Multi-objective BO: OPV light-intensity dependant JV fits with SIMsalabim (real data)

This notebook is a demonstration of how to fit light-intensity dependent JV curves with drift-diffusion models using the SIMsalabim package.

# 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
from scipy import constants
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.axBOtorch.axUtils import *
except Exception as e:
    sys.path.append('../') # add the path to the optimpv module
    from optimpv import *
    from optimpv.axBOtorch.axUtils import *

[INFO 12-08 08:52:50] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.
[INFO 12-08 08:52:50] 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

# 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','JVrealOPV')))
simulation_setup_filename = 'simulation_setup_PM6_L8BO.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 = 'PM6_L8BO.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)
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
# 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)

# import JV data
df = pd.read_csv(os.path.join(input_path, 'JV_PM6_L8BO.dat'), sep=' ')
# X = vext,gfrac columns
X = df[['Vext','Gfrac']].values
y = df[['Jext']].values.reshape(-1)
# same order of the Gfrac as they appear in the data
Gfracs = pd.unique(df['Gfrac'])
# Gfracs = np.unique(df[['Gfrac']].values)
print(f'Gfracs = {Gfracs}')
X_1sun = df[['Vext','Gfrac']].values[df['Gfrac'] == 1.0]
y_1sun = df[['Jext']].values[df['Gfrac'] == 1.0].reshape(-1)
# get 1sun Jsc as it will help narrow down the range for Gehp
Jsc_1sun = np.interp(0.0, X_1sun[:,0], y_1sun)
minJ = np.min(y_1sun)
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))
# get Voc
Voc_1sun = np.interp(0.0, y_1sun, X_1sun[:,0])
print(f'Voc_1sun = {Voc_1sun} V')
# Filter some data
Vmin = -0.1
Jmax = abs(Jsc_1sun)
idx = np.where((X[:,0] > Vmin) & (y < Jmax))
y = y[idx]
X = X[idx]

# get the data for each Gfrac with will be used in the different agents later
X1 = X[X[:,1] == Gfracs[0]]
y1 = y[X[:,1] == Gfracs[0]]
X2 = X[X[:,1] == Gfracs[1]]
y2 = y[X[:,1] == Gfracs[1]]
X3 = X[X[:,1] == Gfracs[2]]
y3 = y[X[:,1] == Gfracs[2]]


# plot the data for each Gfrac
plt.figure(figsize=(10,10))
viridis = plt.get_cmap('viridis', len(Gfracs))
for i, Gfrac in enumerate(Gfracs):
    plt.plot(X[X[:,1] == Gfrac][:,0], y[X[:,1] == Gfrac], '-', label = f'Gfrac = {Gfrac}', color = viridis(i))
plt.grid()
plt.xlabel('Voltage [V]')
plt.ylabel('Current density [A m$^{-2}$]')
plt.legend()
plt.show()

Gfracs = [1.  0.1 0.5]
Voc_1sun = 0.8613743287822753 V

Define the parameters for the simulation

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 = [1e16,8e22], log_scale = True, value_type = 'float', fscale = None, rescale = False,  display_name=r'$N_{T}$', unit=r'm$^{-3}$', axis_type = 'log', force_log = True)
params.append(bulk_tr)

preLangevin = FitParam(name = 'l2.preLangevin', value = 1e-2, bounds = [1e-3,1], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$\gamma_{pre}$', unit=r'', axis_type = 'log', force_log = True)
params.append(preLangevin)

R_series = FitParam(name = 'R_series', value = 1e-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 = False)
params.append(R_series)

R_shunt = FitParam(name = 'R_shunt', value = 1e1, 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 = False)
params.append(R_shunt)

G_ehp = FitParam(name = 'l2.G_ehp', value = G_ehp_calc, bounds = [G_ehp_calc*0.95,G_ehp_max*1.05], log_scale = False, value_type = 'float', fscale = None, rescale = False,  display_name=r'$G_{ehp}$', unit=r'm$^{-3}$ s$^{-1}$', axis_type = 'linear', force_log = False)
params.append(G_ehp)

# save the original parameters for later
params_orig = copy.deepcopy(params)
num_free_params = len([p for p in params if p.type != 'fixed'])

Run the optimization

# 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'
threshold = 0.05 # need this to get a reference point for the hypervolume calculation
jv_main = JVAgent(params, X, y, session_path, simulation_setup, parallel = True, max_jobs = 3, metric = metric, loss = loss) # agent with ALL the light intensities, not necessary for the MOO but useful later
# The agents below need to add a different name for each agent otherwise they will overwrite each other in the jv.run_Ax output dictionary
jv1 = JVAgent(params, X1, y1, session_path, simulation_setup, parallel = True, max_jobs = 1, metric = metric, loss = loss, threshold=threshold,name='Gfrac1') # agent with Gfrac 1
jv2 = JVAgent(params, X2, y2, session_path, simulation_setup, parallel = True, max_jobs = 1, metric = metric, loss = loss, threshold=threshold,name='Gfrac2') # agent with Gfrac 2
jv3 = JVAgent(params, X3, y3, session_path, simulation_setup, parallel = True, max_jobs = 1, metric = metric, loss = loss, threshold=threshold,name='Gfrac3') # agent with Gfrac 3

from optimpv.axBOtorch.axBOtorchOptimizer import axBOtorchOptimizer
from optimpv.axBOtorch.axUtils import get_VMLC_default_model_kwargs_list

optimizer = axBOtorchOptimizer(params = params, agents = [jv1,jv2,jv3], models = ['CENTER','SOBOL','BOTORCH_MODULAR'],n_batches = [1,1,60], batch_size = [1,10,2], max_parallelism = 100, model_kwargs_list = get_VMLC_default_model_kwargs_list(num_free_params,use_CENTER=True,is_MOO=True))

