pymoo GA: OPV light-intensity dependant JV fits with SIMsalabim (fake data)
This notebook is a demonstration of how to fit light-intensity dependent JV curves with drift-diffusion models using the SIMsalabim package.
[1]:
# Import necessary libraries
import warnings, os, sys, shutil
# 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
from numpy.random import default_rng
import torch, copy, uuid
import pySIMsalabim as sim
from pySIMsalabim.experiments.JV_steady_state import *
import ax, logging
from ax.utils.notebook.plotting import init_notebook_plotting, render
init_notebook_plotting() # for Jupyter notebooks
try:
from optimpv import *
from optimpv.pymooOpti.pymooOptimizer import PymooOptimizer
except Exception as e:
sys.path.append('../') # add the path to the optimpv module
from optimpv import *
from optimpv.pymooOpti.pymooOptimizer import PymooOptimizer
[INFO 12-08 09:09:31] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.
[INFO 12-08 09:09:31] ax.utils.notebook.plotting: Please see
(https://ax.dev/tutorials/visualizations.html#Fix-for-plots-that-are-not-rendering)
if visualizations are not rendering.
Define the parameters for the simulation
[2]:
params = [] # list of parameters to be optimized
mun = FitParam(name = 'l2.mu_n', value = 7e-8, bounds = [1e-9,1e-6], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$\mu_n$', unit='m$^2$ V$^{-1}$s$^{-1}$', axis_type = 'log', force_log = True)
params.append(mun)
mup = FitParam(name = 'l2.mu_p', value = 5e-8, bounds = [1e-9,1e-6], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$\mu_p$', unit=r'm$^2$ V$^{-1}$s$^{-1}$', axis_type = 'log', force_log = True)
params.append(mup)
bulk_tr = FitParam(name = 'l2.N_t_bulk', value = 1e20, bounds = [1e19,1e22], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$N_{T}$', unit=r'm$^{-3}$', axis_type = 'log', force_log = False)
params.append(bulk_tr)
preLangevin = FitParam(name = 'l2.preLangevin', value = 1e-2, bounds = [0.005,1], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$\gamma_{pre}$', unit=r'', axis_type = 'log', force_log = False)
params.append(preLangevin)
R_series = FitParam(name = 'R_series', value = 1e-4, bounds = [1e-5,1e-3], 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 = False)
params.append(R_series)
# save the original parameters for later
params_orig = copy.deepcopy(params)
Generate some fake data
Here we generate some fake data to fit. The data is generated using the same model as the one used for the fitting, so it is a good test of the fitting procedure. For more information on how to run SIMsalabim from python see the pySIMsalabim package.
[3]:
# 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','simsalabim_test_inputs','fakeOPV')))
simulation_setup_filename = 'simulation_setup_fakeOPV.txt'
simulation_setup = os.path.join(session_path, simulation_setup_filename)
# path to the layer files defined in the simulation_setup file
l1 = 'ZnO.txt'
l2 = 'ActiveLayer.txt'
l3 = 'BM_HTL.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)
# Show the device structure
fig = sim.plot_band_diagram(simulation_setup, session_path)
# reset simss
# Set the JV parameters
Gfracs = [0.1,0.5,1.0] # Fractions of the generation rate to simulate (None if you want only one light intensity as define in the simulation_setup file)
UUID = str(uuid.uuid4()) # random UUID to avoid overwriting files
cmd_pars = [] # see pySIMsalabim documentation for the command line parameters
