QFLS and light-intensity dependant JV fits perovskite solar cell (real data)
This notebook is a demonstration of how to fit light-intensity dependent QFLS and JV curves with drift-diffusion models using the SIMsalabim package.
It uses real experimental data from perovskite solar cells taken from the following publication:
Grabowski, D., Liu, Z., Schöpe, G., Rau, U. and Kirchartz, T. (2022), Fill Factor Losses and Deviations from the Superposition Principle in Lead Halide Perovskite Solar Cells. Sol. RRL, 6: 2200507. https://doi.org/10.1002/solr.202200507
[1]:
# Import necessary libraries
import warnings, os, sys, shutil,torch, copy
# remove warnings from the output
os.environ["PYTHONWARNINGS"] = "ignore"
warnings.filterwarnings(action='ignore', category=FutureWarning)
warnings.filterwarnings(action='ignore', category=UserWarning)
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
from itertools import combinations
from pySIMsalabim.experiments.JV_steady_state import *
from ax.utils.notebook.plotting import init_notebook_plotting
init_notebook_plotting() # for Jupyter notebooks
try:
from optimpv import *
except Exception as e:
sys.path.append('/home/lecorre/Desktop/optimPV') # add the path to the optimpv module
from optimpv import *
[INFO 12-10 15:13:18] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.
[INFO 12-10 15:13:18] ax.utils.notebook.plotting: Please see
(https://ax.dev/tutorials/visualizations.html#Fix-for-plots-that-are-not-rendering)
if visualizations are not rendering.
Get the experimental data
[2]:
# Set the session path for the simulation and the input files
session_path = os.path.join(os.path.join(os.path.abspath('../'),'SIMsalabim','SimSS'))
input_path = os.path.join(os.path.join(os.path.join(os.path.abspath('../'),'Data','pero_JV_QFLS')))
simulation_setup_filename = 'simulation_setup_K.txt'
simulation_setup = os.path.join(session_path, simulation_setup_filename)
optical_files = ['nk_glass.txt','nk_ITO.txt','nk_PTAA.txt','nk_MAPI.txt','nk_C60_1.txt','nk_Ag.txt']
# path to the layer files defined in the simulation_setup file
l1 = 'PTAA_K.txt'
l2 = 'MAPI_K.txt'
l3 = 'C60_K.txt'
l1 = os.path.join(input_path, l1)
l2 = os.path.join(input_path, l2)
l3 = os.path.join(input_path, l3)
# copy this files to session_path
force_copy = True
if not os.path.exists(session_path):
os.makedirs(session_path)
for file in [l1,l2,l3,simulation_setup_filename]:
file = os.path.join(input_path, os.path.basename(file))
if force_copy or not os.path.exists(os.path.join(session_path, os.path.basename(file))):
shutil.copyfile(file, os.path.join(session_path, os.path.basename(file)))
else:
print('File already exists: ',file)
# copy the optical files to the session path
for file in optical_files:
file = os.path.join(input_path, os.path.basename(file))
if force_copy or not os.path.exists(os.path.join(session_path, os.path.basename(file))):
shutil.copyfile(file, os.path.join(session_path, os.path.basename(file)))
# Show the device structure
fig = sim.plot_band_diagram(simulation_setup, session_path)
dev_par, layers = sim.load_device_parameters(session_path, simulation_setup, run_mode = False)
SIMsalabim_params = {}
for layer in layers:
SIMsalabim_params[layer[1]] = sim.ReadParameterFile(os.path.join(session_path,layer[2]))
L_active_Layer = float(SIMsalabim_params['l2']['L'])
# Load the JV data
X,X_QFLS = [],[]
y,y_QFLS = [],[]
JVs_exp = ['JV_0_279.txt','JV_0_466.txt','JV_0_737.txt','JV_1.txt']
QFLS_exp = ['QFLS_0_279.txt','QFLS_0_466.txt','QFLS_0_737.txt','QFLS_1.txt']
Gfracs = [0.279,0.466,0.737,1]
for i in range(len(JVs_exp)):
df = pd.read_csv(os.path.join(input_path, JVs_exp[i]),delim_whitespace=True)
df = df.dropna()
df_qfls = pd.read_csv(os.path.join(input_path, QFLS_exp[i]),delim_whitespace=True)
df_qfls = df_qfls.dropna()
df['Gfrac'] = Gfracs[i]*np.ones(len(df))
df_qfls['Gfrac'] = Gfracs[i]*np.ones(len(df_qfls))
if i == 0:
X = np.asarray(df[['Vext','Gfrac']])
X_QFLS = np.asarray(df_qfls[['Vext','Gfrac']])
y = np.asarray(df['Jext'].values*10)
y_QFLS = np.asarray(df_qfls['QFLS'].values)
else:
X = np.vstack((X,df[['Vext','Gfrac']]))
X_QFLS = np.vstack((X_QFLS,df_qfls[['Vext','Gfrac']]))
y = np.hstack((y,df['Jext'].values*10))
y_QFLS = np.hstack((y_QFLS,df_qfls['QFLS'].values))
X = np.asarray(X)
y = np.asarray(y)
X_QFLS = np.asarray(X_QFLS)
y_QFLS = np.asarray(y_QFLS)
# get 1sun Jsc as it will help narrow down the range for Gehp
Jsc_1sun = np.interp(0.0, X[X[:,1] == 1][:,0], y[X[:,1] == 1])
minJ = np.min(y)
q = constants.value(u'elementary charge')
G_ehp_calc = abs(Jsc_1sun/(q*L_active_Layer))
G_ehp_max = abs(minJ/(q*L_active_Layer))
# plot the data for each Gfrac
fig, axes = plt.subplots(1,2, figsize=(16,9))
viridis = plt.get_cmap('viridis', len(Gfracs))
for gfrac in Gfracs:
axes[0].plot(X[X[:,1] == gfrac][:,0], y[X[:,1] == gfrac],'-',label=str(gfrac), color=viridis(Gfracs.index(gfrac)))
axes[0].legend(title='Gfrac')
axes[0].grid()
axes[0].set_xlabel('Voltage [V]')
axes[0].set_ylabel('Current density [A m$^{-2}$]')
# plt.show()
for gfrac in Gfracs:
axes[1].plot(X_QFLS[X_QFLS[:,1] == gfrac][:,0], y_QFLS[X_QFLS[:,1] == gfrac],'-',label=str(gfrac), color=viridis(Gfracs.index(gfrac)))
axes[1].grid()
axes[1].set_xlabel('Voltage [V]')
axes[1].set_ylabel('QFLS [eV]')
plt.tight_layout()
plt.show()
Define the parameters for the simulation
[3]:
# Add Ions in TL
ionsinTLs = 1 # 1: ions can enter the transport layers, 0: ions cannot enter the transport layers
params = [] # list of parameters to be optimized
# Perovskite layer parameters
Nc = FitParam(name = 'l2.N_c', value = 3.15e24, bounds = [1e24,4e24], type='range', values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$N_c$', unit='m$^{-3}$', axis_type = 'log', std = 0,encoding = None,force_log = True,)
params.append(Nc)
mun = FitParam(name = 'l2.mu_n', value = 1.49e-5, bounds = [1e-6,1e-3], values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$\mu_n$', unit='m$^2$ V$^{-1}$s$^{-1}$', axis_type = 'log', std = 0,encoding = None,force_log = True)
params.append(mun)
mup = FitParam(name = 'l2.mu_p', value = 1.905e-5, bounds = [1e-6,1e-3], values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$\mu_p$', unit='m$^2$ V$^{-1}$s$^{-1}$', axis_type = 'log', std = 0,encoding = None,force_log = True)
params.append(mup)
bulk_tr = FitParam(name = 'l2.N_t_bulk', value = 1.3e17, bounds = [1e17,8e21], values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$N_{T}$', unit='s', axis_type = 'log', std = 0,encoding = None,force_log = True)
params.append(bulk_tr)
k_direct = FitParam(name = 'l2.k_direct', value = 2.5e-17, bounds = [1e-18,1e-15], values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$k_{direct}$', unit='m$^{3}$ s$^{-1}$', axis_type = 'log', std = 0,encoding = None,force_log = True)
params.append(k_direct)
Nions = FitParam(name = 'l2.C_cation', value = 2.77e23, bounds = [5e22,8e23], type='range', values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$N_{ions}$', unit='m$^{-3}$', axis_type = 'log', std = 0,encoding = None,force_log = True)
params.append(Nions) # Note: C_cation is not defined in SIMsalabim here this sets the N_cation density and the N_A to the same value, this is the equivalent of having the cations being free to move but the anions are immobile.