optimizer.optimize() # run the optimization with ax

[INFO 12-08 08:52:51] optimpv.axBOtorchOptimizer: Trial 0 with parameters: {'l2.mu_n': -7.5, 'l2.mu_p': -7.5, 'l2.N_t_bulk': 19.451544993495972, 'l2.preLangevin': -1.5, 'R_series': 0.0001, 'R_shunt': 1.0, 'l2.G_ehp': 1.4669272681272731e+28}
[INFO 12-08 08:52:52] optimpv.axBOtorchOptimizer: Trial 0 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.13334896922864783), 'Gfrac2_JV_nrmse_linear': np.float64(0.08254634225523515), 'Gfrac3_JV_nrmse_linear': np.float64(0.11049013404965334)} and parameters: {'l2.mu_n': -7.5, 'l2.mu_p': -7.5, 'l2.N_t_bulk': 19.451544993495972, 'l2.preLangevin': -1.5, 'R_series': 0.0001, 'R_shunt': 1.0, 'l2.G_ehp': 1.4669272681272731e+28}
[INFO 12-08 08:52:52] optimpv.axBOtorchOptimizer: Trial 1 with parameters: {'l2.mu_n': -6.521711197681725, 'l2.mu_p': -8.44046551734209, 'l2.N_t_bulk': 20.05074179786675, 'l2.preLangevin': -1.5011896658688784, 'R_series': 5.548023179690129e-06, 'R_shunt': 0.040143778993377906, 'l2.G_ehp': 1.47138805037575e+28}
[INFO 12-08 08:52:52] optimpv.axBOtorchOptimizer: Trial 2 with parameters: {'l2.mu_n': -8.768277366645634, 'l2.mu_p': -6.144868760369718, 'l2.N_t_bulk': 16.75984594703916, 'l2.preLangevin': -0.22261227574199438, 'R_series': 0.009581618338336085, 'R_shunt': 1.4637797583947922, 'l2.G_ehp': 1.410101066988452e+28}
[INFO 12-08 08:52:52] optimpv.axBOtorchOptimizer: Trial 3 with parameters: {'l2.mu_n': -7.575900999829173, 'l2.mu_p': -7.638125874102116, 'l2.N_t_bulk': 22.794438097772648, 'l2.preLangevin': -2.654807542450726, 'R_series': 0.00011114258765788797, 'R_shunt': 31.088726302770475, 'l2.G_ehp': 1.5415967307439826e+28}
[INFO 12-08 08:52:52] optimpv.axBOtorchOptimizer: Trial 4 with parameters: {'l2.mu_n': -6.829424908384681, 'l2.mu_p': -7.0292865028604865, 'l2.N_t_bulk': 19.072046507793093, 'l2.preLangevin': -1.3175269737839699, 'R_series': 2.0350371103671894e-05, 'R_shunt': 0.6421466805240446, 'l2.G_ehp': 1.4332106775885578e+28}
[INFO 12-08 08:52:52] optimpv.axBOtorchOptimizer: Trial 5 with parameters: {'l2.mu_n': -7.298891002312303, 'l2.mu_p': -8.115406855009496, 'l2.N_t_bulk': 17.248540999999204, 'l2.preLangevin': -1.0050850007683039, 'R_series': 4.22322396920176e-05, 'R_shunt': 0.01854443903276296, 'l2.G_ehp': 1.5149196991437895e+28}
[INFO 12-08 08:52:52] optimpv.axBOtorchOptimizer: Trial 6 with parameters: {'l2.mu_n': -8.043992327526212, 'l2.mu_p': -7.22606136649847, 'l2.N_t_bulk': 20.43156988507864, 'l2.preLangevin': -2.333615846000612, 'R_series': 0.0007873288841534642, 'R_shunt': 9.013374966210094, 'l2.G_ehp': 1.458313811837928e+28}
[INFO 12-08 08:52:52] optimpv.axBOtorchOptimizer: Trial 7 with parameters: {'l2.mu_n': -8.29872137401253, 'l2.mu_p': -8.801258160732687, 'l2.N_t_bulk': 17.821590050479337, 'l2.preLangevin': -0.6484995754435658, 'R_series': 0.0012179254586871134, 'R_shunt': 42.24699166821304, 'l2.G_ehp': 1.492202062881649e+28}
[INFO 12-08 08:52:52] optimpv.axBOtorchOptimizer: Trial 8 with parameters: {'l2.mu_n': -6.0537101263180375, 'l2.mu_p': -6.5986675545573235, 'l2.N_t_bulk': 21.436114674710737, 'l2.preLangevin': -1.9416631758213043, 'R_series': 2.0659334490163262e-06, 'R_shunt': 0.11644789344306836, 'l2.G_ehp': 1.3906812370564558e+28}
[INFO 12-08 08:52:52] optimpv.axBOtorchOptimizer: Trial 9 with parameters: {'l2.mu_n': -6.21659005433321, 'l2.mu_p': -7.803713570348918, 'l2.N_t_bulk': 18.333725951105738, 'l2.preLangevin': -2.445695595815778, 'R_series': 0.004117688403576332, 'R_shunt': 0.3721337779659989, 'l2.G_ehp': 1.5357163091161393e+28}
[INFO 12-08 08:52:52] optimpv.axBOtorchOptimizer: Trial 10 with parameters: {'l2.mu_n': -8.470023795962334, 'l2.mu_p': -6.819890130311251, 'l2.N_t_bulk': 21.949080071089817, 'l2.preLangevin': -0.777373923920095, 'R_series': 6.984596817704381e-06, 'R_shunt': 13.670826214449937, 'l2.G_ehp': 1.4507268961043469e+28}
[INFO 12-08 08:52:54] optimpv.axBOtorchOptimizer: Trial 1 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.19953193061920688), 'Gfrac2_JV_nrmse_linear': np.float64(0.15819605177758886), 'Gfrac3_JV_nrmse_linear': np.float64(0.1766750514118032)} and parameters: {'l2.mu_n': -6.521711197681725, 'l2.mu_p': -8.44046551734209, 'l2.N_t_bulk': 20.05074179786675, 'l2.preLangevin': -1.5011896658688784, 'R_series': 5.548023179690129e-06, 'R_shunt': 0.040143778993377906, 'l2.G_ehp': 1.47138805037575e+28}
[INFO 12-08 08:52:54] optimpv.axBOtorchOptimizer: Trial 2 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.4598848750943911), 'Gfrac2_JV_nrmse_linear': np.float64(0.17262492668181656), 'Gfrac3_JV_nrmse_linear': np.float64(0.29744651275276207)} and parameters: {'l2.mu_n': -8.768277366645634, 'l2.mu_p': -6.144868760369718, 'l2.N_t_bulk': 16.75984594703916, 'l2.preLangevin': -0.22261227574199438, 'R_series': 0.009581618338336085, 'R_shunt': 1.4637797583947922, 'l2.G_ehp': 1.410101066988452e+28}
[INFO 12-08 08:52:54] optimpv.axBOtorchOptimizer: Trial 3 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.48602351035079333), 'Gfrac2_JV_nrmse_linear': np.float64(0.13967929700041132), 'Gfrac3_JV_nrmse_linear': np.float64(0.33097905647503123)} and parameters: {'l2.mu_n': -7.575900999829173, 'l2.mu_p': -7.638125874102116, 'l2.N_t_bulk': 22.794438097772648, 'l2.preLangevin': -2.654807542450726, 'R_series': 0.00011114258765788797, 'R_shunt': 31.088726302770475, 'l2.G_ehp': 1.5415967307439826e+28}
[INFO 12-08 08:52:54] optimpv.axBOtorchOptimizer: Trial 4 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.17581791235569683), 'Gfrac2_JV_nrmse_linear': np.float64(0.18387089334394088), 'Gfrac3_JV_nrmse_linear': np.float64(0.17973401543874704)} and parameters: {'l2.mu_n': -6.829424908384681, 'l2.mu_p': -7.0292865028604865, 'l2.N_t_bulk': 19.072046507793093, 'l2.preLangevin': -1.3175269737839699, 'R_series': 2.0350371103671894e-05, 'R_shunt': 0.6421466805240446, 'l2.G_ehp': 1.4332106775885578e+28}
[INFO 12-08 08:52:54] optimpv.axBOtorchOptimizer: Trial 5 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.22913702521606136), 'Gfrac2_JV_nrmse_linear': np.float64(0.14949904454666774), 'Gfrac3_JV_nrmse_linear': np.float64(0.1921331109867857)} and parameters: {'l2.mu_n': -7.298891002312303, 'l2.mu_p': -8.115406855009496, 'l2.N_t_bulk': 17.248540999999204, 'l2.preLangevin': -1.0050850007683039, 'R_series': 4.22322396920176e-05, 'R_shunt': 0.01854443903276296, 'l2.G_ehp': 1.5149196991437895e+28}
[INFO 12-08 08:52:54] optimpv.axBOtorchOptimizer: Trial 6 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.18063648250582143), 'Gfrac2_JV_nrmse_linear': np.float64(0.10622699775517873), 'Gfrac3_JV_nrmse_linear': np.float64(0.1192453974420719)} and parameters: {'l2.mu_n': -8.043992327526212, 'l2.mu_p': -7.22606136649847, 'l2.N_t_bulk': 20.43156988507864, 'l2.preLangevin': -2.333615846000612, 'R_series': 0.0007873288841534642, 'R_shunt': 9.013374966210094, 'l2.G_ehp': 1.458313811837928e+28}
[INFO 12-08 08:52:54] optimpv.axBOtorchOptimizer: Trial 7 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.33902556917668525), 'Gfrac2_JV_nrmse_linear': np.float64(0.16526279501805402), 'Gfrac3_JV_nrmse_linear': np.float64(0.21099584139457914)} and parameters: {'l2.mu_n': -8.29872137401253, 'l2.mu_p': -8.801258160732687, 'l2.N_t_bulk': 17.821590050479337, 'l2.preLangevin': -0.6484995754435658, 'R_series': 0.0012179254586871134, 'R_shunt': 42.24699166821304, 'l2.G_ehp': 1.492202062881649e+28}
[INFO 12-08 08:52:54] optimpv.axBOtorchOptimizer: Trial 8 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.2011581779550703), 'Gfrac2_JV_nrmse_linear': np.float64(0.21458969960315236), 'Gfrac3_JV_nrmse_linear': np.float64(0.20852959515309966)} and parameters: {'l2.mu_n': -6.0537101263180375, 'l2.mu_p': -6.5986675545573235, 'l2.N_t_bulk': 21.436114674710737, 'l2.preLangevin': -1.9416631758213043, 'R_series': 2.0659334490163262e-06, 'R_shunt': 0.11644789344306836, 'l2.G_ehp': 1.3906812370564558e+28}
[INFO 12-08 08:52:54] optimpv.axBOtorchOptimizer: Trial 9 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.3364300149463626), 'Gfrac2_JV_nrmse_linear': np.float64(0.15363194214915413), 'Gfrac3_JV_nrmse_linear': np.float64(0.1903011401614004)} and parameters: {'l2.mu_n': -6.21659005433321, 'l2.mu_p': -7.803713570348918, 'l2.N_t_bulk': 18.333725951105738, 'l2.preLangevin': -2.445695595815778, 'R_series': 0.004117688403576332, 'R_shunt': 0.3721337779659989, 'l2.G_ehp': 1.5357163091161393e+28}
[INFO 12-08 08:52:54] optimpv.axBOtorchOptimizer: Trial 10 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.3131922843019598), 'Gfrac2_JV_nrmse_linear': np.float64(0.12883078495207534), 'Gfrac3_JV_nrmse_linear': np.float64(0.23332143116679188)} and parameters: {'l2.mu_n': -8.470023795962334, 'l2.mu_p': -6.819890130311251, 'l2.N_t_bulk': 21.949080071089817, 'l2.preLangevin': -0.777373923920095, 'R_series': 6.984596817704381e-06, 'R_shunt': 13.670826214449937, 'l2.G_ehp': 1.4507268961043469e+28}
[INFO 12-08 08:52:57] optimpv.axBOtorchOptimizer: Trial 11 with parameters: {'l2.mu_n': -8.226961407180516, 'l2.mu_p': -7.584361940614345, 'l2.N_t_bulk': 19.601087217867928, 'l2.preLangevin': -3.0, 'R_series': 0.01, 'R_shunt': 4.138125574622321, 'l2.G_ehp': 1.4648076655172653e+28}
[INFO 12-08 08:52:57] optimpv.axBOtorchOptimizer: Trial 12 with parameters: {'l2.mu_n': -6.892481509176457, 'l2.mu_p': -7.481593664650604, 'l2.N_t_bulk': 19.98702872273711, 'l2.preLangevin': -3.0, 'R_series': 0.01, 'R_shunt': 100.0, 'l2.G_ehp': 1.4684146831671168e+28}
[INFO 12-08 08:52:59] optimpv.axBOtorchOptimizer: Trial 11 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.41142388866220464), 'Gfrac2_JV_nrmse_linear': np.float64(0.1795576830690904), 'Gfrac3_JV_nrmse_linear': np.float64(0.24275695240152084)} and parameters: {'l2.mu_n': -8.226961407180516, 'l2.mu_p': -7.584361940614345, 'l2.N_t_bulk': 19.601087217867928, 'l2.preLangevin': -3.0, 'R_series': 0.01, 'R_shunt': 4.138125574622321, 'l2.G_ehp': 1.4648076655172653e+28}
[INFO 12-08 08:52:59] optimpv.axBOtorchOptimizer: Trial 12 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.41734767420279234), 'Gfrac2_JV_nrmse_linear': np.float64(0.17591984581539377), 'Gfrac3_JV_nrmse_linear': np.float64(0.24709578856140377)} and parameters: {'l2.mu_n': -6.892481509176457, 'l2.mu_p': -7.481593664650604, 'l2.N_t_bulk': 19.98702872273711, 'l2.preLangevin': -3.0, 'R_series': 0.01, 'R_shunt': 100.0, 'l2.G_ehp': 1.4684146831671168e+28}
[INFO 12-08 08:53:01] optimpv.axBOtorchOptimizer: Trial 13 with parameters: {'l2.mu_n': -7.73432492025592, 'l2.mu_p': -7.313608370503667, 'l2.N_t_bulk': 20.073363911638488, 'l2.preLangevin': -2.046345099408277, 'R_series': 0.0001785437137759873, 'R_shunt': 1.4307005249532567, 'l2.G_ehp': 1.4627598663027381e+28}
[INFO 12-08 08:53:01] optimpv.axBOtorchOptimizer: Trial 14 with parameters: {'l2.mu_n': -6.106555673450589, 'l2.mu_p': -9.0, 'l2.N_t_bulk': 19.449262448730458, 'l2.preLangevin': 0.0, 'R_series': 0.0001935964731852094, 'R_shunt': 44.43089780922503, 'l2.G_ehp': 1.459376936740506e+28}
[INFO 12-08 08:53:02] optimpv.axBOtorchOptimizer: Trial 13 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.11187356840823713), 'Gfrac2_JV_nrmse_linear': np.float64(0.049911525318898795), 'Gfrac3_JV_nrmse_linear': np.float64(0.08247708148476593)} and parameters: {'l2.mu_n': -7.73432492025592, 'l2.mu_p': -7.313608370503667, 'l2.N_t_bulk': 20.073363911638488, 'l2.preLangevin': -2.046345099408277, 'R_series': 0.0001785437137759873, 'R_shunt': 1.4307005249532567, 'l2.G_ehp': 1.4627598663027381e+28}
[INFO 12-08 08:53:02] optimpv.axBOtorchOptimizer: Trial 14 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.3440214024165165), 'Gfrac2_JV_nrmse_linear': np.float64(0.16523058509659413), 'Gfrac3_JV_nrmse_linear': np.float64(0.2518758195909251)} and parameters: {'l2.mu_n': -6.106555673450589, 'l2.mu_p': -9.0, 'l2.N_t_bulk': 19.449262448730458, 'l2.preLangevin': 0.0, 'R_series': 0.0001935964731852094, 'R_shunt': 44.43089780922503, 'l2.G_ehp': 1.459376936740506e+28}
[INFO 12-08 08:53:04] optimpv.axBOtorchOptimizer: Trial 15 with parameters: {'l2.mu_n': -8.906990641012166, 'l2.mu_p': -7.653536009840525, 'l2.N_t_bulk': 19.437750924187757, 'l2.preLangevin': -1.9389321836584867, 'R_series': 0.0001870823436604645, 'R_shunt': 0.026984672173435698, 'l2.G_ehp': 1.4648930151608307e+28}
[INFO 12-08 08:53:04] optimpv.axBOtorchOptimizer: Trial 16 with parameters: {'l2.mu_n': -6.0, 'l2.mu_p': -6.6087684594671945, 'l2.N_t_bulk': 20.643871113664378, 'l2.preLangevin': -1.902202256549553, 'R_series': 0.0001714795306665213, 'R_shunt': 100.0, 'l2.G_ehp': 1.4616205428355315e+28}
[INFO 12-08 08:53:04] optimpv.axBOtorchOptimizer: Trial 15 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.2624807821445182), 'Gfrac2_JV_nrmse_linear': np.float64(0.11580493985949701), 'Gfrac3_JV_nrmse_linear': np.float64(0.17196857370363805)} and parameters: {'l2.mu_n': -8.906990641012166, 'l2.mu_p': -7.653536009840525, 'l2.N_t_bulk': 19.437750924187757, 'l2.preLangevin': -1.9389321836584867, 'R_series': 0.0001870823436604645, 'R_shunt': 0.026984672173435698, 'l2.G_ehp': 1.4648930151608307e+28}