# Add the parameters to the command line arguments
for param in params:
cmd_pars.append({'par':param.name, 'val':str(param.value)})
# Add the layer files to the command line arguments
# cmd_pars.append({'par':'l1', 'val':l1})
# cmd_pars.append({'par':'l2', 'val':l2})
# cmd_pars.append({'par':'l3', 'val':l3})
# Run the JV simulation
ret, mess = run_SS_JV(simulation_setup, session_path, JV_file_name = 'JV.dat', G_fracs = Gfracs, parallel = True, max_jobs = 3, UUID=UUID, cmd_pars=cmd_pars)
# save data for fitting
X,y = [],[]
X_orig,y_orig = [],[]
if Gfracs is None:
data = pd.read_csv(os.path.join(session_path, 'JV_'+UUID+'.dat'), sep=r'\s+') # Load the data
Vext = np.asarray(data['Vext'].values)
Jext = np.asarray(data['Jext'].values)
G = np.ones_like(Vext)
rng = default_rng()#
noise = rng.standard_normal(Jext.shape) * 0.01 * Jext
Jext = Jext + noise
X = Vext
y = Jext
plt.figure()
plt.plot(X,y)
plt.show()
else:
for Gfrac in Gfracs:
data = pd.read_csv(os.path.join(session_path, 'JV_Gfrac_'+str(Gfrac)+'_'+UUID+'.dat'), sep=r'\s+') # Load the data
Vext = np.asarray(data['Vext'].values)
Jext = np.asarray(data['Jext'].values)
G = np.ones_like(Vext)*Gfrac
rng = default_rng()#
noise = rng.standard_normal(Jext.shape) * 0.005 * Jext
if len(X) == 0:
X = np.vstack((Vext,G)).T
y = Jext + noise
y_orig = Jext
else:
X = np.vstack((X,np.vstack((Vext,G)).T))
y = np.hstack((y,Jext+ noise))
y_orig = np.hstack((y_orig,Jext))
# remove all the current where Jext is higher than a given value
X = X[y<200]
X_orig = copy.deepcopy(X)
y_orig = y_orig[y<200]
y = y[y<200]
plt.figure()
for Gfrac in Gfracs:
plt.plot(X[X[:,1]==Gfrac,0],y[X[:,1]==Gfrac],label='Gfrac = '+str(Gfrac))
plt.xlabel('Voltage [V]')
plt.ylabel('Current density [A/m$^2$]')
plt.legend()
plt.show()
Run the optimization
[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, y, session_path, simulation_setup, parallel = True, max_jobs = 3, metric = metric, loss = loss)
# Calulate the target metric for the original parameters
best_fit_possible = loss_function(calc_metric(y,y_orig, metric_name = metric),loss)
print('Best fit: ',best_fit_possible)
Best fit: 0.0016672829609957736
[5]:
# Define the optimizer
optimizer = PymooOptimizer(params=params, agents=jv, algorithm='GA', pop_size=20, n_gen=100, name='pymoo_single_obj', verbose_logging=True,max_parallelism=20, )
[ ]:
optimizer.optimize() # run the optimization
[INFO 12-08 09:09:32] optimpv.pymooOptimizer: Starting optimization using GA algorithm
[INFO 12-08 09:09:32] optimpv.pymooOptimizer: Population size: 20, Generations: 100
[INFO 12-08 09:09:33] optimpv.pymooOptimizer: Generation 1: Best objective = 0.157456
=================================================
n_gen | n_eval | f_avg | f_min
=================================================
1 | 20 | 0.2181420773 | 0.1574555273
[INFO 12-08 09:09:33] optimpv.pymooOptimizer: Generation 2: Best objective = 0.143601
2 | 40 | 0.1772419122 | 0.1436006222
[INFO 12-08 09:09:33] optimpv.pymooOptimizer: Generation 3: Best objective = 0.134452
3 | 60 | 0.1611067882 | 0.1344524713
[INFO 12-08 09:09:34] optimpv.pymooOptimizer: Generation 4: Best objective = 0.125117
4 | 80 | 0.1488762865 | 0.1251173586
[INFO 12-08 09:09:34] optimpv.pymooOptimizer: Generation 5: Best objective = 0.111330
5 | 100 | 0.1385777680 | 0.1113299456
[INFO 12-08 09:09:35] optimpv.pymooOptimizer: Generation 6: Best objective = 0.094492