Gehp = FitParam(name = 'l2.G_ehp', value = 3.98e27, bounds = [G_ehp_calc*0.8,G_ehp_max*1.2], type='range', values = None, start_value = None, log_scale = False, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$G_{ehp}$', unit='m$^{-3}$ s$^{-1}$', axis_type = 'linear', std = 0,encoding = None,force_log = False)
params.append(Gehp) #G_ehp_calc
eps_r = FitParam(name = 'l2.eps_r', value = 34, bounds = [30,40], type='range', values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$\epsilon_r$', unit='-', axis_type = 'log', std = 0,encoding = None,force_log = False)
params.append(eps_r)
# HTL parameters
N_t_int_l1 = FitParam(name = 'l1.N_t_int', value = 1.23e10, bounds = [1e8,1e13], values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$N_{T,int}^{HTL}$', unit='m$^{-2}$', axis_type = 'log', std = 0,encoding = None,force_log = True)
params.append(N_t_int_l1)
offset_l2_l1 = FitParam(name = 'offset_l2_l1.E_v', value = 0.04, bounds = [0,0.1], values = None, start_value = None, log_scale = False, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$\Delta E_{L2-L1}$', unit='eV', axis_type = 'linear', std = 0,encoding = None,force_log = False)
params.append(offset_l2_l1)
Egap_l1 = FitParam(name = 'Egap_l1.E_c', value = 2.15, bounds = [2.1,2.2], type='fixed', values = None, start_value = None, log_scale = False, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$E_{gap,L1}$', unit='eV', axis_type = 'linear', std = 0,encoding = None,force_log = False)
params.append(Egap_l1)
eps_r_l1 = FitParam(name = 'l1.eps_r', value = 3.5, bounds = [2.5,4], type='range', values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$\epsilon_{r}^{HTL}$', unit='-', axis_type = 'log', std = 0,encoding = None,force_log = False)
params.append(eps_r_l1)
Nc_l1 = FitParam(name = 'l1.Nc', value = 5.44e26, bounds = [1e26,1e27], values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$N_{c}^{HTL}$', unit='m$^{-3}$', axis_type = 'log', std = 0,encoding = None,force_log = True)
params.append(Nc_l1)
mu_HTL = FitParam(name = 'l1.mu_p', value = 5e-10, bounds = [5e-10,1e-7], values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$\mu_{p}^{HTL}$', unit='m$^2$ V$^{-1}$s$^{-1}$', axis_type = 'log', std = 0,encoding = None,force_log = True)
params.append(mu_HTL)
ionsMayEnter_HTL = FitParam(name = 'l1.ionsMayEnter', value = ionsinTLs, bounds = [0,1], type='fixed', values = None, start_value = None, log_scale = False, value_type = 'int', fscale = None, rescale = False, stepsize = None, display_name=r'Ions in HTL', unit='-', axis_type = 'linear', std = 0,encoding = None,force_log = False)
params.append(ionsMayEnter_HTL)
# ETL parameters
N_t_int_l2 = FitParam(name = 'l2.N_t_int', value = 1.27e12, bounds = [1e8,1e13], values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$N_{T,int}^{ETL}$', unit='m$^{-2}$', axis_type = 'log', std = 0,encoding = None,force_log = True)
params.append(N_t_int_l2)
offset_l2_l3 = FitParam(name = 'offset_l2_l3.E_c', value = -0.054, bounds = [-0.1,0], values = None, start_value = None, log_scale = False, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$\Delta E_{L2-L3}$', unit='eV', axis_type = 'linear', std = 0,encoding = None,force_log = False)
params.append(offset_l2_l3)
Egap_l3 = FitParam(name = 'Egap_l3.E_v', value = 2.3, bounds = [2.25,2.35], type='fixed', values = None, start_value = None, log_scale = False, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$E_{gap,L3}$', unit='eV', axis_type = 'linear', std = 0,encoding = None,force_log = False)
params.append(Egap_l3)
eps_r_l3 = FitParam(name = 'l3.eps_r', value = 3.16, bounds = [3,6], type='range', values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$\epsilon_{r}^{ETL}$', unit='-', axis_type = 'log', std = 0,encoding = None,force_log = False)
params.append(eps_r_l3)
Nc_l3 = FitParam(name = 'l3.Nc', value = 2.35e26, bounds = [1e26,1e27], type='range', values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$N_{c}^{ETL}$', unit='m$^{-3}$', axis_type = 'log', std = 0,encoding = None,force_log = True)
params.append(Nc_l3)
mu_ETL = FitParam(name = 'l3.mu_n', value = 4.06e-7, bounds = [5e-9,1e-6], values = None, start_value = None, log_scale = True, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$\mu_{n}^{ETL}$', unit='m$^2$ V$^{-1}$s$^{-1}$', axis_type = 'log', std = 0,encoding = None,force_log = True)
params.append(mu_ETL)
ionsMayEnter_ETL = FitParam(name = 'l3.ionsMayEnter', value = ionsinTLs, bounds = [0,1], type='fixed', values = None, start_value = None, log_scale = False, value_type = 'int', fscale = None, rescale = False, stepsize = None, display_name=r'Ions in ETL', unit='-', axis_type = 'linear', std = 0,encoding = None,force_log = False)
params.append(ionsMayEnter_ETL)
# Contact parameters
R_series = FitParam(name = 'R_series', value = 2.98e-4, bounds = [1e-6,1e-2], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$R_{series}$', unit=r'$\Omega$ m$^2$', axis_type = 'log', force_log = True)
params.append(R_series)
R_shunt = FitParam(name = 'R_shunt', value = 0.22, bounds = [1e-2,1e2], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$R_{shunt}$', unit=r'$\Omega$ m$^2$', axis_type = 'log', force_log = True)
params.append(R_shunt)
offset_W_L = FitParam(name = 'offset_W_L.E_v', value = 0.0, bounds = [0,0.1], type='fixed', values = None, start_value = None, log_scale = False, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$\Delta W_L$', unit='eV', axis_type = 'linear', std = 0,encoding = None,force_log = False)
params.append(offset_W_L)
offset_W_R = FitParam(name = 'offset_W_R.E_c', value = -0.0, bounds = [-0.1,0], type='fixed', values = None, start_value = None, log_scale = False, value_type = 'float', fscale = None, rescale = False, stepsize = None, display_name=r'$\Delta W_R$', unit='eV', axis_type = 'linear', std = 0,encoding = None,force_log = False)
params.append(offset_W_R)
# save the original parameters for later
params_orig = copy.deepcopy(params)
num_free_params = len([p for p in params if p.type != 'fixed'])
print('Number of free parameters: ',num_free_params)
Number of free parameters: 20
Run the optimization
In the following, the JVAgent defines two objectives: the QFLS vs light intensity and the JV curves at different light intensities. The QFLS target here is specified as ‘K_QFLSL2’. The means that we are using the QFLS data from layer 2 (the perovskite layer) and we use the formula from the paper from T. Kirchartz’s group (hence the ‘K_’) to calculate the QFLS. The formula is: \begin{equation} QFLS(V) = kTln\left(\frac{\phi(V)}{\phi_{OC}}\right) + qV_{OC} \end{equation} where k is the Boltzmann constant, T is the temperature, q is the elementary charge, \(\phi(V)\) is the photoluminescence emission at voltage V, \(\phi_{OC}\) is the photoluminescence emission at open-circuit voltage and \(V_{OC}\) is the open-circuit voltage.
Note that one can also use QFLSL2 for the QFLS calculated directly from the quasi-Fermi levels in layer 2 (the perovskite layer) instead of using the formula from the paper.
[4]:
# Define the Agent and the target metric/loss function
from optimpv.DDfits.JVAgent import JVAgent
metric = 'nrmse' # can be 'nrmse', 'mse', 'mae'
loss = 'linear' # can be 'linear', 'huber', 'soft_l1'
jv = JVAgent(params, [X,X_QFLS], [y,y_QFLS], session_path, simulation_setup, parallel = True, max_jobs = 4, metric = [metric,metric], loss = [loss,loss], minimize= [True,True], threshold=[0.1,0.3], exp_format=['JV','K_QFLSL2'])
[5]:
# run the simulation with some previously obtained parameters
yfit = jv.run(parameters={},exp_format='JV') # get the JV fit
yfit_QFLS = jv.run(parameters={},X=X_QFLS,exp_format='K_QFLSL2') # get the QFLS fit
viridis = plt.get_cmap('viridis', len(Gfracs))
fig, axes = plt.subplots(1,2, figsize=(16,9))
linewidth = 2
for idx, Gfrac in enumerate(Gfracs[::-1]):
axes[0].plot(X[X[:,1]==Gfrac,0],y[X[:,1]==Gfrac],label='Gfrac = '+str(Gfrac),color=viridis(idx),alpha=0.5,linewidth=linewidth)
axes[0].plot(X[X[:,1]==Gfrac,0],yfit[X[:,1]==Gfrac],label='Gfrac = '+str(Gfrac)+' fit',linestyle='--',color=viridis(idx),linewidth=linewidth)
axes[0].set_xlabel('Voltage [V]')
axes[0].set_ylabel('Current density [A m$^{-2}$]')
axes[0].legend()
for idx, Gfrac in enumerate(Gfracs[::-1]):
axes[1].plot(X_QFLS[X_QFLS[:,1]==Gfrac,0],y_QFLS[X_QFLS[:,1]==Gfrac],label='Gfrac = '+str(Gfrac),color=viridis(idx),alpha=0.5,linewidth=linewidth)
axes[1].plot(X_QFLS[X_QFLS[:,1]==Gfrac,0],yfit_QFLS[X_QFLS[:,1]==Gfrac],label='Gfrac = '+str(Gfrac)+' fit',linestyle='--',color=viridis(idx),linewidth=linewidth)
axes[1].set_xlabel('Voltage [V]')
axes[1].set_ylabel('QFLS [eV]')
plt.tight_layout()
plt.show()
[6]:
# Define the optimizer
from optimpv.pymooOpti.pymooOptimizer import PymooOptimizer
optimizer = PymooOptimizer(params=params, agents=jv, algorithm='NSGA2', pop_size=20, n_gen=300, name='pymoo_multi_obj', verbose_logging=True,max_parallelism=20, )
[7]:
res = optimizer.optimize() # run the optimization
[INFO 12-10 15:13:22] optimpv.pymooOptimizer: Starting optimization using NSGA2 algorithm
[INFO 12-10 15:13:22] optimpv.pymooOptimizer: Population size: 20, Generations: 300
[INFO 12-10 15:13:27] optimpv.pymooOptimizer: Generation 1: Best objective = 0.061527
==========================================================
n_gen | n_eval | n_nds | eps | indicator
==========================================================
1 | 20 | 4 | - | -
[INFO 12-10 15:13:34] optimpv.pymooOptimizer: Generation 2: Best objective = 0.061527
2 | 40 | 6 | 0.1273595105 | f
[INFO 12-10 15:13:38] optimpv.pymooOptimizer: Generation 3: Best objective = 0.035198
3 | 60 | 8 | 0.2350321685 | ideal
[INFO 12-10 15:13:42] optimpv.pymooOptimizer: Generation 4: Best objective = 0.032526
4 | 80 | 9 | 0.0332544768 | ideal
[INFO 12-10 15:13:46] optimpv.pymooOptimizer: Generation 5: Best objective = 0.018448
5 | 100 | 3 | 0.4306108466 | ideal
[INFO 12-10 15:13:49] optimpv.pymooOptimizer: Generation 6: Best objective = 0.018448
6 | 120 | 2 | 0.2091794459 | ideal
[INFO 12-10 15:13:54] optimpv.pymooOptimizer: Generation 7: Best objective = 0.018448
7 | 140 | 3 | 0.2049547217 | f
[INFO 12-10 15:13:58] optimpv.pymooOptimizer: Generation 8: Best objective = 0.018448
8 | 160 | 5 | 0.0616744858 | ideal
[INFO 12-10 15:14:08] optimpv.pymooOptimizer: Generation 9: Best objective = 0.016734
9 | 180 | 6 | 0.1244693464 | ideal
[INFO 12-10 15:14:18] optimpv.pymooOptimizer: Generation 10: Best objective = 0.015855
10 | 200 | 8 | 0.0600082090 | ideal
[INFO 12-10 15:14:22] optimpv.pymooOptimizer: Generation 11: Best objective = 0.015142
11 | 220 | 10 | 0.0464441044 | ideal
[INFO 12-10 15:14:26] optimpv.pymooOptimizer: Generation 12: Best objective = 0.015142
12 | 240 | 9 | 0.0291949888 | ideal
[INFO 12-10 15:14:29] optimpv.pymooOptimizer: Generation 13: Best objective = 0.015142
13 | 260 | 6 | 0.0516673450 | ideal
[INFO 12-10 15:14:32] optimpv.pymooOptimizer: Generation 14: Best objective = 0.014211
14 | 280 | 11 | 0.0553446709 | ideal
[INFO 12-10 15:14:34] optimpv.pymooOptimizer: Generation 15: Best objective = 0.012388
15 | 300 | 12 | 0.0977773827 | ideal
[INFO 12-10 15:14:40] optimpv.pymooOptimizer: Generation 16: Best objective = 0.012316
16 | 320 | 12 | 0.0037989559 | ideal
[INFO 12-10 15:14:43] optimpv.pymooOptimizer: Generation 17: Best objective = 0.012316
17 | 340 | 15 | 0.0143546526 | ideal
[INFO 12-10 15:14:45] optimpv.pymooOptimizer: Generation 18: Best objective = 0.012110
18 | 360 | 9 | 0.0262953393 | ideal
[INFO 12-10 15:14:47] optimpv.pymooOptimizer: Generation 19: Best objective = 0.011576
19 | 380 | 13 | 0.0247507836 | ideal
[INFO 12-10 15:14:50] optimpv.pymooOptimizer: Generation 20: Best objective = 0.011576
20 | 400 | 14 | 0.0039824161 | f
[INFO 12-10 15:14:52] optimpv.pymooOptimizer: Generation 21: Best objective = 0.011208
21 | 420 | 16 | 0.0168073538 | ideal
[INFO 12-10 15:14:55] optimpv.pymooOptimizer: Generation 22: Best objective = 0.009400
22 | 440 | 19 | 0.0761055435 | ideal
[INFO 12-10 15:14:57] optimpv.pymooOptimizer: Generation 23: Best objective = 0.009252
23 | 460 | 20 | 0.0061871222 | ideal
[INFO 12-10 15:14:59] optimpv.pymooOptimizer: Generation 24: Best objective = 0.009239
24 | 480 | 16 | 0.1115738104 | nadir
[INFO 12-10 15:15:02] optimpv.pymooOptimizer: Generation 25: Best objective = 0.008719
25 | 500 | 17 | 0.0212835500 | ideal
[INFO 12-10 15:15:07] optimpv.pymooOptimizer: Generation 26: Best objective = 0.008719
26 | 520 | 20 | 0.0052864369 | f
[INFO 12-10 15:15:09] optimpv.pymooOptimizer: Generation 27: Best objective = 0.008719
27 | 540 | 10 | 0.0128390790 | ideal
[INFO 12-10 15:15:12] optimpv.pymooOptimizer: Generation 28: Best objective = 0.006651
28 | 560 | 9 | 0.1472150271 | ideal
[INFO 12-10 15:15:14] optimpv.pymooOptimizer: Generation 29: Best objective = 0.006567
29 | 580 | 10 | 0.0384451941 | ideal
[INFO 12-10 15:15:17] optimpv.pymooOptimizer: Generation 30: Best objective = 0.006567
30 | 600 | 15 | 0.0205848083 | f
[INFO 12-10 15:15:20] optimpv.pymooOptimizer: Generation 31: Best objective = 0.006434
31 | 620 | 18 | 0.0093948789 | ideal
[INFO 12-10 15:15:22] optimpv.pymooOptimizer: Generation 32: Best objective = 0.006415
32 | 640 | 20 | 0.0796221458 | nadir
[INFO 12-10 15:15:25] optimpv.pymooOptimizer: Generation 33: Best objective = 0.006388
33 | 660 | 20 | 0.1046710070 | nadir
[INFO 12-10 15:15:27] optimpv.pymooOptimizer: Generation 34: Best objective = 0.006333
34 | 680 | 20 | 0.0104413644 | ideal
[INFO 12-10 15:15:29] optimpv.pymooOptimizer: Generation 35: Best objective = 0.006042
35 | 700 | 19 | 0.0194761934 | ideal
[INFO 12-10 15:15:32] optimpv.pymooOptimizer: Generation 36: Best objective = 0.006042
36 | 720 | 17 | 0.0763926435 | nadir
[INFO 12-10 15:15:35] optimpv.pymooOptimizer: Generation 37: Best objective = 0.006042
37 | 740 | 19 | 0.0094419266 | ideal
[INFO 12-10 15:15:37] optimpv.pymooOptimizer: Generation 38: Best objective = 0.006042
38 | 760 | 19 | 0.0034609397 | ideal
[INFO 12-10 15:15:40] optimpv.pymooOptimizer: Generation 39: Best objective = 0.006042
39 | 780 | 20 | 0.0069627607 | f
[INFO 12-10 15:15:42] optimpv.pymooOptimizer: Generation 40: Best objective = 0.006042
40 | 800 | 20 | 0.0048949210 | nadir
[INFO 12-10 15:15:45] optimpv.pymooOptimizer: Generation 41: Best objective = 0.006042
41 | 820 | 20 | 0.0050216976 | ideal
[INFO 12-10 15:15:47] optimpv.pymooOptimizer: Generation 42: Best objective = 0.006042
42 | 840 | 20 | 0.0699903910 | ideal
[INFO 12-10 15:15:50] optimpv.pymooOptimizer: Generation 43: Best objective = 0.006042
43 | 860 | 19 | 0.0030240677 | f
[INFO 12-10 15:15:52] optimpv.pymooOptimizer: Generation 44: Best objective = 0.006042
44 | 880 | 19 | 0.0153845062 | f
[INFO 12-10 15:15:54] optimpv.pymooOptimizer: Generation 45: Best objective = 0.005869
45 | 900 | 20 | 0.0115519923 | ideal
[INFO 12-10 15:15:57] optimpv.pymooOptimizer: Generation 46: Best objective = 0.005869
46 | 920 | 20 | 0.0000645626 | f
[INFO 12-10 15:15:59] optimpv.pymooOptimizer: Generation 47: Best objective = 0.005869