[INFO 12-08 08:53:04] optimpv.axBOtorchOptimizer: Trial 16 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.18216678694031047), 'Gfrac2_JV_nrmse_linear': np.float64(0.1890489043112165), 'Gfrac3_JV_nrmse_linear': np.float64(0.18457967550158963)} and parameters: {'l2.mu_n': -6.0, 'l2.mu_p': -6.6087684594671945, 'l2.N_t_bulk': 20.643871113664378, 'l2.preLangevin': -1.902202256549553, 'R_series': 0.0001714795306665213, 'R_shunt': 100.0, 'l2.G_ehp': 1.4616205428355315e+28}
[INFO 12-08 08:53:07] optimpv.axBOtorchOptimizer: Trial 17 with parameters: {'l2.mu_n': -7.730321225118663, 'l2.mu_p': -7.208427697851139, 'l2.N_t_bulk': 19.92474474513552, 'l2.preLangevin': -1.8905195158711856, 'R_series': 0.0001526178827527835, 'R_shunt': 1.5149199045548505, 'l2.G_ehp': 1.4581600760529914e+28}
[INFO 12-08 08:53:07] optimpv.axBOtorchOptimizer: Trial 18 with parameters: {'l2.mu_n': -7.711927211795485, 'l2.mu_p': -8.13198240157448, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -1.884829207848433, 'R_series': 1e-06, 'R_shunt': 16.674867958862205, 'l2.G_ehp': 1.4703892567022591e+28}
[INFO 12-08 08:53:08] optimpv.axBOtorchOptimizer: Trial 17 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.11964684312394307), 'Gfrac2_JV_nrmse_linear': np.float64(0.0647559316135506), 'Gfrac3_JV_nrmse_linear': np.float64(0.09330190043089077)} and parameters: {'l2.mu_n': -7.730321225118663, 'l2.mu_p': -7.208427697851139, 'l2.N_t_bulk': 19.92474474513552, 'l2.preLangevin': -1.8905195158711856, 'R_series': 0.0001526178827527835, 'R_shunt': 1.5149199045548505, 'l2.G_ehp': 1.4581600760529914e+28}
[INFO 12-08 08:53:08] optimpv.axBOtorchOptimizer: Trial 18 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.09800833798174453), 'Gfrac2_JV_nrmse_linear': np.float64(0.03406234926364753), 'Gfrac3_JV_nrmse_linear': np.float64(0.06632499348334195)} and parameters: {'l2.mu_n': -7.711927211795485, 'l2.mu_p': -8.13198240157448, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -1.884829207848433, 'R_series': 1e-06, 'R_shunt': 16.674867958862205, 'l2.G_ehp': 1.4703892567022591e+28}
[INFO 12-08 08:53:10] optimpv.axBOtorchOptimizer: Trial 19 with parameters: {'l2.mu_n': -7.6979949402684875, 'l2.mu_p': -8.883070242322104, 'l2.N_t_bulk': 18.110873867058423, 'l2.preLangevin': -1.9054713664236858, 'R_series': 5.56025075774701e-06, 'R_shunt': 0.8500999824776709, 'l2.G_ehp': 1.4776087406955772e+28}
[INFO 12-08 08:53:10] optimpv.axBOtorchOptimizer: Trial 20 with parameters: {'l2.mu_n': -7.830488690176405, 'l2.mu_p': -6.0, 'l2.N_t_bulk': 18.056883748523678, 'l2.preLangevin': -1.8755838794522683, 'R_series': 0.01, 'R_shunt': 100.0, 'l2.G_ehp': 1.4771474566282884e+28}
[INFO 12-08 08:53:10] optimpv.axBOtorchOptimizer: Trial 19 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.21531205391132627), 'Gfrac2_JV_nrmse_linear': np.float64(0.07912859751706539), 'Gfrac3_JV_nrmse_linear': np.float64(0.11961140777964095)} and parameters: {'l2.mu_n': -7.6979949402684875, 'l2.mu_p': -8.883070242322104, 'l2.N_t_bulk': 18.110873867058423, 'l2.preLangevin': -1.9054713664236858, 'R_series': 5.56025075774701e-06, 'R_shunt': 0.8500999824776709, 'l2.G_ehp': 1.4776087406955772e+28}
[INFO 12-08 08:53:10] optimpv.axBOtorchOptimizer: Trial 20 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.43730094258293456), 'Gfrac2_JV_nrmse_linear': np.float64(0.17152767963688378), 'Gfrac3_JV_nrmse_linear': np.float64(0.2679420796260175)} and parameters: {'l2.mu_n': -7.830488690176405, 'l2.mu_p': -6.0, 'l2.N_t_bulk': 18.056883748523678, 'l2.preLangevin': -1.8755838794522683, 'R_series': 0.01, 'R_shunt': 100.0, 'l2.G_ehp': 1.4771474566282884e+28}
[INFO 12-08 08:53:13] optimpv.axBOtorchOptimizer: Trial 21 with parameters: {'l2.mu_n': -7.709817514571094, 'l2.mu_p': -7.043087083321207, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.4188786109267193, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.466740125275219e+28}
[INFO 12-08 08:53:13] optimpv.axBOtorchOptimizer: Trial 22 with parameters: {'l2.mu_n': -7.972905228243377, 'l2.mu_p': -6.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -1.6642038048356802, 'R_series': 1e-06, 'R_shunt': 0.01, 'l2.G_ehp': 1.4662326889640435e+28}
[INFO 12-08 08:53:13] optimpv.axBOtorchOptimizer: Trial 21 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.07938950530081022), 'Gfrac2_JV_nrmse_linear': np.float64(0.09172878205313055), 'Gfrac3_JV_nrmse_linear': np.float64(0.08484648873829184)} and parameters: {'l2.mu_n': -7.709817514571094, 'l2.mu_p': -7.043087083321207, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.4188786109267193, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.466740125275219e+28}
[INFO 12-08 08:53:13] optimpv.axBOtorchOptimizer: Trial 22 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.20616167940140007), 'Gfrac2_JV_nrmse_linear': np.float64(0.20440607373395128), 'Gfrac3_JV_nrmse_linear': np.float64(0.20459129720580949)} and parameters: {'l2.mu_n': -7.972905228243377, 'l2.mu_p': -6.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -1.6642038048356802, 'R_series': 1e-06, 'R_shunt': 0.01, 'l2.G_ehp': 1.4662326889640435e+28}
[INFO 12-08 08:53:16] optimpv.axBOtorchOptimizer: Trial 23 with parameters: {'l2.mu_n': -7.6723527689325195, 'l2.mu_p': -8.511770063146788, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.188451840838175, 'R_series': 1e-06, 'R_shunt': 15.29768495028256, 'l2.G_ehp': 1.4705510721283744e+28}
[INFO 12-08 08:53:16] optimpv.axBOtorchOptimizer: Trial 24 with parameters: {'l2.mu_n': -7.96828889221489, 'l2.mu_p': -6.037016636607921, 'l2.N_t_bulk': 20.870130898452125, 'l2.preLangevin': -2.0039309456810335, 'R_series': 1e-06, 'R_shunt': 23.380191530284552, 'l2.G_ehp': 1.4782833432315943e+28}
[INFO 12-08 08:53:16] optimpv.axBOtorchOptimizer: Trial 23 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.11455794497874908), 'Gfrac2_JV_nrmse_linear': np.float64(0.04347622802531014), 'Gfrac3_JV_nrmse_linear': np.float64(0.06317607516493275)} and parameters: {'l2.mu_n': -7.6723527689325195, 'l2.mu_p': -8.511770063146788, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.188451840838175, 'R_series': 1e-06, 'R_shunt': 15.29768495028256, 'l2.G_ehp': 1.4705510721283744e+28}
[INFO 12-08 08:53:16] optimpv.axBOtorchOptimizer: Trial 24 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.17280928280315636), 'Gfrac2_JV_nrmse_linear': np.float64(0.17328174356531167), 'Gfrac3_JV_nrmse_linear': np.float64(0.17146725270306332)} and parameters: {'l2.mu_n': -7.96828889221489, 'l2.mu_p': -6.037016636607921, 'l2.N_t_bulk': 20.870130898452125, 'l2.preLangevin': -2.0039309456810335, 'R_series': 1e-06, 'R_shunt': 23.380191530284552, 'l2.G_ehp': 1.4782833432315943e+28}
[INFO 12-08 08:53:17] optimpv.axBOtorchOptimizer: Trial 25 with parameters: {'l2.mu_n': -7.710420844177488, 'l2.mu_p': -8.211578574430053, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -1.9330266625301795, 'R_series': 1.6838810661403534e-06, 'R_shunt': 36.8528904623608, 'l2.G_ehp': 1.471671938862206e+28}
[INFO 12-08 08:53:17] optimpv.axBOtorchOptimizer: Trial 26 with parameters: {'l2.mu_n': -7.287982848071491, 'l2.mu_p': -8.062188466438908, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -1.9297183574894083, 'R_series': 1e-06, 'R_shunt': 22.271613562724276, 'l2.G_ehp': 1.4733385080752485e+28}
[INFO 12-08 08:53:18] optimpv.axBOtorchOptimizer: Trial 25 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.10006379207204671), 'Gfrac2_JV_nrmse_linear': np.float64(0.03682124546544486), 'Gfrac3_JV_nrmse_linear': np.float64(0.06442142199860985)} and parameters: {'l2.mu_n': -7.710420844177488, 'l2.mu_p': -8.211578574430053, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -1.9330266625301795, 'R_series': 1.6838810661403534e-06, 'R_shunt': 36.8528904623608, 'l2.G_ehp': 1.471671938862206e+28}
[INFO 12-08 08:53:18] optimpv.axBOtorchOptimizer: Trial 26 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.11208103041049024), 'Gfrac2_JV_nrmse_linear': np.float64(0.07355684844761112), 'Gfrac3_JV_nrmse_linear': np.float64(0.09431370466893307)} and parameters: {'l2.mu_n': -7.287982848071491, 'l2.mu_p': -8.062188466438908, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -1.9297183574894083, 'R_series': 1e-06, 'R_shunt': 22.271613562724276, 'l2.G_ehp': 1.4733385080752485e+28}
[INFO 12-08 08:53:21] optimpv.axBOtorchOptimizer: Trial 27 with parameters: {'l2.mu_n': -7.825491086215935, 'l2.mu_p': -7.878874703784472, 'l2.N_t_bulk': 18.30167922324667, 'l2.preLangevin': -2.137572746362843, 'R_series': 3.3214279839019326e-06, 'R_shunt': 60.033280688372265, 'l2.G_ehp': 1.4610335786284904e+28}
[INFO 12-08 08:53:21] optimpv.axBOtorchOptimizer: Trial 28 with parameters: {'l2.mu_n': -7.887329713500599, 'l2.mu_p': -7.902132287628542, 'l2.N_t_bulk': 22.903089986991944, 'l2.preLangevin': -2.158758750668767, 'R_series': 1.490322860570459e-06, 'R_shunt': 0.01, 'l2.G_ehp': 1.467719009017832e+28}
[INFO 12-08 08:53:22] optimpv.axBOtorchOptimizer: Trial 27 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.05831061838002013), 'Gfrac2_JV_nrmse_linear': np.float64(0.01747463336530149), 'Gfrac3_JV_nrmse_linear': np.float64(0.039844943592866375)} and parameters: {'l2.mu_n': -7.825491086215935, 'l2.mu_p': -7.878874703784472, 'l2.N_t_bulk': 18.30167922324667, 'l2.preLangevin': -2.137572746362843, 'R_series': 3.3214279839019326e-06, 'R_shunt': 60.033280688372265, 'l2.G_ehp': 1.4610335786284904e+28}
[INFO 12-08 08:53:22] optimpv.axBOtorchOptimizer: Trial 28 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.5772209126127975), 'Gfrac2_JV_nrmse_linear': np.float64(0.25208611230202266), 'Gfrac3_JV_nrmse_linear': np.float64(0.43827897220179773)} and parameters: {'l2.mu_n': -7.887329713500599, 'l2.mu_p': -7.902132287628542, 'l2.N_t_bulk': 22.903089986991944, 'l2.preLangevin': -2.158758750668767, 'R_series': 1.490322860570459e-06, 'R_shunt': 0.01, 'l2.G_ehp': 1.467719009017832e+28}
[INFO 12-08 08:53:24] optimpv.axBOtorchOptimizer: Trial 29 with parameters: {'l2.mu_n': -8.153774228038262, 'l2.mu_p': -7.914617720949389, 'l2.N_t_bulk': 17.634152260491543, 'l2.preLangevin': -2.488662881278685, 'R_series': 1.5318119639580336e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.4679985776061715e+28}
[INFO 12-08 08:53:24] optimpv.axBOtorchOptimizer: Trial 30 with parameters: {'l2.mu_n': -8.204357968338737, 'l2.mu_p': -7.649534670562158, 'l2.N_t_bulk': 18.43999887807445, 'l2.preLangevin': -3.0, 'R_series': 3.743501741482896e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.4792622982400112e+28}
[INFO 12-08 08:53:25] optimpv.axBOtorchOptimizer: Trial 29 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03860465892627961), 'Gfrac2_JV_nrmse_linear': np.float64(0.06830949933340583), 'Gfrac3_JV_nrmse_linear': np.float64(0.04273728623858014)} and parameters: {'l2.mu_n': -8.153774228038262, 'l2.mu_p': -7.914617720949389, 'l2.N_t_bulk': 17.634152260491543, 'l2.preLangevin': -2.488662881278685, 'R_series': 1.5318119639580336e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.4679985776061715e+28}
[INFO 12-08 08:53:25] optimpv.axBOtorchOptimizer: Trial 30 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.05938501822024886), 'Gfrac2_JV_nrmse_linear': np.float64(0.08347500935204757), 'Gfrac3_JV_nrmse_linear': np.float64(0.06828014432267426)} and parameters: {'l2.mu_n': -8.204357968338737, 'l2.mu_p': -7.649534670562158, 'l2.N_t_bulk': 18.43999887807445, 'l2.preLangevin': -3.0, 'R_series': 3.743501741482896e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.4792622982400112e+28}
[INFO 12-08 08:53:27] optimpv.axBOtorchOptimizer: Trial 31 with parameters: {'l2.mu_n': -7.921227307866452, 'l2.mu_p': -8.208151187691598, 'l2.N_t_bulk': 17.520988229257444, 'l2.preLangevin': -2.1003011656531037, 'R_series': 3.463366631735251e-06, 'R_shunt': 47.60683594546311, 'l2.G_ehp': 1.4587043454436204e+28}
[INFO 12-08 08:53:27] optimpv.axBOtorchOptimizer: Trial 32 with parameters: {'l2.mu_n': -7.8226448875822, 'l2.mu_p': -8.460208596373354, 'l2.N_t_bulk': 17.793285382532225, 'l2.preLangevin': -1.7523528017432284, 'R_series': 2.4640280475143356e-06, 'R_shunt': 76.48267360270563, 'l2.G_ehp': 1.464679645665893e+28}
[INFO 12-08 08:53:27] optimpv.axBOtorchOptimizer: Trial 31 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.07329328263385217), 'Gfrac2_JV_nrmse_linear': np.float64(0.05780531760201433), 'Gfrac3_JV_nrmse_linear': np.float64(0.04621575461855896)} and parameters: {'l2.mu_n': -7.921227307866452, 'l2.mu_p': -8.208151187691598, 'l2.N_t_bulk': 17.520988229257444, 'l2.preLangevin': -2.1003011656531037, 'R_series': 3.463366631735251e-06, 'R_shunt': 47.60683594546311, 'l2.G_ehp': 1.4587043454436204e+28}
[INFO 12-08 08:53:27] optimpv.axBOtorchOptimizer: Trial 32 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.1465434370809369), 'Gfrac2_JV_nrmse_linear': np.float64(0.07651978433603497), 'Gfrac3_JV_nrmse_linear': np.float64(0.08666048068976322)} and parameters: {'l2.mu_n': -7.8226448875822, 'l2.mu_p': -8.460208596373354, 'l2.N_t_bulk': 17.793285382532225, 'l2.preLangevin': -1.7523528017432284, 'R_series': 2.4640280475143356e-06, 'R_shunt': 76.48267360270563, 'l2.G_ehp': 1.464679645665893e+28}