6 | 120 | 0.1279739569 | 0.0944921498
[INFO 12-08 09:09:35] optimpv.pymooOptimizer: Generation 7: Best objective = 0.094492
7 | 140 | 0.1170703872 | 0.0944921498
[INFO 12-08 09:09:35] optimpv.pymooOptimizer: Generation 8: Best objective = 0.089646
8 | 160 | 0.1073697833 | 0.0896460222
[INFO 12-08 09:09:36] optimpv.pymooOptimizer: Generation 9: Best objective = 0.080188
9 | 180 | 0.0995710501 | 0.0801884742
[INFO 12-08 09:09:36] optimpv.pymooOptimizer: Generation 10: Best objective = 0.069847
10 | 200 | 0.0885836788 | 0.0698465409
[INFO 12-08 09:09:37] optimpv.pymooOptimizer: Generation 11: Best objective = 0.069847
11 | 220 | 0.0810962770 | 0.0698465409
[INFO 12-08 09:09:37] optimpv.pymooOptimizer: Generation 12: Best objective = 0.024337
12 | 240 | 0.0716161776 | 0.0243369537
[INFO 12-08 09:09:37] optimpv.pymooOptimizer: Generation 13: Best objective = 0.020038
13 | 260 | 0.0579086644 | 0.0200375892
[INFO 12-08 09:09:38] optimpv.pymooOptimizer: Generation 14: Best objective = 0.020038
14 | 280 | 0.0384411062 | 0.0200375892
[INFO 12-08 09:09:38] optimpv.pymooOptimizer: Generation 15: Best objective = 0.017661
15 | 300 | 0.0253145158 | 0.0176610593
[INFO 12-08 09:09:39] optimpv.pymooOptimizer: Generation 16: Best objective = 0.012084
16 | 320 | 0.0205375243 | 0.0120836707
[INFO 12-08 09:09:39] optimpv.pymooOptimizer: Generation 17: Best objective = 0.007367
17 | 340 | 0.0162956564 | 0.0073671365
[INFO 12-08 09:09:40] optimpv.pymooOptimizer: Generation 18: Best objective = 0.007367
18 | 360 | 0.0114146735 | 0.0073671365
[INFO 12-08 09:09:40] optimpv.pymooOptimizer: Generation 19: Best objective = 0.005270
19 | 380 | 0.0099350841 | 0.0052697998
[INFO 12-08 09:09:40] optimpv.pymooOptimizer: Generation 20: Best objective = 0.005078
20 | 400 | 0.0080293497 | 0.0050780904
[INFO 12-08 09:09:41] optimpv.pymooOptimizer: Generation 21: Best objective = 0.003378
21 | 420 | 0.0066594213 | 0.0033782316
[INFO 12-08 09:09:41] optimpv.pymooOptimizer: Generation 22: Best objective = 0.003378
22 | 440 | 0.0060959622 | 0.0033782316
[INFO 12-08 09:09:41] optimpv.pymooOptimizer: Generation 23: Best objective = 0.003378
23 | 460 | 0.0052459325 | 0.0033782316
[INFO 12-08 09:09:42] optimpv.pymooOptimizer: Generation 24: Best objective = 0.003378
24 | 480 | 0.0049152551 | 0.0033782316
[INFO 12-08 09:09:42] optimpv.pymooOptimizer: Generation 25: Best objective = 0.002736
25 | 500 | 0.0044450325 | 0.0027360158
[INFO 12-08 09:09:42] optimpv.pymooOptimizer: Generation 26: Best objective = 0.002736
26 | 520 | 0.0039569322 | 0.0027360158
[INFO 12-08 09:09:43] optimpv.pymooOptimizer: Generation 27: Best objective = 0.002736
27 | 540 | 0.0035039095 | 0.0027360158
[INFO 12-08 09:09:43] optimpv.pymooOptimizer: Generation 28: Best objective = 0.002503
28 | 560 | 0.0032220952 | 0.0025030626
[INFO 12-08 09:09:44] optimpv.pymooOptimizer: Generation 29: Best objective = 0.002471
29 | 580 | 0.0030308698 | 0.0024706621
[INFO 12-08 09:09:44] optimpv.pymooOptimizer: Generation 30: Best objective = 0.002471
30 | 600 | 0.0029361152 | 0.0024706621
[INFO 12-08 09:09:44] optimpv.pymooOptimizer: Generation 31: Best objective = 0.002245
31 | 620 | 0.0027119938 | 0.0022452282
[INFO 12-08 09:09:45] optimpv.pymooOptimizer: Generation 32: Best objective = 0.002245
32 | 640 | 0.0026242978 | 0.0022452282
[INFO 12-08 09:09:45] optimpv.pymooOptimizer: Generation 33: Best objective = 0.002245