47 | 940 | 20 | 0.0078552284 | f
[INFO 12-10 15:16:01] optimpv.pymooOptimizer: Generation 48: Best objective = 0.005657
48 | 960 | 20 | 0.0139504895 | ideal
[INFO 12-10 15:16:04] optimpv.pymooOptimizer: Generation 49: Best objective = 0.005657
49 | 980 | 20 | 0.0024059921 | f
[INFO 12-10 15:16:06] optimpv.pymooOptimizer: Generation 50: Best objective = 0.005571
50 | 1000 | 20 | 0.0056362456 | ideal
[INFO 12-10 15:16:08] optimpv.pymooOptimizer: Generation 51: Best objective = 0.005571
51 | 1020 | 20 | 0.0026480303 | f
[INFO 12-10 15:16:11] optimpv.pymooOptimizer: Generation 52: Best objective = 0.005571
52 | 1040 | 20 | 0.0033575850 | f
[INFO 12-10 15:16:13] optimpv.pymooOptimizer: Generation 53: Best objective = 0.005557
53 | 1060 | 20 | 0.0447252354 | nadir
[INFO 12-10 15:16:18] optimpv.pymooOptimizer: Generation 54: Best objective = 0.005557
54 | 1080 | 20 | 0.0100959024 | ideal
[INFO 12-10 15:16:20] optimpv.pymooOptimizer: Generation 55: Best objective = 0.005557
55 | 1100 | 20 | 0.000000E+00 | f
[INFO 12-10 15:16:23] optimpv.pymooOptimizer: Generation 56: Best objective = 0.005557
56 | 1120 | 20 | 0.0038240688 | ideal
[INFO 12-10 15:16:25] optimpv.pymooOptimizer: Generation 57: Best objective = 0.005557
57 | 1140 | 20 | 0.0058416021 | f
[INFO 12-10 15:16:27] optimpv.pymooOptimizer: Generation 58: Best objective = 0.005557
58 | 1160 | 20 | 0.0021221492 | f
[INFO 12-10 15:16:30] optimpv.pymooOptimizer: Generation 59: Best objective = 0.005557
59 | 1180 | 20 | 0.0336483673 | ideal
[INFO 12-10 15:16:32] optimpv.pymooOptimizer: Generation 60: Best objective = 0.005557
60 | 1200 | 20 | 0.0001132742 | f
[INFO 12-10 15:16:37] optimpv.pymooOptimizer: Generation 61: Best objective = 0.005557
61 | 1220 | 20 | 0.0060678568 | f
[INFO 12-10 15:16:40] optimpv.pymooOptimizer: Generation 62: Best objective = 0.005557
62 | 1240 | 20 | 0.0020843151 | f
[INFO 12-10 15:16:42] optimpv.pymooOptimizer: Generation 63: Best objective = 0.005557
63 | 1260 | 20 | 0.0041930822 | ideal
[INFO 12-10 15:16:44] optimpv.pymooOptimizer: Generation 64: Best objective = 0.005523
64 | 1280 | 20 | 0.0131154098 | nadir
[INFO 12-10 15:16:47] optimpv.pymooOptimizer: Generation 65: Best objective = 0.005477
65 | 1300 | 20 | 0.0027055938 | ideal
[INFO 12-10 15:16:49] optimpv.pymooOptimizer: Generation 66: Best objective = 0.005477
66 | 1320 | 20 | 0.0049675107 | f
[INFO 12-10 15:16:51] optimpv.pymooOptimizer: Generation 67: Best objective = 0.005477
67 | 1340 | 20 | 0.0043867142 | ideal
[INFO 12-10 15:16:54] optimpv.pymooOptimizer: Generation 68: Best objective = 0.005477
68 | 1360 | 20 | 0.0003359157 | f
[INFO 12-10 15:16:56] optimpv.pymooOptimizer: Generation 69: Best objective = 0.005477
69 | 1380 | 20 | 0.0022244821 | f
[INFO 12-10 15:16:58] optimpv.pymooOptimizer: Generation 70: Best objective = 0.005454
70 | 1400 | 20 | 0.0530085038 | nadir
[INFO 12-10 15:17:00] optimpv.pymooOptimizer: Generation 71: Best objective = 0.005454
71 | 1420 | 20 | 0.0021283673 | f
[INFO 12-10 15:17:03] optimpv.pymooOptimizer: Generation 72: Best objective = 0.005397
72 | 1440 | 19 | 0.0033679921 | ideal
[INFO 12-10 15:17:05] optimpv.pymooOptimizer: Generation 73: Best objective = 0.005397
73 | 1460 | 20 | 0.0020315739 | f
[INFO 12-10 15:17:08] optimpv.pymooOptimizer: Generation 74: Best objective = 0.005397
74 | 1480 | 20 | 0.0076285662 | f
[INFO 12-10 15:17:10] optimpv.pymooOptimizer: Generation 75: Best objective = 0.005397
75 | 1500 | 20 | 0.0061337778 | ideal
[INFO 12-10 15:17:12] optimpv.pymooOptimizer: Generation 76: Best objective = 0.005397
76 | 1520 | 20 | 0.0040506981 | f
[INFO 12-10 15:17:14] optimpv.pymooOptimizer: Generation 77: Best objective = 0.005320
77 | 1540 | 20 | 0.0044429808 | ideal
[INFO 12-10 15:17:17] optimpv.pymooOptimizer: Generation 78: Best objective = 0.005320
78 | 1560 | 20 | 0.0039903942 | f
[INFO 12-10 15:17:19] optimpv.pymooOptimizer: Generation 79: Best objective = 0.005320
79 | 1580 | 20 | 0.0033919759 | f
[INFO 12-10 15:17:25] optimpv.pymooOptimizer: Generation 80: Best objective = 0.005320
80 | 1600 | 20 | 0.0111577503 | f
[INFO 12-10 15:17:27] optimpv.pymooOptimizer: Generation 81: Best objective = 0.005320
81 | 1620 | 20 | 0.0063653283 | f
[INFO 12-10 15:17:29] optimpv.pymooOptimizer: Generation 82: Best objective = 0.005320
82 | 1640 | 20 | 0.0050045629 | f
[INFO 12-10 15:17:32] optimpv.pymooOptimizer: Generation 83: Best objective = 0.005320
83 | 1660 | 20 | 0.0081958079 | f
[INFO 12-10 15:17:34] optimpv.pymooOptimizer: Generation 84: Best objective = 0.005320
84 | 1680 | 20 | 0.0002305835 | f
[INFO 12-10 15:17:36] optimpv.pymooOptimizer: Generation 85: Best objective = 0.005311
85 | 1700 | 20 | 0.0015825790 | f
[INFO 12-10 15:17:39] optimpv.pymooOptimizer: Generation 86: Best objective = 0.005311
86 | 1720 | 20 | 0.0050147924 | f
[INFO 12-10 15:17:41] optimpv.pymooOptimizer: Generation 87: Best objective = 0.005311
87 | 1740 | 20 | 0.0009309333 | f
[INFO 12-10 15:17:43] optimpv.pymooOptimizer: Generation 88: Best objective = 0.005311
88 | 1760 | 20 | 0.0008610503 | f
[INFO 12-10 15:17:46] optimpv.pymooOptimizer: Generation 89: Best objective = 0.005311
89 | 1780 | 20 | 0.0025946220 | f
[INFO 12-10 15:17:48] optimpv.pymooOptimizer: Generation 90: Best objective = 0.005311
90 | 1800 | 20 | 0.0032047523 | f
[INFO 12-10 15:17:51] optimpv.pymooOptimizer: Generation 91: Best objective = 0.005311
91 | 1820 | 20 | 0.0027131224 | f
[INFO 12-10 15:17:53] optimpv.pymooOptimizer: Generation 92: Best objective = 0.005311
92 | 1840 | 20 | 0.0135615897 | nadir
[INFO 12-10 15:17:55] optimpv.pymooOptimizer: Generation 93: Best objective = 0.005311
93 | 1860 | 20 | 0.0014386801 | f
[INFO 12-10 15:17:57] optimpv.pymooOptimizer: Generation 94: Best objective = 0.005311
94 | 1880 | 20 | 0.0084021276 | f
[INFO 12-10 15:18:11] optimpv.pymooOptimizer: Generation 95: Best objective = 0.005311
95 | 1900 | 20 | 0.0028547415 | f
[INFO 12-10 15:18:13] optimpv.pymooOptimizer: Generation 96: Best objective = 0.005311
96 | 1920 | 20 | 0.0063426420 | f
[INFO 12-10 15:18:16] optimpv.pymooOptimizer: Generation 97: Best objective = 0.005311
97 | 1940 | 20 | 0.0008226224 | f
[INFO 12-10 15:18:18] optimpv.pymooOptimizer: Generation 98: Best objective = 0.005311
98 | 1960 | 20 | 0.0014833234 | f
[INFO 12-10 15:18:20] optimpv.pymooOptimizer: Generation 99: Best objective = 0.005311
99 | 1980 | 20 | 0.0059813228 | f
[INFO 12-10 15:18:23] optimpv.pymooOptimizer: Generation 100: Best objective = 0.005311
100 | 2000 | 20 | 0.0017269875 | f
[INFO 12-10 15:18:25] optimpv.pymooOptimizer: Generation 101: Best objective = 0.005311
101 | 2020 | 20 | 0.0606034638 | nadir
[INFO 12-10 15:18:39] optimpv.pymooOptimizer: Generation 102: Best objective = 0.005311
102 | 2040 | 20 | 0.0034069028 | f
[INFO 12-10 15:18:46] optimpv.pymooOptimizer: Generation 103: Best objective = 0.005311
103 | 2060 | 20 | 0.0041966431 | f
[INFO 12-10 15:18:48] optimpv.pymooOptimizer: Generation 104: Best objective = 0.005311
104 | 2080 | 20 | 0.0040216667 | f
[INFO 12-10 15:18:51] optimpv.pymooOptimizer: Generation 105: Best objective = 0.005311
105 | 2100 | 20 | 0.0086705689 | f
[INFO 12-10 15:18:53] optimpv.pymooOptimizer: Generation 106: Best objective = 0.005311
106 | 2120 | 20 | 0.0008971291 | f
[INFO 12-10 15:18:55] optimpv.pymooOptimizer: Generation 107: Best objective = 0.005264
107 | 2140 | 20 | 0.0027898519 | ideal
[INFO 12-10 15:18:58] optimpv.pymooOptimizer: Generation 108: Best objective = 0.005264