[INFO 12-08 08:53:30] optimpv.axBOtorchOptimizer: Trial 33 with parameters: {'l2.mu_n': -7.764673520235565, 'l2.mu_p': -7.649923007147024, 'l2.N_t_bulk': 18.241284974489094, 'l2.preLangevin': -2.3878403027421076, 'R_series': 4.661205566843297e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.4668558210624048e+28}
[INFO 12-08 08:53:30] optimpv.axBOtorchOptimizer: Trial 34 with parameters: {'l2.mu_n': -7.8407595473974405, 'l2.mu_p': -7.63880665598369, 'l2.N_t_bulk': 18.7481676449295, 'l2.preLangevin': -2.309025556295372, 'R_series': 8.186925941537073e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:30] optimpv.axBOtorchOptimizer: Trial 33 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03981430663659448), 'Gfrac2_JV_nrmse_linear': np.float64(0.02669492486764449), 'Gfrac3_JV_nrmse_linear': np.float64(0.03454590663484996)} and parameters: {'l2.mu_n': -7.764673520235565, 'l2.mu_p': -7.649923007147024, 'l2.N_t_bulk': 18.241284974489094, 'l2.preLangevin': -2.3878403027421076, 'R_series': 4.661205566843297e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.4668558210624048e+28}
[INFO 12-08 08:53:30] optimpv.axBOtorchOptimizer: Trial 34 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.0463507672719141), 'Gfrac2_JV_nrmse_linear': np.float64(0.020808005934451267), 'Gfrac3_JV_nrmse_linear': np.float64(0.03720599021712476)} and parameters: {'l2.mu_n': -7.8407595473974405, 'l2.mu_p': -7.63880665598369, 'l2.N_t_bulk': 18.7481676449295, 'l2.preLangevin': -2.309025556295372, 'R_series': 8.186925941537073e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:33] optimpv.axBOtorchOptimizer: Trial 35 with parameters: {'l2.mu_n': -7.9158409910830265, 'l2.mu_p': -7.517053522013878, 'l2.N_t_bulk': 18.42509579039086, 'l2.preLangevin': -2.256051633629354, 'R_series': 4.304581791486221e-06, 'R_shunt': 0.01, 'l2.G_ehp': 1.4664859969259222e+28}
[INFO 12-08 08:53:33] optimpv.axBOtorchOptimizer: Trial 36 with parameters: {'l2.mu_n': -7.6696344002515655, 'l2.mu_p': -7.745224458273469, 'l2.N_t_bulk': 18.69969754365006, 'l2.preLangevin': -2.4394857317175918, 'R_series': 5.412669176471327e-06, 'R_shunt': 0.01, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:53:34] optimpv.axBOtorchOptimizer: Trial 35 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.12578968673986593), 'Gfrac2_JV_nrmse_linear': np.float64(0.14188088504873247), 'Gfrac3_JV_nrmse_linear': np.float64(0.13086160859854354)} and parameters: {'l2.mu_n': -7.9158409910830265, 'l2.mu_p': -7.517053522013878, 'l2.N_t_bulk': 18.42509579039086, 'l2.preLangevin': -2.256051633629354, 'R_series': 4.304581791486221e-06, 'R_shunt': 0.01, 'l2.G_ehp': 1.4664859969259222e+28}
[INFO 12-08 08:53:34] optimpv.axBOtorchOptimizer: Trial 36 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.12082098572176089), 'Gfrac2_JV_nrmse_linear': np.float64(0.13997274127340487), 'Gfrac3_JV_nrmse_linear': np.float64(0.13006070087787455)} and parameters: {'l2.mu_n': -7.6696344002515655, 'l2.mu_p': -7.745224458273469, 'l2.N_t_bulk': 18.69969754365006, 'l2.preLangevin': -2.4394857317175918, 'R_series': 5.412669176471327e-06, 'R_shunt': 0.01, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:53:37] optimpv.axBOtorchOptimizer: Trial 37 with parameters: {'l2.mu_n': -8.003910075706123, 'l2.mu_p': -7.609633969692397, 'l2.N_t_bulk': 18.335326947890053, 'l2.preLangevin': -2.248365744252852, 'R_series': 1.0408613928908891e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:53:37] optimpv.axBOtorchOptimizer: Trial 38 with parameters: {'l2.mu_n': -7.6662780766374805, 'l2.mu_p': -7.789198674705741, 'l2.N_t_bulk': 18.790802445809312, 'l2.preLangevin': -2.3315075026285186, 'R_series': 1.5584904771503644e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:53:38] optimpv.axBOtorchOptimizer: Trial 37 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.06951337914348457), 'Gfrac2_JV_nrmse_linear': np.float64(0.013332270565864005), 'Gfrac3_JV_nrmse_linear': np.float64(0.04175917395848925)} and parameters: {'l2.mu_n': -8.003910075706123, 'l2.mu_p': -7.609633969692397, 'l2.N_t_bulk': 18.335326947890053, 'l2.preLangevin': -2.248365744252852, 'R_series': 1.0408613928908891e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:53:38] optimpv.axBOtorchOptimizer: Trial 38 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.05758890574548109), 'Gfrac2_JV_nrmse_linear': np.float64(0.025945557985218755), 'Gfrac3_JV_nrmse_linear': np.float64(0.043642810117420106)} and parameters: {'l2.mu_n': -7.6662780766374805, 'l2.mu_p': -7.789198674705741, 'l2.N_t_bulk': 18.790802445809312, 'l2.preLangevin': -2.3315075026285186, 'R_series': 1.5584904771503644e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:53:41] optimpv.axBOtorchOptimizer: Trial 39 with parameters: {'l2.mu_n': -7.873407560389887, 'l2.mu_p': -7.582814717880716, 'l2.N_t_bulk': 19.25840159363652, 'l2.preLangevin': -2.1907303457721126, 'R_series': 1.0486645886513687e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:53:41] optimpv.axBOtorchOptimizer: Trial 40 with parameters: {'l2.mu_n': -7.706567461890473, 'l2.mu_p': -7.546276722451832, 'l2.N_t_bulk': 18.776646720622498, 'l2.preLangevin': -2.3216571107382, 'R_series': 4.976806549480205e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:53:42] optimpv.axBOtorchOptimizer: Trial 39 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.07344552570047491), 'Gfrac2_JV_nrmse_linear': np.float64(0.026995584454230358), 'Gfrac3_JV_nrmse_linear': np.float64(0.05267895171241971)} and parameters: {'l2.mu_n': -7.873407560389887, 'l2.mu_p': -7.582814717880716, 'l2.N_t_bulk': 19.25840159363652, 'l2.preLangevin': -2.1907303457721126, 'R_series': 1.0486645886513687e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:53:42] optimpv.axBOtorchOptimizer: Trial 40 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.06257873243815315), 'Gfrac2_JV_nrmse_linear': np.float64(0.04880971516997941), 'Gfrac3_JV_nrmse_linear': np.float64(0.05537477687872861)} and parameters: {'l2.mu_n': -7.706567461890473, 'l2.mu_p': -7.546276722451832, 'l2.N_t_bulk': 18.776646720622498, 'l2.preLangevin': -2.3216571107382, 'R_series': 4.976806549480205e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:53:44] optimpv.axBOtorchOptimizer: Trial 41 with parameters: {'l2.mu_n': -7.861729794133351, 'l2.mu_p': -7.8055757181327365, 'l2.N_t_bulk': 18.29432341465715, 'l2.preLangevin': -2.3330263053094087, 'R_series': 1.0945916210021011e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:44] optimpv.axBOtorchOptimizer: Trial 42 with parameters: {'l2.mu_n': -7.815224701677305, 'l2.mu_p': -7.7732326597610895, 'l2.N_t_bulk': 17.700023302932472, 'l2.preLangevin': -2.402317152144663, 'R_series': 8.316686784624098e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:45] optimpv.axBOtorchOptimizer: Trial 41 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.04116011402755826), 'Gfrac2_JV_nrmse_linear': np.float64(0.017089756538563934), 'Gfrac3_JV_nrmse_linear': np.float64(0.030181165828110628)} and parameters: {'l2.mu_n': -7.861729794133351, 'l2.mu_p': -7.8055757181327365, 'l2.N_t_bulk': 18.29432341465715, 'l2.preLangevin': -2.3330263053094087, 'R_series': 1.0945916210021011e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:45] optimpv.axBOtorchOptimizer: Trial 42 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03870975757475582), 'Gfrac2_JV_nrmse_linear': np.float64(0.01302565632449854), 'Gfrac3_JV_nrmse_linear': np.float64(0.029439156157657867)} and parameters: {'l2.mu_n': -7.815224701677305, 'l2.mu_p': -7.7732326597610895, 'l2.N_t_bulk': 17.700023302932472, 'l2.preLangevin': -2.402317152144663, 'R_series': 8.316686784624098e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:47] optimpv.axBOtorchOptimizer: Trial 43 with parameters: {'l2.mu_n': -8.263592239809446, 'l2.mu_p': -7.955657471822845, 'l2.N_t_bulk': 16.174572314617713, 'l2.preLangevin': -2.4582500856388494, 'R_series': 5.360888783792495e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:47] optimpv.axBOtorchOptimizer: Trial 44 with parameters: {'l2.mu_n': -7.71438116588328, 'l2.mu_p': -7.878176021429928, 'l2.N_t_bulk': 18.097150085677413, 'l2.preLangevin': -2.4219094049363776, 'R_series': 1.1769959686820172e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:48] optimpv.axBOtorchOptimizer: Trial 43 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.04916288209032398), 'Gfrac2_JV_nrmse_linear': np.float64(0.08561375733785197), 'Gfrac3_JV_nrmse_linear': np.float64(0.057415475128166735)} and parameters: {'l2.mu_n': -8.263592239809446, 'l2.mu_p': -7.955657471822845, 'l2.N_t_bulk': 16.174572314617713, 'l2.preLangevin': -2.4582500856388494, 'R_series': 5.360888783792495e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:48] optimpv.axBOtorchOptimizer: Trial 44 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.04070706880977632), 'Gfrac2_JV_nrmse_linear': np.float64(0.013816186285817766), 'Gfrac3_JV_nrmse_linear': np.float64(0.03155260675696806)} and parameters: {'l2.mu_n': -7.71438116588328, 'l2.mu_p': -7.878176021429928, 'l2.N_t_bulk': 18.097150085677413, 'l2.preLangevin': -2.4219094049363776, 'R_series': 1.1769959686820172e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:51] optimpv.axBOtorchOptimizer: Trial 45 with parameters: {'l2.mu_n': -7.76772780762858, 'l2.mu_p': -7.749336611140901, 'l2.N_t_bulk': 18.54033898089919, 'l2.preLangevin': -2.286520091025208, 'R_series': 2.0492487828819667e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:51] optimpv.axBOtorchOptimizer: Trial 46 with parameters: {'l2.mu_n': -7.721144722380292, 'l2.mu_p': -7.7495264549621385, 'l2.N_t_bulk': 18.086186031812133, 'l2.preLangevin': -2.3257708240432207, 'R_series': 8.357629499396613e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:51] optimpv.axBOtorchOptimizer: Trial 45 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.048355406874290144), 'Gfrac2_JV_nrmse_linear': np.float64(0.01831727999413183), 'Gfrac3_JV_nrmse_linear': np.float64(0.037604190221461366)} and parameters: {'l2.mu_n': -7.76772780762858, 'l2.mu_p': -7.749336611140901, 'l2.N_t_bulk': 18.54033898089919, 'l2.preLangevin': -2.286520091025208, 'R_series': 2.0492487828819667e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:51] optimpv.axBOtorchOptimizer: Trial 46 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.047811179843332476), 'Gfrac2_JV_nrmse_linear': np.float64(0.025678624870230735), 'Gfrac3_JV_nrmse_linear': np.float64(0.04014554183132666)} and parameters: {'l2.mu_n': -7.721144722380292, 'l2.mu_p': -7.7495264549621385, 'l2.N_t_bulk': 18.086186031812133, 'l2.preLangevin': -2.3257708240432207, 'R_series': 8.357629499396613e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:55] optimpv.axBOtorchOptimizer: Trial 47 with parameters: {'l2.mu_n': -7.901349084420475, 'l2.mu_p': -7.899426218490574, 'l2.N_t_bulk': 18.412726693567265, 'l2.preLangevin': -2.4149866851905064, 'R_series': 1.3288672225205665e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:55] optimpv.axBOtorchOptimizer: Trial 48 with parameters: {'l2.mu_n': -7.807601396180118, 'l2.mu_p': -7.850208764658854, 'l2.N_t_bulk': 18.12006462113504, 'l2.preLangevin': -2.4601567023178643, 'R_series': 7.515427445421944e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:56] optimpv.axBOtorchOptimizer: Trial 47 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03671277665699896), 'Gfrac2_JV_nrmse_linear': np.float64(0.0328837881398083), 'Gfrac3_JV_nrmse_linear': np.float64(0.02902936551775069)} and parameters: {'l2.mu_n': -7.901349084420475, 'l2.mu_p': -7.899426218490574, 'l2.N_t_bulk': 18.412726693567265, 'l2.preLangevin': -2.4149866851905064, 'R_series': 1.3288672225205665e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:56] optimpv.axBOtorchOptimizer: Trial 48 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03605550272336473), 'Gfrac2_JV_nrmse_linear': np.float64(0.015789025651475348), 'Gfrac3_JV_nrmse_linear': np.float64(0.027417204374025946)} and parameters: {'l2.mu_n': -7.807601396180118, 'l2.mu_p': -7.850208764658854, 'l2.N_t_bulk': 18.12006462113504, 'l2.preLangevin': -2.4601567023178643, 'R_series': 7.515427445421944e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:59] optimpv.axBOtorchOptimizer: Trial 49 with parameters: {'l2.mu_n': -7.8382189991388485, 'l2.mu_p': -7.8086815818114825, 'l2.N_t_bulk': 18.219051038568402, 'l2.preLangevin': -2.446320936558581, 'R_series': 1.2292462406545439e-05, 'R_shunt': 36.49123949884541, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:59] optimpv.axBOtorchOptimizer: Trial 50 with parameters: {'l2.mu_n': -7.672115717276684, 'l2.mu_p': -7.9601494036735225, 'l2.N_t_bulk': 17.741890056696832, 'l2.preLangevin': -2.5893184292865863, 'R_series': 6.187420989356409e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:59] optimpv.axBOtorchOptimizer: Trial 49 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03617715466579691), 'Gfrac2_JV_nrmse_linear': np.float64(0.017429776735673228), 'Gfrac3_JV_nrmse_linear': np.float64(0.027446823301267322)} and parameters: {'l2.mu_n': -7.8382189991388485, 'l2.mu_p': -7.8086815818114825, 'l2.N_t_bulk': 18.219051038568402, 'l2.preLangevin': -2.446320936558581, 'R_series': 1.2292462406545439e-05, 'R_shunt': 36.49123949884541, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:53:59] optimpv.axBOtorchOptimizer: Trial 50 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03520756119472197), 'Gfrac2_JV_nrmse_linear': np.float64(0.014617389663712527), 'Gfrac3_JV_nrmse_linear': np.float64(0.02797992246607798)} and parameters: {'l2.mu_n': -7.672115717276684, 'l2.mu_p': -7.9601494036735225, 'l2.N_t_bulk': 17.741890056696832, 'l2.preLangevin': -2.5893184292865863, 'R_series': 6.187420989356409e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:04] optimpv.axBOtorchOptimizer: Trial 51 with parameters: {'l2.mu_n': -7.795508012244527, 'l2.mu_p': -7.902848530786001, 'l2.N_t_bulk': 17.960408176428814, 'l2.preLangevin': -2.5781823256871337, 'R_series': 1.1734626642782174e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:04] optimpv.axBOtorchOptimizer: Trial 52 with parameters: {'l2.mu_n': -7.753117569392593, 'l2.mu_p': -7.849762580736838, 'l2.N_t_bulk': 17.606296775566758, 'l2.preLangevin': -2.559444479519319, 'R_series': 6.855976099761014e-06, 'R_shunt': 37.58203746373281, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:05] optimpv.axBOtorchOptimizer: Trial 51 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.035146357688978606), 'Gfrac2_JV_nrmse_linear': np.float64(0.024772632406729094), 'Gfrac3_JV_nrmse_linear': np.float64(0.028547359258470392)} and parameters: {'l2.mu_n': -7.795508012244527, 'l2.mu_p': -7.902848530786001, 'l2.N_t_bulk': 17.960408176428814, 'l2.preLangevin': -2.5781823256871337, 'R_series': 1.1734626642782174e-05, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:05] optimpv.axBOtorchOptimizer: Trial 52 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03531710563201022), 'Gfrac2_JV_nrmse_linear': np.float64(0.015007055600544505), 'Gfrac3_JV_nrmse_linear': np.float64(0.027593274460627915)} and parameters: {'l2.mu_n': -7.753117569392593, 'l2.mu_p': -7.849762580736838, 'l2.N_t_bulk': 17.606296775566758, 'l2.preLangevin': -2.559444479519319, 'R_series': 6.855976099761014e-06, 'R_shunt': 37.58203746373281, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:09] optimpv.axBOtorchOptimizer: Trial 53 with parameters: {'l2.mu_n': -7.761404579253544, 'l2.mu_p': -7.831081887261664, 'l2.N_t_bulk': 17.543565837620033, 'l2.preLangevin': -2.537068774815996, 'R_series': 4.35375163195513e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:09] optimpv.axBOtorchOptimizer: Trial 54 with parameters: {'l2.mu_n': -7.761796362001203, 'l2.mu_p': -7.902503182472994, 'l2.N_t_bulk': 18.0279117866934, 'l2.preLangevin': -2.522852625101976, 'R_series': 7.67848855951884e-06, 'R_shunt': 43.5738317123565, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:10] optimpv.axBOtorchOptimizer: Trial 53 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.035517909727454514), 'Gfrac2_JV_nrmse_linear': np.float64(0.014016034198418197), 'Gfrac3_JV_nrmse_linear': np.float64(0.027729148690293658)} and parameters: {'l2.mu_n': -7.761404579253544, 'l2.mu_p': -7.831081887261664, 'l2.N_t_bulk': 17.543565837620033, 'l2.preLangevin': -2.537068774815996, 'R_series': 4.35375163195513e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:10] optimpv.axBOtorchOptimizer: Trial 54 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.035223291782269336), 'Gfrac2_JV_nrmse_linear': np.float64(0.01643634236172869), 'Gfrac3_JV_nrmse_linear': np.float64(0.027316985629510668)} and parameters: {'l2.mu_n': -7.761796362001203, 'l2.mu_p': -7.902503182472994, 'l2.N_t_bulk': 18.0279117866934, 'l2.preLangevin': -2.522852625101976, 'R_series': 7.67848855951884e-06, 'R_shunt': 43.5738317123565, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:14] optimpv.axBOtorchOptimizer: Trial 55 with parameters: {'l2.mu_n': -7.698624399588489, 'l2.mu_p': -7.980197203120468, 'l2.N_t_bulk': 17.250522761122316, 'l2.preLangevin': -2.70410946551057, 'R_series': 6.067053620725482e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:14] optimpv.axBOtorchOptimizer: Trial 56 with parameters: {'l2.mu_n': -7.817384897112197, 'l2.mu_p': -7.896971653169686, 'l2.N_t_bulk': 17.81339720012428, 'l2.preLangevin': -2.515052858179248, 'R_series': 7.3619553358574e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:15] optimpv.axBOtorchOptimizer: Trial 55 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.036827608142452256), 'Gfrac2_JV_nrmse_linear': np.float64(0.024023464215934476), 'Gfrac3_JV_nrmse_linear': np.float64(0.031086515017173903)} and parameters: {'l2.mu_n': -7.698624399588489, 'l2.mu_p': -7.980197203120468, 'l2.N_t_bulk': 17.250522761122316, 'l2.preLangevin': -2.70410946551057, 'R_series': 6.067053620725482e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:15] optimpv.axBOtorchOptimizer: Trial 56 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03473637930433913), 'Gfrac2_JV_nrmse_linear': np.float64(0.022378356513045764), 'Gfrac3_JV_nrmse_linear': np.float64(0.02739537890682797)} and parameters: {'l2.mu_n': -7.817384897112197, 'l2.mu_p': -7.896971653169686, 'l2.N_t_bulk': 17.81339720012428, 'l2.preLangevin': -2.515052858179248, 'R_series': 7.3619553358574e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:19] optimpv.axBOtorchOptimizer: Trial 57 with parameters: {'l2.mu_n': -7.715041436482826, 'l2.mu_p': -7.789426169918688, 'l2.N_t_bulk': 17.936521071829326, 'l2.preLangevin': -2.6420580072030564, 'R_series': 9.495351411517601e-06, 'R_shunt': 90.1123234563033, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:19] optimpv.axBOtorchOptimizer: Trial 58 with parameters: {'l2.mu_n': -7.7053556170658375, 'l2.mu_p': -7.917382999558489, 'l2.N_t_bulk': 17.19061887708716, 'l2.preLangevin': -2.5492428947433403, 'R_series': 4.7604972521829136e-06, 'R_shunt': 27.1482532869613, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:20] optimpv.axBOtorchOptimizer: Trial 57 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.036502926685387904), 'Gfrac2_JV_nrmse_linear': np.float64(0.015623888289605774), 'Gfrac3_JV_nrmse_linear': np.float64(0.028787128953511515)} and parameters: {'l2.mu_n': -7.715041436482826, 'l2.mu_p': -7.789426169918688, 'l2.N_t_bulk': 17.936521071829326, 'l2.preLangevin': -2.6420580072030564, 'R_series': 9.495351411517601e-06, 'R_shunt': 90.1123234563033, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:20] optimpv.axBOtorchOptimizer: Trial 58 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03583922808700113), 'Gfrac2_JV_nrmse_linear': np.float64(0.013944570917958254), 'Gfrac3_JV_nrmse_linear': np.float64(0.028179219153546035)} and parameters: {'l2.mu_n': -7.7053556170658375, 'l2.mu_p': -7.917382999558489, 'l2.N_t_bulk': 17.19061887708716, 'l2.preLangevin': -2.5492428947433403, 'R_series': 4.7604972521829136e-06, 'R_shunt': 27.1482532869613, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:25] optimpv.axBOtorchOptimizer: Trial 59 with parameters: {'l2.mu_n': -7.465239510432409, 'l2.mu_p': -7.957944325150774, 'l2.N_t_bulk': 17.397744373286535, 'l2.preLangevin': -2.9385750049538775, 'R_series': 3.709674867097523e-06, 'R_shunt': 57.10353265922783, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:25] optimpv.axBOtorchOptimizer: Trial 60 with parameters: {'l2.mu_n': -7.7727798521041915, 'l2.mu_p': -7.777127444204082, 'l2.N_t_bulk': 17.748295125235018, 'l2.preLangevin': -2.5435751967071596, 'R_series': 5.891138518929999e-06, 'R_shunt': 40.26024066962305, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:26] optimpv.axBOtorchOptimizer: Trial 59 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.04445988750719536), 'Gfrac2_JV_nrmse_linear': np.float64(0.02397510241466001), 'Gfrac3_JV_nrmse_linear': np.float64(0.036610384928388286)} and parameters: {'l2.mu_n': -7.465239510432409, 'l2.mu_p': -7.957944325150774, 'l2.N_t_bulk': 17.397744373286535, 'l2.preLangevin': -2.9385750049538775, 'R_series': 3.709674867097523e-06, 'R_shunt': 57.10353265922783, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:26] optimpv.axBOtorchOptimizer: Trial 60 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03543234067597318), 'Gfrac2_JV_nrmse_linear': np.float64(0.013914183259025815), 'Gfrac3_JV_nrmse_linear': np.float64(0.027770858023869676)} and parameters: {'l2.mu_n': -7.7727798521041915, 'l2.mu_p': -7.777127444204082, 'l2.N_t_bulk': 17.748295125235018, 'l2.preLangevin': -2.5435751967071596, 'R_series': 5.891138518929999e-06, 'R_shunt': 40.26024066962305, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:31] optimpv.axBOtorchOptimizer: Trial 61 with parameters: {'l2.mu_n': -7.651323140247887, 'l2.mu_p': -7.926330215429316, 'l2.N_t_bulk': 17.690539165515148, 'l2.preLangevin': -2.652117601923493, 'R_series': 1.17998963907129e-05, 'R_shunt': 22.24367188477739, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:31] optimpv.axBOtorchOptimizer: Trial 62 with parameters: {'l2.mu_n': -7.7080554607849425, 'l2.mu_p': -7.8733676839067, 'l2.N_t_bulk': 17.43684257363026, 'l2.preLangevin': -2.637529547961794, 'R_series': 3.416174495967879e-06, 'R_shunt': 40.30030280852881, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:32] optimpv.axBOtorchOptimizer: Trial 61 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03561877889275161), 'Gfrac2_JV_nrmse_linear': np.float64(0.01591388057429009), 'Gfrac3_JV_nrmse_linear': np.float64(0.02851265206131859)} and parameters: {'l2.mu_n': -7.651323140247887, 'l2.mu_p': -7.926330215429316, 'l2.N_t_bulk': 17.690539165515148, 'l2.preLangevin': -2.652117601923493, 'R_series': 1.17998963907129e-05, 'R_shunt': 22.24367188477739, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:32] optimpv.axBOtorchOptimizer: Trial 62 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03614159192952243), 'Gfrac2_JV_nrmse_linear': np.float64(0.01619257676011861), 'Gfrac3_JV_nrmse_linear': np.float64(0.028728516626900003)} and parameters: {'l2.mu_n': -7.7080554607849425, 'l2.mu_p': -7.8733676839067, 'l2.N_t_bulk': 17.43684257363026, 'l2.preLangevin': -2.637529547961794, 'R_series': 3.416174495967879e-06, 'R_shunt': 40.30030280852881, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:40] optimpv.axBOtorchOptimizer: Trial 63 with parameters: {'l2.mu_n': -7.671022442002202, 'l2.mu_p': -7.851523404028802, 'l2.N_t_bulk': 17.314286884179644, 'l2.preLangevin': -2.7050163785405554, 'R_series': 8.677817848406455e-06, 'R_shunt': 20.994708299517303, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:40] optimpv.axBOtorchOptimizer: Trial 64 with parameters: {'l2.mu_n': -7.626692200603252, 'l2.mu_p': -7.837750499225878, 'l2.N_t_bulk': 17.5339775366408, 'l2.preLangevin': -2.596509352181609, 'R_series': 7.763608793999835e-06, 'R_shunt': 41.493616740842576, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:40] optimpv.axBOtorchOptimizer: Trial 63 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03800970508277034), 'Gfrac2_JV_nrmse_linear': np.float64(0.017611307680307366), 'Gfrac3_JV_nrmse_linear': np.float64(0.03024186399387572)} and parameters: {'l2.mu_n': -7.671022442002202, 'l2.mu_p': -7.851523404028802, 'l2.N_t_bulk': 17.314286884179644, 'l2.preLangevin': -2.7050163785405554, 'R_series': 8.677817848406455e-06, 'R_shunt': 20.994708299517303, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:40] optimpv.axBOtorchOptimizer: Trial 64 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03809187540473742), 'Gfrac2_JV_nrmse_linear': np.float64(0.02008405485684585), 'Gfrac3_JV_nrmse_linear': np.float64(0.031754336726217636)} and parameters: {'l2.mu_n': -7.626692200603252, 'l2.mu_p': -7.837750499225878, 'l2.N_t_bulk': 17.5339775366408, 'l2.preLangevin': -2.596509352181609, 'R_series': 7.763608793999835e-06, 'R_shunt': 41.493616740842576, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:54:44] optimpv.axBOtorchOptimizer: Trial 65 with parameters: {'l2.mu_n': -7.712275918046231, 'l2.mu_p': -7.892008836921524, 'l2.N_t_bulk': 17.68206041864202, 'l2.preLangevin': -2.5996438684086867, 'R_series': 7.485675495820661e-06, 'R_shunt': 42.22739986657729, 'l2.G_ehp': 1.4865893105944574e+28}
[INFO 12-08 08:54:44] optimpv.axBOtorchOptimizer: Trial 66 with parameters: {'l2.mu_n': -7.717975563449189, 'l2.mu_p': -7.882747194379896, 'l2.N_t_bulk': 18.047521791276413, 'l2.preLangevin': -2.591434464643919, 'R_series': 1.030332249785359e-05, 'R_shunt': 36.11483577473184, 'l2.G_ehp': 1.5070377513786207e+28}
[INFO 12-08 08:54:45] optimpv.axBOtorchOptimizer: Trial 65 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.01810810931893025), 'Gfrac2_JV_nrmse_linear': np.float64(0.011779921364274847), 'Gfrac3_JV_nrmse_linear': np.float64(0.01442536273474785)} and parameters: {'l2.mu_n': -7.712275918046231, 'l2.mu_p': -7.892008836921524, 'l2.N_t_bulk': 17.68206041864202, 'l2.preLangevin': -2.5996438684086867, 'R_series': 7.485675495820661e-06, 'R_shunt': 42.22739986657729, 'l2.G_ehp': 1.4865893105944574e+28}