33 | 660 | 0.0025788534 | 0.0022452282
[INFO 12-08 09:09:45] optimpv.pymooOptimizer: Generation 34: Best objective = 0.002245
34 | 680 | 0.0025240837 | 0.0022452282
[INFO 12-08 09:09:46] optimpv.pymooOptimizer: Generation 35: Best objective = 0.002245
35 | 700 | 0.0024507603 | 0.0022452282
[INFO 12-08 09:09:46] optimpv.pymooOptimizer: Generation 36: Best objective = 0.002245
36 | 720 | 0.0024147871 | 0.0022452282
[INFO 12-08 09:09:46] optimpv.pymooOptimizer: Generation 37: Best objective = 0.002245
37 | 740 | 0.0023584060 | 0.0022452282
[INFO 12-08 09:09:47] optimpv.pymooOptimizer: Generation 38: Best objective = 0.002110
38 | 760 | 0.0022780702 | 0.0021097612
[INFO 12-08 09:09:47] optimpv.pymooOptimizer: Generation 39: Best objective = 0.002110
39 | 780 | 0.0022267535 | 0.0021097597
[INFO 12-08 09:09:47] optimpv.pymooOptimizer: Generation 40: Best objective = 0.002110
40 | 800 | 0.0022193161 | 0.0021097597
[INFO 12-08 09:09:48] optimpv.pymooOptimizer: Generation 41: Best objective = 0.002110
41 | 820 | 0.0021988334 | 0.0021096931
[INFO 12-08 09:09:48] optimpv.pymooOptimizer: Generation 42: Best objective = 0.002096
42 | 840 | 0.0021499694 | 0.0020957796
[INFO 12-08 09:09:49] optimpv.pymooOptimizer: Generation 43: Best objective = 0.002047
43 | 860 | 0.0021114954 | 0.0020465374
[INFO 12-08 09:09:49] optimpv.pymooOptimizer: Generation 44: Best objective = 0.002047
44 | 880 | 0.0021036686 | 0.0020465374
[INFO 12-08 09:09:49] optimpv.pymooOptimizer: Generation 45: Best objective = 0.002047
45 | 900 | 0.0021000508 | 0.0020465374
[INFO 12-08 09:09:50] optimpv.pymooOptimizer: Generation 46: Best objective = 0.002047
46 | 920 | 0.0020950012 | 0.0020465374
[INFO 12-08 09:09:50] optimpv.pymooOptimizer: Generation 47: Best objective = 0.002047
47 | 940 | 0.0020945226 | 0.0020465374
[INFO 12-08 09:09:50] optimpv.pymooOptimizer: Generation 48: Best objective = 0.002044
48 | 960 | 0.0020803461 | 0.0020442911
[INFO 12-08 09:09:51] optimpv.pymooOptimizer: Generation 49: Best objective = 0.001854
49 | 980 | 0.0020626675 | 0.0018537904
[INFO 12-08 09:09:51] optimpv.pymooOptimizer: Generation 50: Best objective = 0.001854
50 | 1000 | 0.0020450974 | 0.0018537904
[INFO 12-08 09:09:51] optimpv.pymooOptimizer: Generation 51: Best objective = 0.001854
51 | 1020 | 0.0020245462 | 0.0018537899
[INFO 12-08 09:09:52] optimpv.pymooOptimizer: Generation 52: Best objective = 0.001854
52 | 1040 | 0.0020234912 | 0.0018537899
[INFO 12-08 09:09:52] optimpv.pymooOptimizer: Generation 53: Best objective = 0.001834
53 | 1060 | 0.0020013555 | 0.0018340181
[INFO 12-08 09:09:53] optimpv.pymooOptimizer: Generation 54: Best objective = 0.001823
54 | 1080 | 0.0019769086 | 0.0018226401
[INFO 12-08 09:09:53] optimpv.pymooOptimizer: Generation 55: Best objective = 0.001823
55 | 1100 | 0.0019151538 | 0.0018226401
[INFO 12-08 09:09:53] optimpv.pymooOptimizer: Generation 56: Best objective = 0.001823
56 | 1120 | 0.0018771175 | 0.0018226401
[INFO 12-08 09:09:54] optimpv.pymooOptimizer: Generation 57: Best objective = 0.001823
57 | 1140 | 0.0018406410 | 0.0018226401
[INFO 12-08 09:09:54] optimpv.pymooOptimizer: Generation 58: Best objective = 0.001823
58 | 1160 | 0.0018331370 | 0.0018226401
[INFO 12-08 09:09:54] optimpv.pymooOptimizer: Generation 59: Best objective = 0.001823
59 | 1180 | 0.0018286166 | 0.0018226401
[INFO 12-08 09:09:55] optimpv.pymooOptimizer: Generation 60: Best objective = 0.001823
