cov_labels = model.get_occupation_header().split(' ')
#Number of kmc steps taken in each sample
sample_step = 1e5
#Number of samples
N = 100
#prepare arrays for TOFs, coverages and kmc steps
tofs = np.zeros((N, len(tof_labels)))
covs = np.zeros((N, len(cov_labels)))
steps = np.zeros((N, 1))
#run model and save data
for i in range(N):
atoms = model.get_atoms(geometry=False)
tof = atoms.tof_integ
tofs[i, :] = tof
cov = atoms.occupation
covs[i, :] = cov.flatten()
step = atoms.kmc_step
steps[i] = step
model.do_steps(sample_step)
#prepare figure and plot colors
fig = pylab.figure()
colors = [
"#0065bd", "#a2ad00", "#e37222", "#B452CD", "#dad7cb", "#000000", "r"
]
#plot TOFs
"""Visualize the growth of a run of the SOS model""" import sys, os from kmos.run import KMC_Model from ase.visualize import view config = os.path.splitext(sys.argv[1])[0] size = (30, 30, 30) model = KMC_Model(banner=False, size=size) model.load_config(config) at = model.get_atoms() view(at)
"""Visualize the growth of a run of the SOS model""" import sys, os from kmos.run import KMC_Model from ase.visualize import view config = os.path.splitext(sys.argv[1])[0] size = (30, 30, 30) model = KMC_Model(banner = False, size = size) model.load_config(config) at = model.get_atoms() view(at)
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()
