from kmos.run import KMC_Model
import random
Ts = [350, 450]
kads = 3e-3
size = (30, 30, 30)
targetML = 4.
nsteps = 100
for i, T in enumerate(Ts):
random_seed = random.random() * 1e12
model = KMC_Model(banner=False, size=size, random_seed=random_seed)
model.parameters.T = T
model.parameters.kads = kads
tsim = 0.0
ML = 0.0
while ML < targetML:
model.do_steps(nsteps)
at = model.get_atoms(geometry=False)
# Convert TOF into ML growth
ML += at.tof_data[model.tofs.index('Growth')] * at.delta_t * size[2]
outname = '_'.join(['config', 'T{}'.format(T)] +
['{}'.format(d) for d in model.size])
model.dump_config(outname)
print('Finished with T={}K'.format(T))
print('Deposited {}ML in {} s'.format(ML, model.base.get_kmc_time()))
model.deallocate()
Ts = [350, 450]
kads = 3e-3
size = (30, 30, 30)
targetML = 4.
nsteps = 100
for i,T in enumerate(Ts):
random_seed = random.random()*1e12
model = KMC_Model(banner=False,
size = size,
random_seed = random_seed)
model.parameters.T = T
model.parameters.kads = kads
tsim = 0.0
ML = 0.0
while ML < targetML:
model.do_steps(nsteps)
at = model.get_atoms(geometry=False)
# Convert TOF into ML growth
ML += at.tof_data[model.tofs.index('Growth')]*at.delta_t*size[2]
outname = '_'.join(['config', 'T{}'.format(T)] +
['{}'.format(d) for d in model.size])
model.dump_config(outname)
print('Finished with T={}K'.format(T))
print('Deposited {}ML in {} s'.format(
ML,
model.base.get_kmc_time()))
model.deallocate()