[INFO 12-08 08:54:45] optimpv.axBOtorchOptimizer: Trial 66 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.022261912263808005), 'Gfrac2_JV_nrmse_linear': np.float64(0.012815542523786326), 'Gfrac3_JV_nrmse_linear': np.float64(0.01777495183011543)} and parameters: {'l2.mu_n': -7.717975563449189, 'l2.mu_p': -7.882747194379896, 'l2.N_t_bulk': 18.047521791276413, 'l2.preLangevin': -2.591434464643919, 'R_series': 1.030332249785359e-05, 'R_shunt': 36.11483577473184, 'l2.G_ehp': 1.5070377513786207e+28}
[INFO 12-08 08:54:48] optimpv.axBOtorchOptimizer: Trial 67 with parameters: {'l2.mu_n': -7.6695284304018685, 'l2.mu_p': -7.929793812071442, 'l2.N_t_bulk': 17.652095096584855, 'l2.preLangevin': -2.6709419440700835, 'R_series': 8.693221615370743e-06, 'R_shunt': 28.089480825854118, 'l2.G_ehp': 1.455778442280221e+28}
[INFO 12-08 08:54:48] optimpv.axBOtorchOptimizer: Trial 68 with parameters: {'l2.mu_n': -7.652667633003288, 'l2.mu_p': -7.931148701499049, 'l2.N_t_bulk': 17.256065595782584, 'l2.preLangevin': -2.71318321966543, 'R_series': 6.553071653569317e-06, 'R_shunt': 26.059701664039824, 'l2.G_ehp': 1.4309566197229028e+28}
[INFO 12-08 08:54:48] optimpv.axBOtorchOptimizer: Trial 67 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.011117010256388664), 'Gfrac2_JV_nrmse_linear': np.float64(0.01280004982139846), 'Gfrac3_JV_nrmse_linear': np.float64(0.008989997104321636)} and parameters: {'l2.mu_n': -7.6695284304018685, 'l2.mu_p': -7.929793812071442, 'l2.N_t_bulk': 17.652095096584855, 'l2.preLangevin': -2.6709419440700835, 'R_series': 8.693221615370743e-06, 'R_shunt': 28.089480825854118, 'l2.G_ehp': 1.455778442280221e+28}
[INFO 12-08 08:54:48] optimpv.axBOtorchOptimizer: Trial 68 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.010196062918676222), 'Gfrac2_JV_nrmse_linear': np.float64(0.013155781405318881), 'Gfrac3_JV_nrmse_linear': np.float64(0.007422619381164346)} and parameters: {'l2.mu_n': -7.652667633003288, 'l2.mu_p': -7.931148701499049, 'l2.N_t_bulk': 17.256065595782584, 'l2.preLangevin': -2.71318321966543, 'R_series': 6.553071653569317e-06, 'R_shunt': 26.059701664039824, 'l2.G_ehp': 1.4309566197229028e+28}
[INFO 12-08 08:54:52] optimpv.axBOtorchOptimizer: Trial 69 with parameters: {'l2.mu_n': -7.616516180114099, 'l2.mu_p': -7.997755562417888, 'l2.N_t_bulk': 17.548201990801104, 'l2.preLangevin': -2.767421560951986, 'R_series': 8.295097670272863e-06, 'R_shunt': 23.158673945025864, 'l2.G_ehp': 1.4129223385341147e+28}
[INFO 12-08 08:54:52] optimpv.axBOtorchOptimizer: Trial 70 with parameters: {'l2.mu_n': -7.706362885023103, 'l2.mu_p': -7.893613585895512, 'l2.N_t_bulk': 17.469592077819367, 'l2.preLangevin': -2.6822845921003986, 'R_series': 9.353010663319162e-06, 'R_shunt': 20.839167831057843, 'l2.G_ehp': 1.418654482733787e+28}
[INFO 12-08 08:54:53] optimpv.axBOtorchOptimizer: Trial 69 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.013430796719398614), 'Gfrac2_JV_nrmse_linear': np.float64(0.016616257826195895), 'Gfrac3_JV_nrmse_linear': np.float64(0.008207693962737959)} and parameters: {'l2.mu_n': -7.616516180114099, 'l2.mu_p': -7.997755562417888, 'l2.N_t_bulk': 17.548201990801104, 'l2.preLangevin': -2.767421560951986, 'R_series': 8.295097670272863e-06, 'R_shunt': 23.158673945025864, 'l2.G_ehp': 1.4129223385341147e+28}
[INFO 12-08 08:54:53] optimpv.axBOtorchOptimizer: Trial 70 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.012264458170809477), 'Gfrac2_JV_nrmse_linear': np.float64(0.014521020221406934), 'Gfrac3_JV_nrmse_linear': np.float64(0.006403164033481467)} and parameters: {'l2.mu_n': -7.706362885023103, 'l2.mu_p': -7.893613585895512, 'l2.N_t_bulk': 17.469592077819367, 'l2.preLangevin': -2.6822845921003986, 'R_series': 9.353010663319162e-06, 'R_shunt': 20.839167831057843, 'l2.G_ehp': 1.418654482733787e+28}
[INFO 12-08 08:54:57] optimpv.axBOtorchOptimizer: Trial 71 with parameters: {'l2.mu_n': -7.660279938311181, 'l2.mu_p': -7.915892955676968, 'l2.N_t_bulk': 17.416189857157846, 'l2.preLangevin': -2.630754706942275, 'R_series': 7.426862967988729e-06, 'R_shunt': 17.802444216015786, 'l2.G_ehp': 1.431167017526282e+28}
[INFO 12-08 08:54:57] optimpv.axBOtorchOptimizer: Trial 72 with parameters: {'l2.mu_n': -7.694505928977648, 'l2.mu_p': -7.885655795072742, 'l2.N_t_bulk': 17.442252034237683, 'l2.preLangevin': -2.703930562824458, 'R_series': 6.9997612840687435e-06, 'R_shunt': 38.44066905084771, 'l2.G_ehp': 1.4277489351792179e+28}
[INFO 12-08 08:54:58] optimpv.axBOtorchOptimizer: Trial 71 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.017427722717347733), 'Gfrac2_JV_nrmse_linear': np.float64(0.008902867988740413), 'Gfrac3_JV_nrmse_linear': np.float64(0.011141481665753416)} and parameters: {'l2.mu_n': -7.660279938311181, 'l2.mu_p': -7.915892955676968, 'l2.N_t_bulk': 17.416189857157846, 'l2.preLangevin': -2.630754706942275, 'R_series': 7.426862967988729e-06, 'R_shunt': 17.802444216015786, 'l2.G_ehp': 1.431167017526282e+28}
[INFO 12-08 08:54:58] optimpv.axBOtorchOptimizer: Trial 72 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.009395405839363529), 'Gfrac2_JV_nrmse_linear': np.float64(0.01429808365440892), 'Gfrac3_JV_nrmse_linear': np.float64(0.006997235000950733)} and parameters: {'l2.mu_n': -7.694505928977648, 'l2.mu_p': -7.885655795072742, 'l2.N_t_bulk': 17.442252034237683, 'l2.preLangevin': -2.703930562824458, 'R_series': 6.9997612840687435e-06, 'R_shunt': 38.44066905084771, 'l2.G_ehp': 1.4277489351792179e+28}
[INFO 12-08 08:55:01] optimpv.axBOtorchOptimizer: Trial 73 with parameters: {'l2.mu_n': -7.713410777193577, 'l2.mu_p': -7.906702186574119, 'l2.N_t_bulk': 16.928288160086076, 'l2.preLangevin': -2.825014579019521, 'R_series': 5.44517597660176e-06, 'R_shunt': 28.911569921001817, 'l2.G_ehp': 1.3931787505485532e+28}
[INFO 12-08 08:55:01] optimpv.axBOtorchOptimizer: Trial 74 with parameters: {'l2.mu_n': -7.676414069450621, 'l2.mu_p': -7.896787842839229, 'l2.N_t_bulk': 17.437714787642747, 'l2.preLangevin': -2.7601792053943646, 'R_series': 9.770039372609927e-06, 'R_shunt': 44.276530475019044, 'l2.G_ehp': 1.4180924522372685e+28}
[INFO 12-08 08:55:02] optimpv.axBOtorchOptimizer: Trial 73 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.021040919888008092), 'Gfrac2_JV_nrmse_linear': np.float64(0.027288196563238235), 'Gfrac3_JV_nrmse_linear': np.float64(0.01975230107477191)} and parameters: {'l2.mu_n': -7.713410777193577, 'l2.mu_p': -7.906702186574119, 'l2.N_t_bulk': 16.928288160086076, 'l2.preLangevin': -2.825014579019521, 'R_series': 5.44517597660176e-06, 'R_shunt': 28.911569921001817, 'l2.G_ehp': 1.3931787505485532e+28}
[INFO 12-08 08:55:02] optimpv.axBOtorchOptimizer: Trial 74 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.010565270541591157), 'Gfrac2_JV_nrmse_linear': np.float64(0.01813507598088623), 'Gfrac3_JV_nrmse_linear': np.float64(0.009958257710454164)} and parameters: {'l2.mu_n': -7.676414069450621, 'l2.mu_p': -7.896787842839229, 'l2.N_t_bulk': 17.437714787642747, 'l2.preLangevin': -2.7601792053943646, 'R_series': 9.770039372609927e-06, 'R_shunt': 44.276530475019044, 'l2.G_ehp': 1.4180924522372685e+28}
[INFO 12-08 08:55:05] optimpv.axBOtorchOptimizer: Trial 75 with parameters: {'l2.mu_n': -7.658392477084057, 'l2.mu_p': -7.91492995275761, 'l2.N_t_bulk': 17.529915969264728, 'l2.preLangevin': -2.650170131509391, 'R_series': 8.80551021955047e-06, 'R_shunt': 35.28707127579035, 'l2.G_ehp': 1.4339595948511954e+28}
[INFO 12-08 08:55:05] optimpv.axBOtorchOptimizer: Trial 76 with parameters: {'l2.mu_n': -7.66941574250613, 'l2.mu_p': -7.883372226378269, 'l2.N_t_bulk': 17.633294999408243, 'l2.preLangevin': -2.7222897662302046, 'R_series': 8.152225648735954e-06, 'R_shunt': 24.686574682785523, 'l2.G_ehp': 1.4370410924751881e+28}
[INFO 12-08 08:55:06] optimpv.axBOtorchOptimizer: Trial 75 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.014948260409037304), 'Gfrac2_JV_nrmse_linear': np.float64(0.00962177350253533), 'Gfrac3_JV_nrmse_linear': np.float64(0.009385527733029524)} and parameters: {'l2.mu_n': -7.658392477084057, 'l2.mu_p': -7.91492995275761, 'l2.N_t_bulk': 17.529915969264728, 'l2.preLangevin': -2.650170131509391, 'R_series': 8.80551021955047e-06, 'R_shunt': 35.28707127579035, 'l2.G_ehp': 1.4339595948511954e+28}
[INFO 12-08 08:55:06] optimpv.axBOtorchOptimizer: Trial 76 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.008183311405841932), 'Gfrac2_JV_nrmse_linear': np.float64(0.013863874401671531), 'Gfrac3_JV_nrmse_linear': np.float64(0.008113722593991603)} and parameters: {'l2.mu_n': -7.66941574250613, 'l2.mu_p': -7.883372226378269, 'l2.N_t_bulk': 17.633294999408243, 'l2.preLangevin': -2.7222897662302046, 'R_series': 8.152225648735954e-06, 'R_shunt': 24.686574682785523, 'l2.G_ehp': 1.4370410924751881e+28}
[INFO 12-08 08:55:09] optimpv.axBOtorchOptimizer: Trial 77 with parameters: {'l2.mu_n': -7.706201389014374, 'l2.mu_p': -7.934640660299905, 'l2.N_t_bulk': 17.586016873054223, 'l2.preLangevin': -2.720280279789628, 'R_series': 7.89401967562189e-06, 'R_shunt': 23.954215057857937, 'l2.G_ehp': 1.4425842545996229e+28}
[INFO 12-08 08:55:09] optimpv.axBOtorchOptimizer: Trial 78 with parameters: {'l2.mu_n': -7.686310256271225, 'l2.mu_p': -7.881169715444326, 'l2.N_t_bulk': 17.656671327001398, 'l2.preLangevin': -2.708040285078263, 'R_series': 1.2124784768032963e-05, 'R_shunt': 21.567876546320218, 'l2.G_ehp': 1.4355597675721846e+28}
[INFO 12-08 08:55:10] optimpv.axBOtorchOptimizer: Trial 77 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.007470718986512004), 'Gfrac2_JV_nrmse_linear': np.float64(0.020232314573236286), 'Gfrac3_JV_nrmse_linear': np.float64(0.010283149798486312)} and parameters: {'l2.mu_n': -7.706201389014374, 'l2.mu_p': -7.934640660299905, 'l2.N_t_bulk': 17.586016873054223, 'l2.preLangevin': -2.720280279789628, 'R_series': 7.89401967562189e-06, 'R_shunt': 23.954215057857937, 'l2.G_ehp': 1.4425842545996229e+28}
[INFO 12-08 08:55:10] optimpv.axBOtorchOptimizer: Trial 78 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.007792454005212207), 'Gfrac2_JV_nrmse_linear': np.float64(0.015015892875904482), 'Gfrac3_JV_nrmse_linear': np.float64(0.0070835734863170345)} and parameters: {'l2.mu_n': -7.686310256271225, 'l2.mu_p': -7.881169715444326, 'l2.N_t_bulk': 17.656671327001398, 'l2.preLangevin': -2.708040285078263, 'R_series': 1.2124784768032963e-05, 'R_shunt': 21.567876546320218, 'l2.G_ehp': 1.4355597675721846e+28}
[INFO 12-08 08:55:12] optimpv.axBOtorchOptimizer: Trial 79 with parameters: {'l2.mu_n': -7.627168688059779, 'l2.mu_p': -7.852880928600664, 'l2.N_t_bulk': 17.51643461145818, 'l2.preLangevin': -2.72225664376659, 'R_series': 1.0045442431018735e-05, 'R_shunt': 23.426489896606288, 'l2.G_ehp': 1.439477201161764e+28}
[INFO 12-08 08:55:12] optimpv.axBOtorchOptimizer: Trial 80 with parameters: {'l2.mu_n': -7.61701213334141, 'l2.mu_p': -7.9576168021472675, 'l2.N_t_bulk': 17.615372238260633, 'l2.preLangevin': -2.7261856130006863, 'R_series': 1.2280700197840616e-05, 'R_shunt': 23.584374061924542, 'l2.G_ehp': 1.4402250132234396e+28}
[INFO 12-08 08:55:13] optimpv.axBOtorchOptimizer: Trial 79 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.010555984967124836), 'Gfrac2_JV_nrmse_linear': np.float64(0.01236887569205854), 'Gfrac3_JV_nrmse_linear': np.float64(0.010550748180904217)} and parameters: {'l2.mu_n': -7.627168688059779, 'l2.mu_p': -7.852880928600664, 'l2.N_t_bulk': 17.51643461145818, 'l2.preLangevin': -2.72225664376659, 'R_series': 1.0045442431018735e-05, 'R_shunt': 23.426489896606288, 'l2.G_ehp': 1.439477201161764e+28}
[INFO 12-08 08:55:13] optimpv.axBOtorchOptimizer: Trial 80 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.009505851139462958), 'Gfrac2_JV_nrmse_linear': np.float64(0.013446406706404615), 'Gfrac3_JV_nrmse_linear': np.float64(0.007279665366651261)} and parameters: {'l2.mu_n': -7.61701213334141, 'l2.mu_p': -7.9576168021472675, 'l2.N_t_bulk': 17.615372238260633, 'l2.preLangevin': -2.7261856130006863, 'R_series': 1.2280700197840616e-05, 'R_shunt': 23.584374061924542, 'l2.G_ehp': 1.4402250132234396e+28}
[INFO 12-08 08:55:17] optimpv.axBOtorchOptimizer: Trial 81 with parameters: {'l2.mu_n': -7.67974137394659, 'l2.mu_p': -7.93114914915645, 'l2.N_t_bulk': 17.51686583150049, 'l2.preLangevin': -2.668421524341741, 'R_series': 1.2440332376998001e-05, 'R_shunt': 21.824262461839627, 'l2.G_ehp': 1.4394915691551798e+28}
[INFO 12-08 08:55:17] optimpv.axBOtorchOptimizer: Trial 82 with parameters: {'l2.mu_n': -7.634992713380891, 'l2.mu_p': -7.91966436592176, 'l2.N_t_bulk': 17.788428048292165, 'l2.preLangevin': -2.736756485651252, 'R_series': 1.1266928732977554e-05, 'R_shunt': 19.941206742565164, 'l2.G_ehp': 1.4380153761765193e+28}
