barostat_interval = 25
barostat = mm.MonteCarloBarostat(pressure, temperature, barostat_interval)
system.addForce(barostat)
#### Platform
platform = mm.Platform.getPlatformByName('CUDA')
properties = {'CudaPrecision': 'mixed'}
#### Simulation
simulation = app.Simulation(topology, system, integrator, platform, properties)
#### Initial Conditions
positions = m3t.get(pdb, coordinates=True)
simulation.context.setPositions(positions)
simulation.context.setVelocitiesToTemperature(temperature)
#### Iterations Parameters
steps_simulation = 10000
steps_interval_saving = 500
steps_interval_verbose = 2500
steps_interval_checkpoint = 2500
time_simulation = (steps_simulation * step_size).in_units_of(unit.picoseconds)
time_saving = (steps_interval_saving * step_size).in_units_of(unit.picoseconds)
time_verbose = (steps_interval_verbose * step_size).in_units_of(
unit.picoseconds)
time_checkpoint = (steps_interval_checkpoint * step_size).in_units_of(
time_saving = 1.0 * unit.picoseconds
time_verbose = 100.0 * unit.picoseconds
time_checkpoint = 100.0 * unit.picoseconds
number_iterations = int(time_simulation / time_saving)
elapsed_iterations_verbose = int(time_verbose / time_saving)
elapsed_iterations_checkpoint = int(time_checkpoint / time_saving)
steps_per_iteration = int(time_saving / step_size)
total_simulation_steps = number_iterations * steps_per_iteration
#### Reporters
# Observables Stored
net_mass, n_degrees_of_freedom = m3t.get(system,
net_mass=True,
n_degrees_of_freedom=True)
number_states_saved = number_iterations + 1
data = dict()
data['time'] = unit.Quantity(np.zeros([number_states_saved], np.float64),
unit.picoseconds)
data['potential'] = unit.Quantity(np.zeros([number_states_saved], np.float64),
unit.kilocalories_per_mole)
data['kinetic'] = unit.Quantity(np.zeros([number_states_saved], np.float64),
unit.kilocalories_per_mole)
data['volume'] = unit.Quantity(np.zeros([number_states_saved], np.float64),
unit.nanometers**3)
data['density'] = unit.Quantity(np.zeros([number_states_saved], np.float64),
unit.gram / unit.centimeters**3)
data['kinetic_temperature'] = unit.Quantity(
np.zeros([number_states_saved], np.float64), unit.kelvin)
import os
import molmodmt as m3t
import simtk.openmm as mm
import simtk.openmm.app as app
import simtk.unit as unit
from openmmtools.integrators import LangevinIntegrator
# Loading initial PDB file
molmod_modeller = m3t.load('system_init.pdb','openmm.Modeller')
# System
topology = m3t.convert(molmod_modeller, 'openmm.Topology')
positions = m3t.get(molmod_modeller, coordinates=True)
forcefield = app.ForceField('amber99sbildn.xml','tip3p.xml')
system = forcefield.createSystem(topology, constraints=app.HBonds)
# Thermodynamic state (meaningless here but necessary)
temperature = 0.0*unit.kelvin
pressure = None
# Integrator
friction = 1.0/unit.picosecond
step_size = 2.0*unit.femtoseconds
integrator = LangevinIntegrator(temperature, friction, step_size)
integrator.setConstraintTolerance(0.00001)
barostat_interval = 25
barostat = mm.MonteCarloBarostat(pressure, temperature, barostat_interval)
system.addForce(barostat)
#### Platform
platform = mm.Platform.getPlatformByName('CUDA')
properties = {'CudaPrecision': 'mixed'}
#### Simulation
simulation = app.Simulation(topology, system, integrator, platform, properties)
#### Initial Conditions
positions = m3t.get(pdb, coordinates=True)
simulation.context.setPositions(positions)
simulation.context.setVelocitiesToTemperature(temperature)
#### Iterations Parameters
time_simulation = 1.0 * unit.nanoseconds
time_saving = 1.0 * unit.picoseconds
time_verbose = 100.0 * unit.picoseconds
time_checkpoint = 100.0 * unit.picoseconds
number_iterations = int(time_simulation / time_saving)
elapsed_iterations_verbose = int(time_verbose / time_saving)
elapsed_iterations_checkpoint = int(time_checkpoint / time_saving)
steps_per_iteration = int(time_saving / step_size)
total_simulation_steps = number_iterations * steps_per_iteration