108 | 2160 | 20 | 0.0030565366 | f
[INFO 12-10 15:19:00] optimpv.pymooOptimizer: Generation 109: Best objective = 0.005264
109 | 2180 | 20 | 0.0032939468 | f
[INFO 12-10 15:19:02] optimpv.pymooOptimizer: Generation 110: Best objective = 0.005030
110 | 2200 | 20 | 0.0139112867 | ideal
[INFO 12-10 15:19:05] optimpv.pymooOptimizer: Generation 111: Best objective = 0.005030
111 | 2220 | 20 | 0.0086986755 | f
[INFO 12-10 15:19:07] optimpv.pymooOptimizer: Generation 112: Best objective = 0.005029
112 | 2240 | 20 | 0.0037227417 | f
[INFO 12-10 15:19:09] optimpv.pymooOptimizer: Generation 113: Best objective = 0.005029
113 | 2260 | 20 | 0.0012861491 | f
[INFO 12-10 15:19:11] optimpv.pymooOptimizer: Generation 114: Best objective = 0.005021
114 | 2280 | 19 | 0.2123867209 | nadir
[INFO 12-10 15:19:14] optimpv.pymooOptimizer: Generation 115: Best objective = 0.005021
115 | 2300 | 20 | 0.0079763986 | f
[INFO 12-10 15:19:16] optimpv.pymooOptimizer: Generation 116: Best objective = 0.004988
116 | 2320 | 18 | 0.0568415012 | nadir
[INFO 12-10 15:19:19] optimpv.pymooOptimizer: Generation 117: Best objective = 0.004988
117 | 2340 | 20 | 0.0016899363 | f
[INFO 12-10 15:19:21] optimpv.pymooOptimizer: Generation 118: Best objective = 0.004983
118 | 2360 | 20 | 0.0048059985 | nadir
[INFO 12-10 15:19:24] optimpv.pymooOptimizer: Generation 119: Best objective = 0.004983
119 | 2380 | 20 | 0.0006689204 | f
[INFO 12-10 15:19:27] optimpv.pymooOptimizer: Generation 120: Best objective = 0.004983
120 | 2400 | 20 | 0.0025743803 | f
[INFO 12-10 15:19:29] optimpv.pymooOptimizer: Generation 121: Best objective = 0.004983
121 | 2420 | 20 | 0.0032443280 | f
[INFO 12-10 15:19:31] optimpv.pymooOptimizer: Generation 122: Best objective = 0.004982
122 | 2440 | 20 | 0.0043808270 | nadir
[INFO 12-10 15:19:39] optimpv.pymooOptimizer: Generation 123: Best objective = 0.004982
123 | 2460 | 20 | 0.0020901316 | f
[INFO 12-10 15:19:42] optimpv.pymooOptimizer: Generation 124: Best objective = 0.004982
124 | 2480 | 20 | 0.0045996068 | f
[INFO 12-10 15:19:44] optimpv.pymooOptimizer: Generation 125: Best objective = 0.004982
125 | 2500 | 20 | 0.0843555605 | nadir
[INFO 12-10 15:19:46] optimpv.pymooOptimizer: Generation 126: Best objective = 0.004982
126 | 2520 | 20 | 0.0047614035 | f
[INFO 12-10 15:19:49] optimpv.pymooOptimizer: Generation 127: Best objective = 0.004982
127 | 2540 | 20 | 0.0028611625 | f
[INFO 12-10 15:19:51] optimpv.pymooOptimizer: Generation 128: Best objective = 0.004982
128 | 2560 | 20 | 0.0024932797 | f
[INFO 12-10 15:19:54] optimpv.pymooOptimizer: Generation 129: Best objective = 0.004982
129 | 2580 | 20 | 0.0024932797 | f
[INFO 12-10 15:19:56] optimpv.pymooOptimizer: Generation 130: Best objective = 0.004982
130 | 2600 | 20 | 0.0030200854 | f
[INFO 12-10 15:19:58] optimpv.pymooOptimizer: Generation 131: Best objective = 0.004982
131 | 2620 | 20 | 0.0021039369 | f
[INFO 12-10 15:20:01] optimpv.pymooOptimizer: Generation 132: Best objective = 0.004982
132 | 2640 | 20 | 0.0049948735 | f
[INFO 12-10 15:20:03] optimpv.pymooOptimizer: Generation 133: Best objective = 0.004982
133 | 2660 | 20 | 0.0001845828 | f
[INFO 12-10 15:20:06] optimpv.pymooOptimizer: Generation 134: Best objective = 0.004982
134 | 2680 | 20 | 0.0049843131 | f
[INFO 12-10 15:20:08] optimpv.pymooOptimizer: Generation 135: Best objective = 0.004978
135 | 2700 | 20 | 0.0232738966 | nadir
[INFO 12-10 15:20:10] optimpv.pymooOptimizer: Generation 136: Best objective = 0.004978
136 | 2720 | 20 | 0.000000E+00 | f
[INFO 12-10 15:20:13] optimpv.pymooOptimizer: Generation 137: Best objective = 0.004978
137 | 2740 | 20 | 0.0149134239 | ideal
[INFO 12-10 15:20:27] optimpv.pymooOptimizer: Generation 138: Best objective = 0.004976
138 | 2760 | 20 | 0.0103238480 | ideal
[INFO 12-10 15:20:29] optimpv.pymooOptimizer: Generation 139: Best objective = 0.004906
139 | 2780 | 20 | 0.0041833832 | ideal
[INFO 12-10 15:20:32] optimpv.pymooOptimizer: Generation 140: Best objective = 0.004906
140 | 2800 | 20 | 0.0238691484 | nadir
[INFO 12-10 15:20:34] optimpv.pymooOptimizer: Generation 141: Best objective = 0.004906
141 | 2820 | 20 | 0.0008653748 | f
[INFO 12-10 15:20:36] optimpv.pymooOptimizer: Generation 142: Best objective = 0.004906
142 | 2840 | 20 | 0.0044397445 | f
[INFO 12-10 15:20:38] optimpv.pymooOptimizer: Generation 143: Best objective = 0.004870
143 | 2860 | 20 | 0.0352165275 | nadir
[INFO 12-10 15:20:42] optimpv.pymooOptimizer: Generation 144: Best objective = 0.004870
144 | 2880 | 20 | 0.0022077673 | f
[INFO 12-10 15:20:44] optimpv.pymooOptimizer: Generation 145: Best objective = 0.004870
145 | 2900 | 20 | 0.0052413638 | f
[INFO 12-10 15:20:49] optimpv.pymooOptimizer: Generation 146: Best objective = 0.004870
146 | 2920 | 20 | 0.0022513829 | f
[INFO 12-10 15:20:51] optimpv.pymooOptimizer: Generation 147: Best objective = 0.004870
147 | 2940 | 20 | 0.0033170676 | f
[INFO 12-10 15:20:53] optimpv.pymooOptimizer: Generation 148: Best objective = 0.004758
148 | 2960 | 20 | 0.0064947482 | ideal
[INFO 12-10 15:20:55] optimpv.pymooOptimizer: Generation 149: Best objective = 0.004758
149 | 2980 | 20 | 0.0003752557 | f
[INFO 12-10 15:20:58] optimpv.pymooOptimizer: Generation 150: Best objective = 0.004758
150 | 3000 | 20 | 0.0055154274 | f
[INFO 12-10 15:21:00] optimpv.pymooOptimizer: Generation 151: Best objective = 0.004758
151 | 3020 | 20 | 0.0038522921 | f
[INFO 12-10 15:21:02] optimpv.pymooOptimizer: Generation 152: Best objective = 0.004758
152 | 3040 | 20 | 0.0015671839 | f
[INFO 12-10 15:21:05] optimpv.pymooOptimizer: Generation 153: Best objective = 0.004758
153 | 3060 | 20 | 0.0043430572 | f
[INFO 12-10 15:21:07] optimpv.pymooOptimizer: Generation 154: Best objective = 0.004758
154 | 3080 | 20 | 0.0027533560 | f
[INFO 12-10 15:21:09] optimpv.pymooOptimizer: Generation 155: Best objective = 0.004758
155 | 3100 | 20 | 0.0053719283 | f
[INFO 12-10 15:21:12] optimpv.pymooOptimizer: Generation 156: Best objective = 0.004758
156 | 3120 | 20 | 0.0054483337 | f
[INFO 12-10 15:21:14] optimpv.pymooOptimizer: Generation 157: Best objective = 0.004758
157 | 3140 | 20 | 0.0016516725 | f
[INFO 12-10 15:21:16] optimpv.pymooOptimizer: Generation 158: Best objective = 0.004758
158 | 3160 | 20 | 0.0026247259 | f
[INFO 12-10 15:21:19] optimpv.pymooOptimizer: Generation 159: Best objective = 0.004758
159 | 3180 | 20 | 0.0054369939 | f
[INFO 12-10 15:21:21] optimpv.pymooOptimizer: Generation 160: Best objective = 0.004758
160 | 3200 | 20 | 0.0014814972 | f
[INFO 12-10 15:21:23] optimpv.pymooOptimizer: Generation 161: Best objective = 0.004758
161 | 3220 | 20 | 0.0068749486 | f
[INFO 12-10 15:21:25] optimpv.pymooOptimizer: Generation 162: Best objective = 0.004758
162 | 3240 | 20 | 0.0046361747 | f
[INFO 12-10 15:21:28] optimpv.pymooOptimizer: Generation 163: Best objective = 0.004758
163 | 3260 | 20 | 0.0053016411 | f
[INFO 12-10 15:21:30] optimpv.pymooOptimizer: Generation 164: Best objective = 0.004758
164 | 3280 | 20 | 0.0061878065 | f
[INFO 12-10 15:21:33] optimpv.pymooOptimizer: Generation 165: Best objective = 0.004758
165 | 3300 | 20 | 0.0041181892 | f
[INFO 12-10 15:21:35] optimpv.pymooOptimizer: Generation 166: Best objective = 0.004758
166 | 3320 | 20 | 0.000000E+00 | f
[INFO 12-10 15:21:37] optimpv.pymooOptimizer: Generation 167: Best objective = 0.004747
167 | 3340 | 20 | 0.0235813972 | nadir
[INFO 12-10 15:21:40] optimpv.pymooOptimizer: Generation 168: Best objective = 0.004747
168 | 3360 | 20 | 0.0022181500 | f