60 | 1200 | 0.0018259443 | 0.0018226401
[INFO 12-08 09:09:55] optimpv.pymooOptimizer: Generation 61: Best objective = 0.001823
61 | 1220 | 0.0018250656 | 0.0018226401
[INFO 12-08 09:09:55] optimpv.pymooOptimizer: Generation 62: Best objective = 0.001823
62 | 1240 | 0.0018235952 | 0.0018226065
[INFO 12-08 09:09:56] optimpv.pymooOptimizer: Generation 63: Best objective = 0.001823
63 | 1260 | 0.0018230134 | 0.0018225919
[INFO 12-08 09:09:56] optimpv.pymooOptimizer: Generation 64: Best objective = 0.001822
64 | 1280 | 0.0018227115 | 0.0018218634
[INFO 12-08 09:09:56] optimpv.pymooOptimizer: Generation 65: Best objective = 0.001807
65 | 1300 | 0.0018218116 | 0.0018067023
[INFO 12-08 09:09:57] optimpv.pymooOptimizer: Generation 66: Best objective = 0.001807
66 | 1320 | 0.0018209570 | 0.0018067023
[INFO 12-08 09:09:57] optimpv.pymooOptimizer: Generation 67: Best objective = 0.001807
67 | 1340 | 0.0018200772 | 0.0018067023
[INFO 12-08 09:09:58] optimpv.pymooOptimizer: Generation 68: Best objective = 0.001807
68 | 1360 | 0.0018184333 | 0.0018067023
[INFO 12-08 09:09:58] optimpv.pymooOptimizer: Generation 69: Best objective = 0.001807
69 | 1380 | 0.0018151662 | 0.0018067023
[INFO 12-08 09:09:58] optimpv.pymooOptimizer: Generation 70: Best objective = 0.001807
70 | 1400 | 0.0018128357 | 0.0018067023
[INFO 12-08 09:09:59] optimpv.pymooOptimizer: Generation 71: Best objective = 0.001806
71 | 1420 | 0.0018082247 | 0.0018064618
[INFO 12-08 09:09:59] optimpv.pymooOptimizer: Generation 72: Best objective = 0.001806
72 | 1440 | 0.0018066785 | 0.0018064588
[INFO 12-08 09:09:59] optimpv.pymooOptimizer: Generation 73: Best objective = 0.001806
73 | 1460 | 0.0018066661 | 0.0018064554
[INFO 12-08 09:10:00] optimpv.pymooOptimizer: Generation 74: Best objective = 0.001804
74 | 1480 | 0.0018065127 | 0.0018038790
[INFO 12-08 09:10:00] optimpv.pymooOptimizer: Generation 75: Best objective = 0.001804
75 | 1500 | 0.0018064880 | 0.0018038790
[INFO 12-08 09:10:00] optimpv.pymooOptimizer: Generation 76: Best objective = 0.001804
76 | 1520 | 0.0018063338 | 0.0018038790
[INFO 12-08 09:10:01] optimpv.pymooOptimizer: Generation 77: Best objective = 0.001804
77 | 1540 | 0.0018062961 | 0.0018038790
[INFO 12-08 09:10:01] optimpv.pymooOptimizer: Generation 78: Best objective = 0.001804
78 | 1560 | 0.0018061280 | 0.0018038790
[INFO 12-08 09:10:01] optimpv.pymooOptimizer: Generation 79: Best objective = 0.001804
79 | 1580 | 0.0018059421 | 0.0018038790
[INFO 12-08 09:10:02] optimpv.pymooOptimizer: Generation 80: Best objective = 0.001804
80 | 1600 | 0.0018058082 | 0.0018038790
[INFO 12-08 09:10:02] optimpv.pymooOptimizer: Generation 81: Best objective = 0.001804
81 | 1620 | 0.0018055506 | 0.0018038790
[INFO 12-08 09:10:03] optimpv.pymooOptimizer: Generation 82: Best objective = 0.001804
82 | 1640 | 0.0018052907 | 0.0018038790
[INFO 12-08 09:10:03] optimpv.pymooOptimizer: Generation 83: Best objective = 0.001804
83 | 1660 | 0.0018048195 | 0.0018038790
[INFO 12-08 09:10:03] optimpv.pymooOptimizer: Generation 84: Best objective = 0.001804
84 | 1680 | 0.0018043174 | 0.0018038786
[INFO 12-08 09:10:04] optimpv.pymooOptimizer: Generation 85: Best objective = 0.001804
85 | 1700 | 0.0018038915 | 0.0018038766
[INFO 12-08 09:10:04] optimpv.pymooOptimizer: Generation 86: Best objective = 0.001804
86 | 1720 | 0.0018038793 | 0.0018038766
[INFO 12-08 09:10:04] optimpv.pymooOptimizer: Generation 87: Best objective = 0.001804
