from kmos.run import KMC_Model
model = KMC_Model(banner=False)
model.parameters.p_COgas = 2.e-1
model.parameters.p_O2gas = 1.e-1
nrel = 1e7
nsample = 1e7 # numerical parameters
Ts = range(450, 650, 20) # 20 values between 450 and 650 K
TOFs = [] # empty list for output
# Loop over the temperature
for T in Ts:
model.parameters.T = T # Set the temperature
model.do_steps(nrel) # Relax the system
# Sample the reactivity
output = model.get_std_sampled_data(1, nsample, output='dict')
# Collect output
TOFs.append(output['CO_oxidation'])
# Transform the variables
invTs = [1 / float(T) for T in Ts]
logTOFs = [math.log(TOF, 10.) for TOF in TOFs]
# and plot
import matplotlib.pyplot as plt
plt.plot(invTs, logTOFs, '-o')
plt.xlabel('1/T [1/K]')
plt.ylabel('log(TOF) / events (sites s)^-1')
plt.savefig('arrhenius.pdf') # Optionally, save plot
plt.show()
from kmos.run import KMC_Model
model = KMC_Model(banner = False)
model.parameters.p_COgas = 2.e-1
model.parameters.p_O2gas = 1.e-1
nrel = 1e7; nsample = 1e7 # numerical parameters
Ts = range(450,650,20) # 20 values between 450 and 650 K
TOFs = [] # empty list for output
# Loop over the temperature
for T in Ts:
model.parameters.T = T # Set the temperature
model.do_steps(nrel) # Relax the system
# Sample the reactivity
output = model.get_std_sampled_data(1, nsample, output='dict')
# Collect output
TOFs.append(output['CO_oxidation'])
# Transform the variables
invTs = [1/float(T) for T in Ts]
logTOFs = [math.log(TOF,10.) for TOF in TOFs]
# and plot
import matplotlib.pyplot as plt
plt.plot(invTs, logTOFs, '-o')
plt.xlabel('1/T [1/K]')
plt.ylabel('log(TOF) / events (sites s)^-1')
plt.savefig('arrhenius.pdf') # Optionally, save plot
plt.show()
from kmos.run import KMC_Model
model = KMC_Model(banner = False)
model.parameters.p_COgas = 2.e-1
model.parameters.p_O2gas = 1.e-1
nrel = 1e7; nsample = 1e7 # numerical parameters
Ts = range(450,650,20) # 20 values between 450 and 650 K
fout = open('arrhenius.dat', 'w') # open an output file
fout.write(model.get_std_header()) # print the header
# Loop over the temperature
for T in Ts:
model.parameters.T = T # Set the desired temperature
model.do_steps(nrel) # Relax the system
# Sample the reactivity and print data to file
output = model.get_std_sampled_data(1, nsample)
fout.write(output)
fout.close() # Close output file
# We can read the file with numpy...
import numpy as np
data = np.loadtxt('arrhenius.dat')
iT = 0; iTOF = 3 # columns for each variable
# ... and then plot
import matplotlib.pyplot as plt
# numpy arrays can be transformed much more easily
plt.plot(1/data[:,iT], np.log10(data[:,iTOF]), '-o')
plt.xlabel('1/T [1/K]')
plt.ylabel('log(TOF) / events (sites s)^-1')
# plt.savefig('arrhenius.pdf') # Optionally, save plot
plt.show()
#sample_steps
sample_steps = 1e7
#relax model
model.do_steps(relax_steps)
atoms = model.get_atoms(geometry=False)
#get rate constant
k = float(model.rate_constants(process).split('=')[1][1:-8])
#get initial TOF
k_ini = k * (1 - delta)
model.rate_constants.set("CO_adsorption_cus", k_ini)
data = model.get_std_sampled_data(samples=1,
sample_size=sample_steps,
tof_method="integ")
tof_ini = float(data.split(' ')[3])
#get final TOF
k_fin = k * (1 + delta)
model.rate_constants.set("CO_adsorption_cus", k_fin)
data = model.get_std_sampled_data(samples=1,
sample_size=sample_steps,
tof_method="integ")
tof_fin = float(data.split(' ')[3])
delta_TOF = tof_fin - tof_ini
delta_k = k_fin - k_ini
DRC = (k_ini / tof_ini) * (delta_TOF / delta_k)
model = KMC_Model(banner=False)
model.parameters.p_COgas = 2.e-1
model.parameters.p_O2gas = 1.e-1
nrel = 1e7
nsample = 1e7 # numerical parameters
Ts = range(450, 650, 20) # 20 values between 450 and 650 K
fout = open('arrhenius.dat', 'w') # open an output file
fout.write(model.get_std_header()) # print the header
# Loop over the temperature
for T in Ts:
model.parameters.T = T # Set the desired temperature
model.do_steps(nrel) # Relax the system
# Sample the reactivity and print data to file
output = model.get_std_sampled_data(1, nsample)
fout.write(output)
fout.close() # Close output file
# We can read the file with numpy...
import numpy as np
data = np.loadtxt('arrhenius.dat')
iT = 0
iTOF = 3 # columns for each variable
# ... and then plot
import matplotlib.pyplot as plt
# numpy arrays can be transformed much more easily
plt.plot(1 / data[:, iT], np.log10(data[:, iTOF]), '-o')
plt.xlabel('1/T [1/K]')
plt.ylabel('log(TOF) / events (sites s)^-1')
# plt.savefig('arrhenius.pdf') # Optionally, save plot
relax_steps = 1e6
#sample_steps
sample_steps = 1e7
#relax model
model.do_steps(relax_steps)
atoms = model.get_atoms(geometry=False)
#get rate constant
k = float(model.rate_constants(process).split('=')[1][1:-8])
#get initial TOF
k_ini = k*(1-delta)
model.rate_constants.set("CO_adsorption_cus", k_ini)
data = model.get_std_sampled_data(samples=1,sample_size=sample_steps,tof_method="integ")
tof_ini = float(data.split(' ')[3])
#get final TOF
k_fin = k*(1+delta)
model.rate_constants.set("CO_adsorption_cus", k_fin)
data = model.get_std_sampled_data(samples=1,sample_size=sample_steps,tof_method="integ")
tof_fin = float(data.split(' ')[3])
delta_TOF = tof_fin - tof_ini
delta_k = k_fin - k_ini
DRC = (k_ini / tof_ini) * (delta_TOF / delta_k)
print 'delta: ',delta
print 'sample_steps: ',sample_steps
print 'DRC: ',DRC