[INFO 12-08 08:55:18] optimpv.axBOtorchOptimizer: Trial 81 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.010815464558395634), 'Gfrac2_JV_nrmse_linear': np.float64(0.014058014502524778), 'Gfrac3_JV_nrmse_linear': np.float64(0.006238577513562874)} and parameters: {'l2.mu_n': -7.67974137394659, 'l2.mu_p': -7.93114914915645, 'l2.N_t_bulk': 17.51686583150049, 'l2.preLangevin': -2.668421524341741, 'R_series': 1.2440332376998001e-05, 'R_shunt': 21.824262461839627, 'l2.G_ehp': 1.4394915691551798e+28}
[INFO 12-08 08:55:18] optimpv.axBOtorchOptimizer: Trial 82 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.008133256718011243), 'Gfrac2_JV_nrmse_linear': np.float64(0.014002746963234764), 'Gfrac3_JV_nrmse_linear': np.float64(0.007820709005720732)} and parameters: {'l2.mu_n': -7.634992713380891, 'l2.mu_p': -7.91966436592176, 'l2.N_t_bulk': 17.788428048292165, 'l2.preLangevin': -2.736756485651252, 'R_series': 1.1266928732977554e-05, 'R_shunt': 19.941206742565164, 'l2.G_ehp': 1.4380153761765193e+28}
[INFO 12-08 08:55:22] optimpv.axBOtorchOptimizer: Trial 83 with parameters: {'l2.mu_n': -7.699812275525335, 'l2.mu_p': -7.8727232257533215, 'l2.N_t_bulk': 17.820403175475448, 'l2.preLangevin': -2.6592574901997468, 'R_series': 2.0400465563491985e-05, 'R_shunt': 13.547055916072697, 'l2.G_ehp': 1.427711813011201e+28}
[INFO 12-08 08:55:22] optimpv.axBOtorchOptimizer: Trial 84 with parameters: {'l2.mu_n': -7.665545426260547, 'l2.mu_p': -7.936475167789792, 'l2.N_t_bulk': 17.56122961274248, 'l2.preLangevin': -2.757129736639709, 'R_series': 1.078652154807765e-05, 'R_shunt': 16.747561740718286, 'l2.G_ehp': 1.4455815344782294e+28}
[INFO 12-08 08:55:22] optimpv.axBOtorchOptimizer: Trial 83 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.012401821312726581), 'Gfrac2_JV_nrmse_linear': np.float64(0.01457524352773122), 'Gfrac3_JV_nrmse_linear': np.float64(0.0060165701280365325)} and parameters: {'l2.mu_n': -7.699812275525335, 'l2.mu_p': -7.8727232257533215, 'l2.N_t_bulk': 17.820403175475448, 'l2.preLangevin': -2.6592574901997468, 'R_series': 2.0400465563491985e-05, 'R_shunt': 13.547055916072697, 'l2.G_ehp': 1.427711813011201e+28}
[INFO 12-08 08:55:22] optimpv.axBOtorchOptimizer: Trial 84 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.00791679914952808), 'Gfrac2_JV_nrmse_linear': np.float64(0.019709091141753673), 'Gfrac3_JV_nrmse_linear': np.float64(0.01148643944561319)} and parameters: {'l2.mu_n': -7.665545426260547, 'l2.mu_p': -7.936475167789792, 'l2.N_t_bulk': 17.56122961274248, 'l2.preLangevin': -2.757129736639709, 'R_series': 1.078652154807765e-05, 'R_shunt': 16.747561740718286, 'l2.G_ehp': 1.4455815344782294e+28}
[INFO 12-08 08:55:26] optimpv.axBOtorchOptimizer: Trial 85 with parameters: {'l2.mu_n': -7.658465425144588, 'l2.mu_p': -7.906377977288418, 'l2.N_t_bulk': 17.69262963618126, 'l2.preLangevin': -2.6350417514590254, 'R_series': 1.361543651625197e-05, 'R_shunt': 21.358756922598026, 'l2.G_ehp': 1.430497857971197e+28}
[INFO 12-08 08:55:26] optimpv.axBOtorchOptimizer: Trial 86 with parameters: {'l2.mu_n': -7.708398407449767, 'l2.mu_p': -7.897329017348248, 'l2.N_t_bulk': 17.70000569774494, 'l2.preLangevin': -2.6446406276956083, 'R_series': 1.3161094685279308e-05, 'R_shunt': 25.426161082019615, 'l2.G_ehp': 1.4356708113552548e+28}
[INFO 12-08 08:55:26] optimpv.axBOtorchOptimizer: Trial 85 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.0166253356023881), 'Gfrac2_JV_nrmse_linear': np.float64(0.008718045056820816), 'Gfrac3_JV_nrmse_linear': np.float64(0.009355827199075812)} and parameters: {'l2.mu_n': -7.658465425144588, 'l2.mu_p': -7.906377977288418, 'l2.N_t_bulk': 17.69262963618126, 'l2.preLangevin': -2.6350417514590254, 'R_series': 1.361543651625197e-05, 'R_shunt': 21.358756922598026, 'l2.G_ehp': 1.430497857971197e+28}
[INFO 12-08 08:55:26] optimpv.axBOtorchOptimizer: Trial 86 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.012002350574170652), 'Gfrac2_JV_nrmse_linear': np.float64(0.013779128122495928), 'Gfrac3_JV_nrmse_linear': np.float64(0.006095549525715331)} and parameters: {'l2.mu_n': -7.708398407449767, 'l2.mu_p': -7.897329017348248, 'l2.N_t_bulk': 17.70000569774494, 'l2.preLangevin': -2.6446406276956083, 'R_series': 1.3161094685279308e-05, 'R_shunt': 25.426161082019615, 'l2.G_ehp': 1.4356708113552548e+28}
[INFO 12-08 08:55:29] optimpv.axBOtorchOptimizer: Trial 87 with parameters: {'l2.mu_n': -7.728637455004433, 'l2.mu_p': -7.837264564295402, 'l2.N_t_bulk': 17.565592974799202, 'l2.preLangevin': -2.6474609094427866, 'R_series': 1.2932406228198657e-05, 'R_shunt': 23.184265530282193, 'l2.G_ehp': 1.4395119858012608e+28}
[INFO 12-08 08:55:29] optimpv.axBOtorchOptimizer: Trial 88 with parameters: {'l2.mu_n': -7.641544081703145, 'l2.mu_p': -7.906367474179232, 'l2.N_t_bulk': 17.613696894194884, 'l2.preLangevin': -2.696965560359507, 'R_series': 1.4807686976146275e-05, 'R_shunt': 30.503890174763733, 'l2.G_ehp': 1.4373871841225316e+28}
[INFO 12-08 08:55:30] optimpv.axBOtorchOptimizer: Trial 87 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.01040301386764579), 'Gfrac2_JV_nrmse_linear': np.float64(0.012878740487269443), 'Gfrac3_JV_nrmse_linear': np.float64(0.006342166624359189)} and parameters: {'l2.mu_n': -7.728637455004433, 'l2.mu_p': -7.837264564295402, 'l2.N_t_bulk': 17.565592974799202, 'l2.preLangevin': -2.6474609094427866, 'R_series': 1.2932406228198657e-05, 'R_shunt': 23.184265530282193, 'l2.G_ehp': 1.4395119858012608e+28}
[INFO 12-08 08:55:30] optimpv.axBOtorchOptimizer: Trial 88 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.010201888290125844), 'Gfrac2_JV_nrmse_linear': np.float64(0.011853509554933922), 'Gfrac3_JV_nrmse_linear': np.float64(0.006845550181928838)} and parameters: {'l2.mu_n': -7.641544081703145, 'l2.mu_p': -7.906367474179232, 'l2.N_t_bulk': 17.613696894194884, 'l2.preLangevin': -2.696965560359507, 'R_series': 1.4807686976146275e-05, 'R_shunt': 30.503890174763733, 'l2.G_ehp': 1.4373871841225316e+28}
[INFO 12-08 08:55:34] optimpv.axBOtorchOptimizer: Trial 89 with parameters: {'l2.mu_n': -7.697808157017457, 'l2.mu_p': -7.83541154605022, 'l2.N_t_bulk': 17.723040936771717, 'l2.preLangevin': -2.6984153156797452, 'R_series': 1.687843183336081e-05, 'R_shunt': 26.91473137203423, 'l2.G_ehp': 1.435449769080215e+28}
[INFO 12-08 08:55:34] optimpv.axBOtorchOptimizer: Trial 90 with parameters: {'l2.mu_n': -7.702674417110662, 'l2.mu_p': -7.862851602312098, 'l2.N_t_bulk': 17.498410180010914, 'l2.preLangevin': -2.635215318238272, 'R_series': 1.7379078139881004e-05, 'R_shunt': 22.451486989480973, 'l2.G_ehp': 1.4338251546779689e+28}
[INFO 12-08 08:55:35] optimpv.axBOtorchOptimizer: Trial 89 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.007822309429376199), 'Gfrac2_JV_nrmse_linear': np.float64(0.014379100849274958), 'Gfrac3_JV_nrmse_linear': np.float64(0.0066225135351403305)} and parameters: {'l2.mu_n': -7.697808157017457, 'l2.mu_p': -7.83541154605022, 'l2.N_t_bulk': 17.723040936771717, 'l2.preLangevin': -2.6984153156797452, 'R_series': 1.687843183336081e-05, 'R_shunt': 26.91473137203423, 'l2.G_ehp': 1.435449769080215e+28}
[INFO 12-08 08:55:35] optimpv.axBOtorchOptimizer: Trial 90 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.013271170599880797), 'Gfrac2_JV_nrmse_linear': np.float64(0.011736241396707982), 'Gfrac3_JV_nrmse_linear': np.float64(0.006481862341748008)} and parameters: {'l2.mu_n': -7.702674417110662, 'l2.mu_p': -7.862851602312098, 'l2.N_t_bulk': 17.498410180010914, 'l2.preLangevin': -2.635215318238272, 'R_series': 1.7379078139881004e-05, 'R_shunt': 22.451486989480973, 'l2.G_ehp': 1.4338251546779689e+28}
[INFO 12-08 08:55:39] optimpv.axBOtorchOptimizer: Trial 91 with parameters: {'l2.mu_n': -7.82916896809179, 'l2.mu_p': -7.731402151105109, 'l2.N_t_bulk': 17.85486271024732, 'l2.preLangevin': -2.551017576216609, 'R_series': 2.9445282394655257e-05, 'R_shunt': 13.816935586494747, 'l2.G_ehp': 1.435606578397061e+28}
[INFO 12-08 08:55:39] optimpv.axBOtorchOptimizer: Trial 92 with parameters: {'l2.mu_n': -7.71679971755621, 'l2.mu_p': -7.780992338844819, 'l2.N_t_bulk': 17.794800861749028, 'l2.preLangevin': -2.650874119704155, 'R_series': 3.205761940802594e-05, 'R_shunt': 17.245187531338658, 'l2.G_ehp': 1.4213534193849528e+28}
[INFO 12-08 08:55:40] optimpv.axBOtorchOptimizer: Trial 91 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.02180914378481993), 'Gfrac2_JV_nrmse_linear': np.float64(0.017912075349698447), 'Gfrac3_JV_nrmse_linear': np.float64(0.011837654480048547)} and parameters: {'l2.mu_n': -7.82916896809179, 'l2.mu_p': -7.731402151105109, 'l2.N_t_bulk': 17.85486271024732, 'l2.preLangevin': -2.551017576216609, 'R_series': 2.9445282394655257e-05, 'R_shunt': 13.816935586494747, 'l2.G_ehp': 1.435606578397061e+28}
[INFO 12-08 08:55:40] optimpv.axBOtorchOptimizer: Trial 92 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.014812406779297656), 'Gfrac2_JV_nrmse_linear': np.float64(0.01428518115725183), 'Gfrac3_JV_nrmse_linear': np.float64(0.007240320086225407)} and parameters: {'l2.mu_n': -7.71679971755621, 'l2.mu_p': -7.780992338844819, 'l2.N_t_bulk': 17.794800861749028, 'l2.preLangevin': -2.650874119704155, 'R_series': 3.205761940802594e-05, 'R_shunt': 17.245187531338658, 'l2.G_ehp': 1.4213534193849528e+28}
[INFO 12-08 08:55:44] optimpv.axBOtorchOptimizer: Trial 93 with parameters: {'l2.mu_n': -7.637420600747156, 'l2.mu_p': -7.88697822621115, 'l2.N_t_bulk': 17.44152638920532, 'l2.preLangevin': -2.6604146522404992, 'R_series': 1.4347618677504728e-05, 'R_shunt': 24.17408625059412, 'l2.G_ehp': 1.4357654227982847e+28}
[INFO 12-08 08:55:44] optimpv.axBOtorchOptimizer: Trial 94 with parameters: {'l2.mu_n': -7.629004410769637, 'l2.mu_p': -7.941295502996628, 'l2.N_t_bulk': 17.828316731644172, 'l2.preLangevin': -2.7069279444991063, 'R_series': 2.4992707159725993e-05, 'R_shunt': 24.816735759060688, 'l2.G_ehp': 1.429841804365266e+28}
[INFO 12-08 08:55:44] optimpv.axBOtorchOptimizer: Trial 93 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.0144636204946909), 'Gfrac2_JV_nrmse_linear': np.float64(0.009323948412169332), 'Gfrac3_JV_nrmse_linear': np.float64(0.009769896523094323)} and parameters: {'l2.mu_n': -7.637420600747156, 'l2.mu_p': -7.88697822621115, 'l2.N_t_bulk': 17.44152638920532, 'l2.preLangevin': -2.6604146522404992, 'R_series': 1.4347618677504728e-05, 'R_shunt': 24.17408625059412, 'l2.G_ehp': 1.4357654227982847e+28}
[INFO 12-08 08:55:44] optimpv.axBOtorchOptimizer: Trial 94 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.011445801751199918), 'Gfrac2_JV_nrmse_linear': np.float64(0.015727104651068458), 'Gfrac3_JV_nrmse_linear': np.float64(0.005872217016678691)} and parameters: {'l2.mu_n': -7.629004410769637, 'l2.mu_p': -7.941295502996628, 'l2.N_t_bulk': 17.828316731644172, 'l2.preLangevin': -2.7069279444991063, 'R_series': 2.4992707159725993e-05, 'R_shunt': 24.816735759060688, 'l2.G_ehp': 1.429841804365266e+28}
[INFO 12-08 08:55:47] optimpv.axBOtorchOptimizer: Trial 95 with parameters: {'l2.mu_n': -7.675004485387082, 'l2.mu_p': -7.875734205422226, 'l2.N_t_bulk': 17.69608588255448, 'l2.preLangevin': -2.6684525859060906, 'R_series': 1.640706420161189e-05, 'R_shunt': 29.040795255266517, 'l2.G_ehp': 1.428507896369058e+28}
[INFO 12-08 08:55:47] optimpv.axBOtorchOptimizer: Trial 96 with parameters: {'l2.mu_n': -7.6345340369443395, 'l2.mu_p': -7.8652446572365236, 'l2.N_t_bulk': 17.657192001174874, 'l2.preLangevin': -2.70050187931568, 'R_series': 1.7304365980568224e-05, 'R_shunt': 20.866832250351425, 'l2.G_ehp': 1.4284982810704694e+28}
[INFO 12-08 08:55:48] optimpv.axBOtorchOptimizer: Trial 95 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.012188933055872883), 'Gfrac2_JV_nrmse_linear': np.float64(0.011431883220333368), 'Gfrac3_JV_nrmse_linear': np.float64(0.006069968217725247)} and parameters: {'l2.mu_n': -7.675004485387082, 'l2.mu_p': -7.875734205422226, 'l2.N_t_bulk': 17.69608588255448, 'l2.preLangevin': -2.6684525859060906, 'R_series': 1.640706420161189e-05, 'R_shunt': 29.040795255266517, 'l2.G_ehp': 1.428507896369058e+28}
[INFO 12-08 08:55:48] optimpv.axBOtorchOptimizer: Trial 96 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.011256604163003187), 'Gfrac2_JV_nrmse_linear': np.float64(0.010370034003157993), 'Gfrac3_JV_nrmse_linear': np.float64(0.007164702498383267)} and parameters: {'l2.mu_n': -7.6345340369443395, 'l2.mu_p': -7.8652446572365236, 'l2.N_t_bulk': 17.657192001174874, 'l2.preLangevin': -2.70050187931568, 'R_series': 1.7304365980568224e-05, 'R_shunt': 20.866832250351425, 'l2.G_ehp': 1.4284982810704694e+28}
[INFO 12-08 08:55:51] optimpv.axBOtorchOptimizer: Trial 97 with parameters: {'l2.mu_n': -8.11933002931894, 'l2.mu_p': -7.436353217515936, 'l2.N_t_bulk': 16.59248467125088, 'l2.preLangevin': -2.486261728345232, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5343681598842788e+28}
[INFO 12-08 08:55:51] optimpv.axBOtorchOptimizer: Trial 98 with parameters: {'l2.mu_n': -7.459065544359745, 'l2.mu_p': -8.157533492346982, 'l2.N_t_bulk': 17.4298509986632, 'l2.preLangevin': -2.6587939435720775, 'R_series': 4.93961166125157e-06, 'R_shunt': 35.32088660359826, 'l2.G_ehp': 1.4408702716810933e+28}
