parameters
schedule_scale = <physical.values> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 factors for physical restraint types in scaling the schedule
residue_span_range = <int:2> 0 99999 range of residues spanning the allowed distances; for MAKE_RESTRAINTS, PICK_RESTRAINTS, non-bonded dynamic pairs
max_iterations = <int:1> 200 maximal iterations in optimization
output = <str:1> 'LONG' 'NO_REPORT' | 'REPORT'
md_time_step = <float:1> 4.0 time step for MD in fs
init_velocities = <bool:1> True whether to initialize velocities before MD
temperature = <float:1> 293.0 temperature for MD simulation in K
equilibrate = <int:1> 999999 equilibrate during MD every that many steps
md_return = <str:1> 'FINAL' return MODEL with 'MINIMAL' energy or 'FINAL' MODEL
cap_atom_shift = <float:1> 0.2 limit for atomic shifts in optimization
guide_factor = <float:1>   factor for guided MD
guide_time = <float:1>   time for guided MD, in fs
actions = []   list of periodic actions

Output:
molpdf

Requirements:
restraints

This command creates a new Python optimizer object. Calling the object's optimize method with an atom selection then performs a molecular dynamics optimization at a fixed temperature. This is the most basic version of the iterative solver of the Newton's equations of motion. The integrator uses the Verlet algorithm [Verlet, 1967]. All atomic masses are set to that of carbon 12. A brief description of the algorithm is given in Section A.2.

The molecular dynamics optimizer pretends that the natural logarithm of the molecular pdf is energy in kcal/mole. md_time_step is the time step in femtoseconds. temperature is the temperature of the system in Kelvin. max_iterations determines the number of MD steps. If md_return is 'FINAL' the last structure is returned as the MODEL. If md_return is 'MINIMAL' then the structure with the lowest value of the objective function on the whole trajectory is returned as the MODEL. Rescaling of velocities is done every equilibration steps to match the specified temperature. Atomic shifts along one axis are limited by cap_atom_shift. This value should be smaller than energy_data.update_dynamic. If init_velocities = True, the velocity arrays are initialized, otherwise they are not. In that case, the final velocities from the previous run are used as the initial velocities for the current run.

If both guide_factor and guide_time are non-zero, self-guided molecular dynamics [Wu & Wang, 1999] is carried out.

See conjugate_gradients() for a description of the other parameters.

Example: See conjugate_gradients() command.


next up previous contents index
Next: actions.write_structure() write Up: The optimizers module: optimization Previous: quasi_newton() optimize   Contents   Index
Ben Webb 2007-01-19