87 | 1740 | 0.0018038788 | 0.0018038766
[INFO 12-08 09:10:05] optimpv.pymooOptimizer: Generation 88: Best objective = 0.001804
88 | 1760 | 0.0018038787 | 0.0018038766
[INFO 12-08 09:10:05] optimpv.pymooOptimizer: Generation 89: Best objective = 0.001804
89 | 1780 | 0.0018038784 | 0.0018038765
[INFO 12-08 09:10:05] optimpv.pymooOptimizer: Generation 90: Best objective = 0.001804
90 | 1800 | 0.0018038781 | 0.0018038765
[INFO 12-08 09:10:06] optimpv.pymooOptimizer: Generation 91: Best objective = 0.001804
91 | 1820 | 0.0018038777 | 0.0018038745
[INFO 12-08 09:10:06] optimpv.pymooOptimizer: Generation 92: Best objective = 0.001804
92 | 1840 | 0.0018038772 | 0.0018038745
[INFO 12-08 09:10:06] optimpv.pymooOptimizer: Generation 93: Best objective = 0.001804
93 | 1860 | 0.0018038766 | 0.0018038745
[INFO 12-08 09:10:07] optimpv.pymooOptimizer: Generation 94: Best objective = 0.001804
94 | 1880 | 0.0018038760 | 0.0018038745
[INFO 12-08 09:10:07] optimpv.pymooOptimizer: Generation 95: Best objective = 0.001804
95 | 1900 | 0.0018038756 | 0.0018038744
[INFO 12-08 09:10:08] optimpv.pymooOptimizer: Generation 96: Best objective = 0.001804
96 | 1920 | 0.0018038752 | 0.0018038742
[INFO 12-08 09:10:08] optimpv.pymooOptimizer: Generation 97: Best objective = 0.001804
97 | 1940 | 0.0018038746 | 0.0018038740
[INFO 12-08 09:10:08] optimpv.pymooOptimizer: Generation 98: Best objective = 0.001804
98 | 1960 | 0.0018038744 | 0.0018038740
[INFO 12-08 09:10:09] optimpv.pymooOptimizer: Generation 99: Best objective = 0.001804
99 | 1980 | 0.0018038743 | 0.0018038740
[INFO 12-08 09:10:09] optimpv.pymooOptimizer: Generation 100: Best objective = 0.001804
[INFO 12-08 09:10:09] optimpv.pymooOptimizer: Optimization completed after 101 generations
[INFO 12-08 09:10:09] optimpv.pymooOptimizer: Number of function evaluations: 2000
[INFO 12-08 09:10:09] optimpv.pymooOptimizer: Best objective value: 0.001804
100 | 2000 | 0.0018038743 | 0.0018038740
<pymoo.core.result.Result at 0x7cdff0259450>
[7]:
# get the best parameters and update the params list in the optimizer and the agent
optimizer.update_params_with_best_balance()
jv.params = optimizer.params # update the params list in the agent with the best parameters
# print the best parameters
print('Best parameters:')
for p,po in zip(optimizer.params, params_orig):
print(p.name, 'fitted value:', p.value, 'original value:', po.value)
print('\nSimSS command line:')
print(jv.get_SIMsalabim_clean_cmd(jv.params)) # print the simss command line with the best parameters
Best parameters:
l2.mu_n fitted value: 5.73829786902353e-08 original value: 7e-08
l2.mu_p fitted value: 4.782789665325164e-08 original value: 5e-08
l2.N_t_bulk fitted value: 1.0159002279063536e+20 original value: 1e+20
l2.preLangevin fitted value: 0.01109467912661712 original value: 0.01
R_series fitted value: 7.898524752656494e-05 original value: 0.0001
SimSS command line:
./simss -l2.mu_n 5.73829786902353e-08 -l2.mu_p 4.782789665325164e-08 -l2.N_t_bulk 1.0159002279063536e+20 -l2.preLangevin 0.01109467912661712 -R_series 7.898524752656494e-05
[8]:
optimizer.plot_convergence(yscale='log', xscale='linear')
[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()
[10]:
# 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('ZnO',session_path)
sim.clean_up_output('ActiveLayer',session_path)
sim.clean_up_output('BM_HTL',session_path)
sim.clean_up_output('simulation_setup_fakeOPV',session_path)