[INFO 12-10 15:21:42] optimpv.pymooOptimizer: Generation 169: Best objective = 0.004747
169 | 3380 | 20 | 0.0039967414 | ideal
[INFO 12-10 15:21:44] optimpv.pymooOptimizer: Generation 170: Best objective = 0.004747
170 | 3400 | 20 | 0.0042242491 | f
[INFO 12-10 15:21:46] optimpv.pymooOptimizer: Generation 171: Best objective = 0.004720
171 | 3420 | 20 | 0.2815585066 | nadir
[INFO 12-10 15:21:49] optimpv.pymooOptimizer: Generation 172: Best objective = 0.004720
172 | 3440 | 20 | 0.0067616069 | f
[INFO 12-10 15:21:51] optimpv.pymooOptimizer: Generation 173: Best objective = 0.004720
173 | 3460 | 20 | 0.0059718181 | f
[INFO 12-10 15:21:53] optimpv.pymooOptimizer: Generation 174: Best objective = 0.004720
174 | 3480 | 20 | 0.0152903681 | ideal
[INFO 12-10 15:21:56] optimpv.pymooOptimizer: Generation 175: Best objective = 0.004720
175 | 3500 | 20 | 0.0128491193 | f
[INFO 12-10 15:21:58] optimpv.pymooOptimizer: Generation 176: Best objective = 0.004720
176 | 3520 | 20 | 0.0138084432 | nadir
[INFO 12-10 15:22:00] optimpv.pymooOptimizer: Generation 177: Best objective = 0.004720
177 | 3540 | 20 | 0.0031870990 | f
[INFO 12-10 15:22:03] optimpv.pymooOptimizer: Generation 178: Best objective = 0.004720
178 | 3560 | 20 | 0.0064912092 | f
[INFO 12-10 15:22:05] optimpv.pymooOptimizer: Generation 179: Best objective = 0.004720
179 | 3580 | 20 | 0.0050141614 | f
[INFO 12-10 15:22:07] optimpv.pymooOptimizer: Generation 180: Best objective = 0.004720
180 | 3600 | 20 | 0.0077635020 | f
[INFO 12-10 15:22:10] optimpv.pymooOptimizer: Generation 181: Best objective = 0.004707
181 | 3620 | 20 | 0.0588785997 | nadir
[INFO 12-10 15:22:12] optimpv.pymooOptimizer: Generation 182: Best objective = 0.004707
182 | 3640 | 20 | 0.0050857311 | f
[INFO 12-10 15:22:15] optimpv.pymooOptimizer: Generation 183: Best objective = 0.004707
183 | 3660 | 20 | 0.0034475454 | f
[INFO 12-10 15:22:17] optimpv.pymooOptimizer: Generation 184: Best objective = 0.004707
184 | 3680 | 20 | 0.0033316063 | f
[INFO 12-10 15:22:20] optimpv.pymooOptimizer: Generation 185: Best objective = 0.004707
185 | 3700 | 20 | 0.0023419062 | f
[INFO 12-10 15:22:22] optimpv.pymooOptimizer: Generation 186: Best objective = 0.004707
186 | 3720 | 20 | 0.0313120209 | ideal
[INFO 12-10 15:22:25] optimpv.pymooOptimizer: Generation 187: Best objective = 0.004707
187 | 3740 | 20 | 0.0024152177 | f
[INFO 12-10 15:22:27] optimpv.pymooOptimizer: Generation 188: Best objective = 0.004707
188 | 3760 | 20 | 0.0057437752 | f
[INFO 12-10 15:22:30] optimpv.pymooOptimizer: Generation 189: Best objective = 0.004707
189 | 3780 | 20 | 0.0023376144 | f
[INFO 12-10 15:22:32] optimpv.pymooOptimizer: Generation 190: Best objective = 0.004707
190 | 3800 | 20 | 0.0053462444 | f
[INFO 12-10 15:22:35] optimpv.pymooOptimizer: Generation 191: Best objective = 0.004704
191 | 3820 | 20 | 0.0067809241 | ideal
[INFO 12-10 15:22:37] optimpv.pymooOptimizer: Generation 192: Best objective = 0.004704
192 | 3840 | 20 | 0.0016834574 | f
[INFO 12-10 15:22:40] optimpv.pymooOptimizer: Generation 193: Best objective = 0.004704
193 | 3860 | 20 | 0.0034616219 | f
[INFO 12-10 15:22:42] optimpv.pymooOptimizer: Generation 194: Best objective = 0.004704
194 | 3880 | 20 | 0.0035274375 | f
[INFO 12-10 15:22:45] optimpv.pymooOptimizer: Generation 195: Best objective = 0.004704
195 | 3900 | 20 | 0.0058773863 | f
[INFO 12-10 15:22:48] optimpv.pymooOptimizer: Generation 196: Best objective = 0.004704
196 | 3920 | 20 | 0.0013997133 | f
[INFO 12-10 15:22:51] optimpv.pymooOptimizer: Generation 197: Best objective = 0.004704
197 | 3940 | 20 | 0.0014278841 | f
[INFO 12-10 15:22:53] optimpv.pymooOptimizer: Generation 198: Best objective = 0.004704
198 | 3960 | 20 | 0.0020576322 | f
[INFO 12-10 15:22:56] optimpv.pymooOptimizer: Generation 199: Best objective = 0.004704
199 | 3980 | 20 | 0.0052415324 | f
[INFO 12-10 15:22:58] optimpv.pymooOptimizer: Generation 200: Best objective = 0.004704
200 | 4000 | 20 | 0.0009839253 | f
[INFO 12-10 15:23:01] optimpv.pymooOptimizer: Generation 201: Best objective = 0.004704
201 | 4020 | 20 | 0.0044620509 | f
[INFO 12-10 15:23:03] optimpv.pymooOptimizer: Generation 202: Best objective = 0.004703
202 | 4040 | 20 | 0.0077349824 | f
[INFO 12-10 15:23:06] optimpv.pymooOptimizer: Generation 203: Best objective = 0.004700
203 | 4060 | 20 | 0.2913080588 | nadir
[INFO 12-10 15:23:08] optimpv.pymooOptimizer: Generation 204: Best objective = 0.004700
204 | 4080 | 20 | 0.0037303409 | f
[INFO 12-10 15:23:11] optimpv.pymooOptimizer: Generation 205: Best objective = 0.004700
205 | 4100 | 20 | 0.0010354355 | f
[INFO 12-10 15:23:13] optimpv.pymooOptimizer: Generation 206: Best objective = 0.004700
206 | 4120 | 20 | 0.0074655336 | f
[INFO 12-10 15:23:16] optimpv.pymooOptimizer: Generation 207: Best objective = 0.004700
207 | 4140 | 20 | 0.0022493880 | f
[INFO 12-10 15:23:18] optimpv.pymooOptimizer: Generation 208: Best objective = 0.004700
208 | 4160 | 20 | 0.0028530530 | f
[INFO 12-10 15:23:21] optimpv.pymooOptimizer: Generation 209: Best objective = 0.004700
209 | 4180 | 20 | 0.000000E+00 | f
[INFO 12-10 15:23:23] optimpv.pymooOptimizer: Generation 210: Best objective = 0.004700
210 | 4200 | 20 | 0.0360000112 | nadir
[INFO 12-10 15:23:26] optimpv.pymooOptimizer: Generation 211: Best objective = 0.004700
211 | 4220 | 20 | 0.0016302410 | f
[INFO 12-10 15:23:29] optimpv.pymooOptimizer: Generation 212: Best objective = 0.004700
212 | 4240 | 20 | 0.0032421985 | f
[INFO 12-10 15:23:31] optimpv.pymooOptimizer: Generation 213: Best objective = 0.004699
213 | 4260 | 20 | 0.0108997183 | nadir
[INFO 12-10 15:23:34] optimpv.pymooOptimizer: Generation 214: Best objective = 0.004699
214 | 4280 | 20 | 0.0053707493 | f
[INFO 12-10 15:23:36] optimpv.pymooOptimizer: Generation 215: Best objective = 0.004699
215 | 4300 | 20 | 0.0029185048 | f
[INFO 12-10 15:23:39] optimpv.pymooOptimizer: Generation 216: Best objective = 0.004699
216 | 4320 | 20 | 0.0025592527 | f
[INFO 12-10 15:23:42] optimpv.pymooOptimizer: Generation 217: Best objective = 0.004699
217 | 4340 | 20 | 0.0235572284 | nadir
[INFO 12-10 15:23:44] optimpv.pymooOptimizer: Generation 218: Best objective = 0.004699
218 | 4360 | 20 | 0.0015888488 | f
[INFO 12-10 15:23:47] optimpv.pymooOptimizer: Generation 219: Best objective = 0.004699
219 | 4380 | 20 | 0.0031110611 | f
[INFO 12-10 15:23:49] optimpv.pymooOptimizer: Generation 220: Best objective = 0.004699
220 | 4400 | 20 | 0.0030939749 | f
[INFO 12-10 15:23:52] optimpv.pymooOptimizer: Generation 221: Best objective = 0.004699
221 | 4420 | 20 | 0.0032923119 | f
[INFO 12-10 15:23:55] optimpv.pymooOptimizer: Generation 222: Best objective = 0.004699
222 | 4440 | 20 | 0.0057441335 | f
[INFO 12-10 15:23:57] optimpv.pymooOptimizer: Generation 223: Best objective = 0.004699
223 | 4460 | 20 | 0.0003780160 | f
[INFO 12-10 15:24:00] optimpv.pymooOptimizer: Generation 224: Best objective = 0.004699
224 | 4480 | 20 | 0.0059055221 | f
[INFO 12-10 15:24:02] optimpv.pymooOptimizer: Generation 225: Best objective = 0.004699
225 | 4500 | 20 | 0.0042822348 | f
[INFO 12-10 15:24:05] optimpv.pymooOptimizer: Generation 226: Best objective = 0.004699
226 | 4520 | 20 | 0.0032294834 | f
[INFO 12-10 15:24:08] optimpv.pymooOptimizer: Generation 227: Best objective = 0.004699
227 | 4540 | 20 | 0.0032176645 | f
[INFO 12-10 15:24:10] optimpv.pymooOptimizer: Generation 228: Best objective = 0.004699
228 | 4560 | 20 | 0.0020327846 | f
[INFO 12-10 15:24:15] optimpv.pymooOptimizer: Generation 229: Best objective = 0.004699
229 | 4580 | 20 | 0.0039544122 | f
[INFO 12-10 15:24:18] optimpv.pymooOptimizer: Generation 230: Best objective = 0.004699
230 | 4600 | 20 | 0.0003659405 | f
[INFO 12-10 15:24:20] optimpv.pymooOptimizer: Generation 231: Best objective = 0.004699
231 | 4620 | 20 | 0.0036995952 | f
[INFO 12-10 15:24:23] optimpv.pymooOptimizer: Generation 232: Best objective = 0.004698
232 | 4640 | 20 | 0.1808647347 | nadir
[INFO 12-10 15:24:26] optimpv.pymooOptimizer: Generation 233: Best objective = 0.004698
233 | 4660 | 20 | 0.0003253962 | f
[INFO 12-10 15:24:28] optimpv.pymooOptimizer: Generation 234: Best objective = 0.004698
234 | 4680 | 20 | 0.0046576689 | f
[INFO 12-10 15:24:31] optimpv.pymooOptimizer: Generation 235: Best objective = 0.004698
235 | 4700 | 20 | 0.0012981022 | f
[INFO 12-10 15:24:33] optimpv.pymooOptimizer: Generation 236: Best objective = 0.004687
236 | 4720 | 20 | 0.0093762635 | nadir
[INFO 12-10 15:24:35] optimpv.pymooOptimizer: Generation 237: Best objective = 0.004687
237 | 4740 | 20 | 0.0055607268 | f
[INFO 12-10 15:24:38] optimpv.pymooOptimizer: Generation 238: Best objective = 0.004687
238 | 4760 | 20 | 0.0029983649 | f
[INFO 12-10 15:24:40] optimpv.pymooOptimizer: Generation 239: Best objective = 0.004687
239 | 4780 | 20 | 0.0025136774 | f
[INFO 12-10 15:24:42] optimpv.pymooOptimizer: Generation 240: Best objective = 0.004687
240 | 4800 | 20 | 0.0021662737 | f
[INFO 12-10 15:24:44] optimpv.pymooOptimizer: Generation 241: Best objective = 0.004687
241 | 4820 | 20 | 0.0049218394 | f
[INFO 12-10 15:24:47] optimpv.pymooOptimizer: Generation 242: Best objective = 0.004687
242 | 4840 | 20 | 0.0013683407 | f
[INFO 12-10 15:24:49] optimpv.pymooOptimizer: Generation 243: Best objective = 0.004687
243 | 4860 | 20 | 0.0043969810 | f
[INFO 12-10 15:24:52] optimpv.pymooOptimizer: Generation 244: Best objective = 0.004687
244 | 4880 | 20 | 0.0037973727 | ideal
[INFO 12-10 15:24:55] optimpv.pymooOptimizer: Generation 245: Best objective = 0.004687
245 | 4900 | 20 | 0.0025528338 | f
[INFO 12-10 15:24:57] optimpv.pymooOptimizer: Generation 246: Best objective = 0.004687
246 | 4920 | 20 | 0.0003184244 | f
[INFO 12-10 15:24:59] optimpv.pymooOptimizer: Generation 247: Best objective = 0.004685
247 | 4940 | 20 | 0.0069135880 | nadir
[INFO 12-10 15:25:02] optimpv.pymooOptimizer: Generation 248: Best objective = 0.004682
248 | 4960 | 20 | 0.0072507266 | nadir
[INFO 12-10 15:25:10] optimpv.pymooOptimizer: Generation 249: Best objective = 0.004682
249 | 4980 | 20 | 0.0011856982 | f
[INFO 12-10 15:25:12] optimpv.pymooOptimizer: Generation 250: Best objective = 0.004682
250 | 5000 | 20 | 0.0045097168 | f
[INFO 12-10 15:25:15] optimpv.pymooOptimizer: Generation 251: Best objective = 0.004682
251 | 5020 | 20 | 0.0007065779 | f
[INFO 12-10 15:25:17] optimpv.pymooOptimizer: Generation 252: Best objective = 0.004682
252 | 5040 | 20 | 0.0039582612 | f
[INFO 12-10 15:25:19] optimpv.pymooOptimizer: Generation 253: Best objective = 0.004682
253 | 5060 | 20 | 0.0012835734 | f
[INFO 12-10 15:25:22] optimpv.pymooOptimizer: Generation 254: Best objective = 0.004682
254 | 5080 | 20 | 0.0032297950 | f
[INFO 12-10 15:25:24] optimpv.pymooOptimizer: Generation 255: Best objective = 0.004682
255 | 5100 | 20 | 0.0011031904 | f
[INFO 12-10 15:25:26] optimpv.pymooOptimizer: Generation 256: Best objective = 0.004682
256 | 5120 | 20 | 0.0011370784 | f
[INFO 12-10 15:25:29] optimpv.pymooOptimizer: Generation 257: Best objective = 0.004682
257 | 5140 | 20 | 0.0009623637 | f
[INFO 12-10 15:25:31] optimpv.pymooOptimizer: Generation 258: Best objective = 0.004682
258 | 5160 | 20 | 0.0017339645 | f
[INFO 12-10 15:25:33] optimpv.pymooOptimizer: Generation 259: Best objective = 0.004682
259 | 5180 | 20 | 0.0050451604 | f
[INFO 12-10 15:25:35] optimpv.pymooOptimizer: Generation 260: Best objective = 0.004682
260 | 5200 | 20 | 0.0022780892 | f
[INFO 12-10 15:25:39] optimpv.pymooOptimizer: Generation 261: Best objective = 0.004682
261 | 5220 | 20 | 0.0043500393 | f
[INFO 12-10 15:25:41] optimpv.pymooOptimizer: Generation 262: Best objective = 0.004682
262 | 5240 | 20 | 0.000000E+00 | f
[INFO 12-10 15:25:43] optimpv.pymooOptimizer: Generation 263: Best objective = 0.004682
263 | 5260 | 20 | 0.0014050884 | f
[INFO 12-10 15:25:45] optimpv.pymooOptimizer: Generation 264: Best objective = 0.004682
264 | 5280 | 20 | 0.0059660825 | f
[INFO 12-10 15:25:48] optimpv.pymooOptimizer: Generation 265: Best objective = 0.004682
265 | 5300 | 20 | 0.0042954628 | f
[INFO 12-10 15:25:50] optimpv.pymooOptimizer: Generation 266: Best objective = 0.004682
266 | 5320 | 20 | 0.0005143862 | f
[INFO 12-10 15:25:52] optimpv.pymooOptimizer: Generation 267: Best objective = 0.004682
267 | 5340 | 20 | 0.0041420555 | f
[INFO 12-10 15:25:54] optimpv.pymooOptimizer: Generation 268: Best objective = 0.004682
268 | 5360 | 20 | 0.0036482087 | f
[INFO 12-10 15:25:57] optimpv.pymooOptimizer: Generation 269: Best objective = 0.004682
269 | 5380 | 20 | 0.0005576906 | f
[INFO 12-10 15:25:59] optimpv.pymooOptimizer: Generation 270: Best objective = 0.004682
270 | 5400 | 20 | 0.0015817990 | f
[INFO 12-10 15:26:01] optimpv.pymooOptimizer: Generation 271: Best objective = 0.004656
271 | 5420 | 19 | 0.1461575062 | nadir
[INFO 12-10 15:26:03] optimpv.pymooOptimizer: Generation 272: Best objective = 0.004656
272 | 5440 | 20 | 0.0028373188 | f
[INFO 12-10 15:26:06] optimpv.pymooOptimizer: Generation 273: Best objective = 0.004656
273 | 5460 | 20 | 0.0001232806 | f
[INFO 12-10 15:26:08] optimpv.pymooOptimizer: Generation 274: Best objective = 0.004656
274 | 5480 | 20 | 0.0049694547 | f
[INFO 12-10 15:26:11] optimpv.pymooOptimizer: Generation 275: Best objective = 0.004656
275 | 5500 | 20 | 0.0004371670 | f
[INFO 12-10 15:26:13] optimpv.pymooOptimizer: Generation 276: Best objective = 0.004656
276 | 5520 | 20 | 0.0048582380 | f
[INFO 12-10 15:26:16] optimpv.pymooOptimizer: Generation 277: Best objective = 0.004656
277 | 5540 | 20 | 0.0051835526 | nadir
[INFO 12-10 15:26:18] optimpv.pymooOptimizer: Generation 278: Best objective = 0.004656
278 | 5560 | 20 | 0.0008658544 | f
[INFO 12-10 15:26:20] optimpv.pymooOptimizer: Generation 279: Best objective = 0.004656
279 | 5580 | 20 | 0.0038945716 | f
[INFO 12-10 15:26:23] optimpv.pymooOptimizer: Generation 280: Best objective = 0.004656
280 | 5600 | 20 | 0.0034531219 | f
[INFO 12-10 15:26:25] optimpv.pymooOptimizer: Generation 281: Best objective = 0.004645
281 | 5620 | 20 | 0.0318256277 | nadir
[INFO 12-10 15:26:28] optimpv.pymooOptimizer: Generation 282: Best objective = 0.004639
282 | 5640 | 20 | 0.0107194072 | nadir
[INFO 12-10 15:26:30] optimpv.pymooOptimizer: Generation 283: Best objective = 0.004639
283 | 5660 | 20 | 0.0025500707 | f
[INFO 12-10 15:26:32] optimpv.pymooOptimizer: Generation 284: Best objective = 0.004639
284 | 5680 | 20 | 0.0406251710 | nadir
[INFO 12-10 15:26:35] optimpv.pymooOptimizer: Generation 285: Best objective = 0.004639
285 | 5700 | 20 | 0.0026570769 | f
[INFO 12-10 15:26:37] optimpv.pymooOptimizer: Generation 286: Best objective = 0.004639
286 | 5720 | 20 | 0.0024668066 | f
[INFO 12-10 15:26:39] optimpv.pymooOptimizer: Generation 287: Best objective = 0.004639
287 | 5740 | 20 | 0.0053905552 | f
[INFO 12-10 15:26:41] optimpv.pymooOptimizer: Generation 288: Best objective = 0.004639
288 | 5760 | 20 | 0.0058925036 | f
[INFO 12-10 15:26:44] optimpv.pymooOptimizer: Generation 289: Best objective = 0.004639
289 | 5780 | 20 | 0.0041895518 | f
[INFO 12-10 15:26:46] optimpv.pymooOptimizer: Generation 290: Best objective = 0.004639
290 | 5800 | 20 | 0.0052915944 | ideal
[INFO 12-10 15:26:49] optimpv.pymooOptimizer: Generation 291: Best objective = 0.004639
291 | 5820 | 20 | 0.0006976219 | f
[INFO 12-10 15:26:51] optimpv.pymooOptimizer: Generation 292: Best objective = 0.004639
292 | 5840 | 20 | 0.0028895071 | f
[INFO 12-10 15:26:53] optimpv.pymooOptimizer: Generation 293: Best objective = 0.004639
293 | 5860 | 20 | 0.0024610983 | f
[INFO 12-10 15:26:55] optimpv.pymooOptimizer: Generation 294: Best objective = 0.004639
294 | 5880 | 20 | 0.0054960184 | f
[INFO 12-10 15:26:58] optimpv.pymooOptimizer: Generation 295: Best objective = 0.004639
295 | 5900 | 20 | 0.0020832230 | f
[INFO 12-10 15:27:00] optimpv.pymooOptimizer: Generation 296: Best objective = 0.004638
296 | 5920 | 20 | 0.0030483907 | nadir
[INFO 12-10 15:27:02] optimpv.pymooOptimizer: Generation 297: Best objective = 0.004638
297 | 5940 | 20 | 0.0036917828 | f
[INFO 12-10 15:27:05] optimpv.pymooOptimizer: Generation 298: Best objective = 0.004638
298 | 5960 | 20 | 0.0068334332 | f
[INFO 12-10 15:27:07] optimpv.pymooOptimizer: Generation 299: Best objective = 0.004638
299 | 5980 | 20 | 0.0039362106 | f
[INFO 12-10 15:27:09] optimpv.pymooOptimizer: Generation 300: Best objective = 0.004638
[INFO 12-10 15:27:09] optimpv.pymooOptimizer: Optimization completed after 301 generations
[INFO 12-10 15:27:09] optimpv.pymooOptimizer: Number of function evaluations: 6000
[INFO 12-10 15:27:09] optimpv.pymooOptimizer: Final population size: 20
[INFO 12-10 15:27:09] optimpv.pymooOptimizer: Pareto front size: 20
300 | 6000 | 20 | 0.0045536190 | f
[8]:
# get the best parameters and update the params list in the optimizer and the agent
optimizer.update_params_with_best_balance() # update the params list in the optimizer with the best parameters
jv.params = optimizer.params # update the params list in the agent with the best parameters
# print the best parameters
print('Best parameters:')
for p in optimizer.params:
if p.axis_type == 'log' or (p.value>1e4 or p.value<1e-4):
print(f'{p.name}, fitted value: {p.value:.4e}')
else:
print(f'{p.name}, fitted value: {p.value:.4f}')
print('\nSimSS command line:')
print(jv.get_SIMsalabim_clean_cmd(jv.params)) # print the simss command line with the best parameters
Best parameters:
l2.N_c, fitted value: 2.5678e+24
l2.mu_n, fitted value: 7.2137e-05
l2.mu_p, fitted value: 1.0030e-06
l2.N_t_bulk, fitted value: 2.6543e+20
l2.k_direct, fitted value: 4.8279e-18
l2.C_cation, fitted value: 5.1234e+23
l2.G_ehp, fitted value: 4.1298e+27
l2.eps_r, fitted value: 3.2832e+01
l1.N_t_int, fitted value: 1.2365e+10
offset_l2_l1.E_v, fitted value: 0.0044
Egap_l1.E_c, fitted value: 2.1500
l1.eps_r, fitted value: 3.6339e+00
l1.Nc, fitted value: 9.9252e+26
l1.mu_p, fitted value: 3.6344e-08
l1.ionsMayEnter, fitted value: 1.0000
l2.N_t_int, fitted value: 7.3365e+11
offset_l2_l3.E_c, fitted value: -8.8316e-02
Egap_l3.E_v, fitted value: 2.3000
l3.eps_r, fitted value: 4.3775e+00
l3.Nc, fitted value: 4.3585e+26
l3.mu_n, fitted value: 1.4053e-08
l3.ionsMayEnter, fitted value: 1.0000
R_series, fitted value: 2.5056e-04
R_shunt, fitted value: 4.3506e+01
offset_W_L.E_v, fitted value: 0.0000e+00
offset_W_R.E_c, fitted value: -0.0000e+00
SimSS command line:
./simss -l2.N_c 2.56776498605863e+24 -l2.mu_n 7.21372787783067e-05 -l2.mu_p 1.0029625525690294e-06 -l2.N_t_bulk 2.6543244059740894e+20 -l2.k_direct 4.82787523491415e-18 -l2.N_cation 5.123400191731074e+23 -l2.N_A 5.123400191731074e+23 -l2.G_ehp 4.129761728726564e+27 -l2.eps_r 32.83166381279246 -l1.N_t_int 12365245783.194468 -l1.eps_r 3.633894003646212 -l1.mu_p 3.63435750481879e-08 -l1.ionsMayEnter 1 -l2.N_t_int 733653967911.3058 -l3.eps_r 4.377512498850031 -l3.mu_n 1.405258677807112e-08 -l3.ionsMayEnter 1 -R_series 0.0002505618901349245 -R_shunt 43.50605724256587 -l3.E_c 4.0883156507303635 -l1.E_v 5.545640605160449 -l1.E_c 3.3956406051604495 -l3.E_v 6.388315650730363 -W_L 5.545640605160449 -W_R 4.0883156507303635
[9]:
# rerun the simulation with the best parameters
yfit = jv.run(parameters={}) # run the simulation with the best parameters
viridis = plt.get_cmap('viridis', len(Gfracs))
plt.figure(figsize=(10,10))
linewidth = 2
for idx, Gfrac in enumerate(Gfracs[::-1]):
plt.plot(X[X[:,1]==Gfrac,0],y[X[:,1]==Gfrac],label='Gfrac = '+str(Gfrac),color=viridis(idx),alpha=0.5,linewidth=linewidth)
plt.plot(X[X[:,1]==Gfrac,0],yfit[X[:,1]==Gfrac],label='Gfrac = '+str(Gfrac)+' fit',linestyle='--',color=viridis(idx),linewidth=linewidth)
plt.xlabel('Voltage [V]')
plt.ylabel('Current density [A m$^{-2}$]')
plt.legend()
plt.show()
from optimpv.general.general import *
# calculate target
nrmse = calc_metric(y,yfit,metric_name='nrmse')
print('Final NRMSE: ',nrmse)
Final NRMSE: 0.012416489322556878
[10]:
# rerun the simulation with the best parameters
yfit_QFLS = jv.run(parameters={},X=X_QFLS,exp_format='K_QFLSL2') # run the simulation with the best parameters
viridis = plt.get_cmap('viridis', len(Gfracs))
plt.figure(figsize=(10,10))
linewidth = 2
for idx, Gfrac in enumerate(Gfracs[::-1]):
plt.plot(X_QFLS[X_QFLS[:,1]==Gfrac,0],y_QFLS[X_QFLS[:,1]==Gfrac],label='Gfrac = '+str(Gfrac),color=viridis(idx),alpha=0.5,linewidth=linewidth)
plt.plot(X_QFLS[X_QFLS[:,1]==Gfrac,0],yfit_QFLS[X_QFLS[:,1]==Gfrac],label='Gfrac = '+str(Gfrac)+' fit',linestyle='--',color=viridis(idx),linewidth=linewidth)
plt.xlabel('Voltage [V]')
plt.ylabel('Current density [A m$^{-2}$]')
plt.legend()
plt.show()
from optimpv.general.general import *
# calculate target
nrmse = calc_metric(y_QFLS,yfit_QFLS,metric_name='nrmse')
print('Final NRMSE: ',nrmse)
Final NRMSE: 0.018508008647164675
[13]:
import matplotlib
# import itertools
from itertools import combinations
comb = list(combinations(optimizer.all_metrics, 2))
threshold_list = []
for i in range(len(optimizer.agents)):
for j in range(len(optimizer.agents[i].threshold)):
threshold_list.append(optimizer.agents[i].threshold[j])
threshold_comb = list(combinations(threshold_list, 2))
pareto = np.asarray(res.F)
cm = matplotlib.colormaps.get_cmap('viridis')
df = optimizer.get_df_from_pymoo() # get the dataframe from the optimizer
# create pareto df
dum_dic = {}
for i , metr in enumerate(optimizer.all_metrics):
if i not in df.keys():
dum_dic[metr] = pareto[:, i]
df_pareto = pd.DataFrame(dum_dic)
dum_dic = jv.run_Ax(parameters={})
best_balanced = [ dum_dic[metr] for metr in optimizer.all_metrics]
for c,t_c in zip(comb,threshold_comb):
plt.figure(figsize=(10, 10))
plt.scatter(df[c[0]],df[c[1]],c=df.index, cmap=cm, marker='o', s=100) # plot the points with color according to the iteration
cbar = plt.colorbar()
cbar.set_label('Iteration')
sorted_df = df_pareto.sort_values(by=c[0])
plt.plot(sorted_df[c[0]],sorted_df[c[1]],'r')
plt.scatter(t_c[0],t_c[1],c='r', marker='x', s=100) # plot the threshold
plt.scatter(best_balanced[0],best_balanced[1],c='k', marker='*', s=200, label='Best balanced') # plot the best balanced point
plt.xlabel('nrmse JV')
plt.ylabel('nrmse QFLS')
plt.xscale('log')
plt.yscale('log')
plt.show()
[ ]:
# Clean up the output files (comment out if you want to keep the output files)
sim.clean_all_output(session_path)
sim.delete_folders('tmp',session_path)
# uncomment the following lines to delete specific files
sim.clean_up_output('PTAA',session_path)
sim.clean_up_output('perovskite',session_path)
sim.clean_up_output('C60',session_path)
sim.clean_up_output('simulation_setup_realPerovskite.txt',session_path)