[INFO 12-08 08:55:52] optimpv.axBOtorchOptimizer: Trial 97 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.042359601114521486), 'Gfrac2_JV_nrmse_linear': np.float64(0.013735029185280167), 'Gfrac3_JV_nrmse_linear': np.float64(0.023946582134385594)} and parameters: {'l2.mu_n': -8.11933002931894, 'l2.mu_p': -7.436353217515936, 'l2.N_t_bulk': 16.59248467125088, 'l2.preLangevin': -2.486261728345232, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5343681598842788e+28}
[INFO 12-08 08:55:52] optimpv.axBOtorchOptimizer: Trial 98 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.03885135737729269), 'Gfrac2_JV_nrmse_linear': np.float64(0.013588785767183718), 'Gfrac3_JV_nrmse_linear': np.float64(0.024956355867295776)} and parameters: {'l2.mu_n': -7.459065544359745, 'l2.mu_p': -8.157533492346982, 'l2.N_t_bulk': 17.4298509986632, 'l2.preLangevin': -2.6587939435720775, 'R_series': 4.93961166125157e-06, 'R_shunt': 35.32088660359826, 'l2.G_ehp': 1.4408702716810933e+28}
[INFO 12-08 08:55:57] optimpv.axBOtorchOptimizer: Trial 99 with parameters: {'l2.mu_n': -7.905328323098102, 'l2.mu_p': -7.683572707322531, 'l2.N_t_bulk': 16.318775546714377, 'l2.preLangevin': -2.431171128114973, 'R_series': 2.2506091132064914e-06, 'R_shunt': 31.69244710818399, 'l2.G_ehp': 1.4529621104555735e+28}
[INFO 12-08 08:55:57] optimpv.axBOtorchOptimizer: Trial 100 with parameters: {'l2.mu_n': -7.662067632988803, 'l2.mu_p': -7.866541788057443, 'l2.N_t_bulk': 17.67672123492629, 'l2.preLangevin': -2.6655715306664716, 'R_series': 1.3810776887794974e-05, 'R_shunt': 25.297893246581754, 'l2.G_ehp': 1.4166274347901656e+28}
[INFO 12-08 08:55:58] optimpv.axBOtorchOptimizer: Trial 99 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.030508340280619797), 'Gfrac2_JV_nrmse_linear': np.float64(0.0078251230885778), 'Gfrac3_JV_nrmse_linear': np.float64(0.01785484118205591)} and parameters: {'l2.mu_n': -7.905328323098102, 'l2.mu_p': -7.683572707322531, 'l2.N_t_bulk': 16.318775546714377, 'l2.preLangevin': -2.431171128114973, 'R_series': 2.2506091132064914e-06, 'R_shunt': 31.69244710818399, 'l2.G_ehp': 1.4529621104555735e+28}
[INFO 12-08 08:55:58] optimpv.axBOtorchOptimizer: Trial 100 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.015663631402458116), 'Gfrac2_JV_nrmse_linear': np.float64(0.009138343653310062), 'Gfrac3_JV_nrmse_linear': np.float64(0.008546581256483372)} and parameters: {'l2.mu_n': -7.662067632988803, 'l2.mu_p': -7.866541788057443, 'l2.N_t_bulk': 17.67672123492629, 'l2.preLangevin': -2.6655715306664716, 'R_series': 1.3810776887794974e-05, 'R_shunt': 25.297893246581754, 'l2.G_ehp': 1.4166274347901656e+28}
[INFO 12-08 08:56:01] optimpv.axBOtorchOptimizer: Trial 101 with parameters: {'l2.mu_n': -7.940446879488659, 'l2.mu_p': -7.55434337311577, 'l2.N_t_bulk': 17.01593315429935, 'l2.preLangevin': -2.6040024216082482, 'R_series': 4.47312492800543e-06, 'R_shunt': 56.87328716459102, 'l2.G_ehp': 1.4635790727300735e+28}
[INFO 12-08 08:56:01] optimpv.axBOtorchOptimizer: Trial 102 with parameters: {'l2.mu_n': -7.985803053987189, 'l2.mu_p': -7.661026031755201, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.2675385695082126, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:56:01] optimpv.axBOtorchOptimizer: Trial 101 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.01935986778081043), 'Gfrac2_JV_nrmse_linear': np.float64(0.011855920216500617), 'Gfrac3_JV_nrmse_linear': np.float64(0.010317436101586665)} and parameters: {'l2.mu_n': -7.940446879488659, 'l2.mu_p': -7.55434337311577, 'l2.N_t_bulk': 17.01593315429935, 'l2.preLangevin': -2.6040024216082482, 'R_series': 4.47312492800543e-06, 'R_shunt': 56.87328716459102, 'l2.G_ehp': 1.4635790727300735e+28}
[INFO 12-08 08:56:01] optimpv.axBOtorchOptimizer: Trial 102 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.04527082096091976), 'Gfrac2_JV_nrmse_linear': np.float64(0.015364130811988723), 'Gfrac3_JV_nrmse_linear': np.float64(0.03158982411509747)} and parameters: {'l2.mu_n': -7.985803053987189, 'l2.mu_p': -7.661026031755201, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.2675385695082126, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:56:04] optimpv.axBOtorchOptimizer: Trial 103 with parameters: {'l2.mu_n': -7.7940755900363765, 'l2.mu_p': -7.736343681001056, 'l2.N_t_bulk': 17.094064493367565, 'l2.preLangevin': -2.5743255475926703, 'R_series': 7.616651756950718e-06, 'R_shunt': 30.685593282531833, 'l2.G_ehp': 1.432775401188268e+28}
[INFO 12-08 08:56:04] optimpv.axBOtorchOptimizer: Trial 104 with parameters: {'l2.mu_n': -7.937291246054394, 'l2.mu_p': -7.588856842997895, 'l2.N_t_bulk': 16.922431711382362, 'l2.preLangevin': -2.6202332667631056, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.4646612401890955e+28}
[INFO 12-08 08:56:05] optimpv.axBOtorchOptimizer: Trial 103 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.01803206869146264), 'Gfrac2_JV_nrmse_linear': np.float64(0.008233174890718597), 'Gfrac3_JV_nrmse_linear': np.float64(0.010448520155627444)} and parameters: {'l2.mu_n': -7.7940755900363765, 'l2.mu_p': -7.736343681001056, 'l2.N_t_bulk': 17.094064493367565, 'l2.preLangevin': -2.5743255475926703, 'R_series': 7.616651756950718e-06, 'R_shunt': 30.685593282531833, 'l2.G_ehp': 1.432775401188268e+28}
[INFO 12-08 08:56:05] optimpv.axBOtorchOptimizer: Trial 104 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.01629361280921261), 'Gfrac2_JV_nrmse_linear': np.float64(0.014288967426521726), 'Gfrac3_JV_nrmse_linear': np.float64(0.009775702606052204)} and parameters: {'l2.mu_n': -7.937291246054394, 'l2.mu_p': -7.588856842997895, 'l2.N_t_bulk': 16.922431711382362, 'l2.preLangevin': -2.6202332667631056, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.4646612401890955e+28}
[INFO 12-08 08:56:07] optimpv.axBOtorchOptimizer: Trial 105 with parameters: {'l2.mu_n': -7.926196207986833, 'l2.mu_p': -7.6243512018299775, 'l2.N_t_bulk': 17.12554411713878, 'l2.preLangevin': -2.549925090027763, 'R_series': 1.8456933717416622e-06, 'R_shunt': 27.148566272348308, 'l2.G_ehp': 1.460697709362538e+28}
[INFO 12-08 08:56:07] optimpv.axBOtorchOptimizer: Trial 106 with parameters: {'l2.mu_n': -7.9576662863750265, 'l2.mu_p': -7.517484651992214, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.734085871949685, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.4975531490360748e+28}
[INFO 12-08 08:56:08] optimpv.axBOtorchOptimizer: Trial 105 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.020677171673991315), 'Gfrac2_JV_nrmse_linear': np.float64(0.01119737047860323), 'Gfrac3_JV_nrmse_linear': np.float64(0.010975290283375145)} and parameters: {'l2.mu_n': -7.926196207986833, 'l2.mu_p': -7.6243512018299775, 'l2.N_t_bulk': 17.12554411713878, 'l2.preLangevin': -2.549925090027763, 'R_series': 1.8456933717416622e-06, 'R_shunt': 27.148566272348308, 'l2.G_ehp': 1.460697709362538e+28}
[INFO 12-08 08:56:08] optimpv.axBOtorchOptimizer: Trial 106 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.018489253707381807), 'Gfrac2_JV_nrmse_linear': np.float64(0.02068292695804407), 'Gfrac3_JV_nrmse_linear': np.float64(0.018137434028744036)} and parameters: {'l2.mu_n': -7.9576662863750265, 'l2.mu_p': -7.517484651992214, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.734085871949685, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.4975531490360748e+28}
[INFO 12-08 08:56:10] optimpv.axBOtorchOptimizer: Trial 107 with parameters: {'l2.mu_n': -8.024203387742169, 'l2.mu_p': -7.4156747578247995, 'l2.N_t_bulk': 16.710501842080397, 'l2.preLangevin': -2.7200604335961285, 'R_series': 1e-06, 'R_shunt': 50.72632962210789, 'l2.G_ehp': 1.3805851227656472e+28}
[INFO 12-08 08:56:10] optimpv.axBOtorchOptimizer: Trial 108 with parameters: {'l2.mu_n': -8.32891249831045, 'l2.mu_p': -7.031429374792535, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:56:11] optimpv.axBOtorchOptimizer: Trial 107 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.04007048871394975), 'Gfrac2_JV_nrmse_linear': np.float64(0.013992123822033681), 'Gfrac3_JV_nrmse_linear': np.float64(0.01909746761048513)} and parameters: {'l2.mu_n': -8.024203387742169, 'l2.mu_p': -7.4156747578247995, 'l2.N_t_bulk': 16.710501842080397, 'l2.preLangevin': -2.7200604335961285, 'R_series': 1e-06, 'R_shunt': 50.72632962210789, 'l2.G_ehp': 1.3805851227656472e+28}
[INFO 12-08 08:56:11] optimpv.axBOtorchOptimizer: Trial 108 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.0828797656517884), 'Gfrac2_JV_nrmse_linear': np.float64(0.02772178765507892), 'Gfrac3_JV_nrmse_linear': np.float64(0.03892040846241589)} and parameters: {'l2.mu_n': -8.32891249831045, 'l2.mu_p': -7.031429374792535, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:56:14] optimpv.axBOtorchOptimizer: Trial 109 with parameters: {'l2.mu_n': -8.030316391480792, 'l2.mu_p': -7.238683156578317, 'l2.N_t_bulk': 17.239223857465547, 'l2.preLangevin': -2.921438595128521, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5183113207721256e+28}
[INFO 12-08 08:56:14] optimpv.axBOtorchOptimizer: Trial 110 with parameters: {'l2.mu_n': -7.821569403239703, 'l2.mu_p': -7.639997567774624, 'l2.N_t_bulk': 17.236877117576064, 'l2.preLangevin': -2.683487752081019, 'R_series': 3.784286555720323e-06, 'R_shunt': 36.79141833541336, 'l2.G_ehp': 1.4348399569970316e+28}
[INFO 12-08 08:56:15] optimpv.axBOtorchOptimizer: Trial 109 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.02452047906366603), 'Gfrac2_JV_nrmse_linear': np.float64(0.0211734525599026), 'Gfrac3_JV_nrmse_linear': np.float64(0.022122375537035596)} and parameters: {'l2.mu_n': -8.030316391480792, 'l2.mu_p': -7.238683156578317, 'l2.N_t_bulk': 17.239223857465547, 'l2.preLangevin': -2.921438595128521, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5183113207721256e+28}
[INFO 12-08 08:56:15] optimpv.axBOtorchOptimizer: Trial 110 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.010934912320726761), 'Gfrac2_JV_nrmse_linear': np.float64(0.012389332800478475), 'Gfrac3_JV_nrmse_linear': np.float64(0.0082689631070375)} and parameters: {'l2.mu_n': -7.821569403239703, 'l2.mu_p': -7.639997567774624, 'l2.N_t_bulk': 17.236877117576064, 'l2.preLangevin': -2.683487752081019, 'R_series': 3.784286555720323e-06, 'R_shunt': 36.79141833541336, 'l2.G_ehp': 1.4348399569970316e+28}
[INFO 12-08 08:56:19] optimpv.axBOtorchOptimizer: Trial 111 with parameters: {'l2.mu_n': -7.715334095296364, 'l2.mu_p': -7.794474066214579, 'l2.N_t_bulk': 17.539278373408685, 'l2.preLangevin': -2.6566706229669257, 'R_series': 1.049998206555895e-05, 'R_shunt': 24.1826679105796, 'l2.G_ehp': 1.4257897943660637e+28}
[INFO 12-08 08:56:19] optimpv.axBOtorchOptimizer: Trial 112 with parameters: {'l2.mu_n': -7.842199928920524, 'l2.mu_p': -7.622579468505512, 'l2.N_t_bulk': 16.79648355613102, 'l2.preLangevin': -2.6472076006855607, 'R_series': 2.551540587234701e-06, 'R_shunt': 30.286900857152258, 'l2.G_ehp': 1.4600217308818328e+28}
[INFO 12-08 08:56:20] optimpv.axBOtorchOptimizer: Trial 111 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.012851421753525551), 'Gfrac2_JV_nrmse_linear': np.float64(0.009917034676462549), 'Gfrac3_JV_nrmse_linear': np.float64(0.007996802074206279)} and parameters: {'l2.mu_n': -7.715334095296364, 'l2.mu_p': -7.794474066214579, 'l2.N_t_bulk': 17.539278373408685, 'l2.preLangevin': -2.6566706229669257, 'R_series': 1.049998206555895e-05, 'R_shunt': 24.1826679105796, 'l2.G_ehp': 1.4257897943660637e+28}
[INFO 12-08 08:56:20] optimpv.axBOtorchOptimizer: Trial 112 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.012361645225168152), 'Gfrac2_JV_nrmse_linear': np.float64(0.012115327951856679), 'Gfrac3_JV_nrmse_linear': np.float64(0.010596337338930163)} and parameters: {'l2.mu_n': -7.842199928920524, 'l2.mu_p': -7.622579468505512, 'l2.N_t_bulk': 16.79648355613102, 'l2.preLangevin': -2.6472076006855607, 'R_series': 2.551540587234701e-06, 'R_shunt': 30.286900857152258, 'l2.G_ehp': 1.4600217308818328e+28}
[INFO 12-08 08:56:25] optimpv.axBOtorchOptimizer: Trial 113 with parameters: {'l2.mu_n': -7.671116456447143, 'l2.mu_p': -7.857761494550706, 'l2.N_t_bulk': 17.628285292843213, 'l2.preLangevin': -2.6644320867771416, 'R_series': 1.387956261993137e-05, 'R_shunt': 24.16879996295283, 'l2.G_ehp': 1.4295707988225134e+28}
[INFO 12-08 08:56:25] optimpv.axBOtorchOptimizer: Trial 114 with parameters: {'l2.mu_n': -8.788996068110187, 'l2.mu_p': -7.069027754494031, 'l2.N_t_bulk': 16.278290081713497, 'l2.preLangevin': -2.392993200400541, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:56:25] optimpv.axBOtorchOptimizer: Trial 113 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.012807426913004148), 'Gfrac2_JV_nrmse_linear': np.float64(0.00984779717564717), 'Gfrac3_JV_nrmse_linear': np.float64(0.007638186624130234)} and parameters: {'l2.mu_n': -7.671116456447143, 'l2.mu_p': -7.857761494550706, 'l2.N_t_bulk': 17.628285292843213, 'l2.preLangevin': -2.6644320867771416, 'R_series': 1.387956261993137e-05, 'R_shunt': 24.16879996295283, 'l2.G_ehp': 1.4295707988225134e+28}
[INFO 12-08 08:56:25] optimpv.axBOtorchOptimizer: Trial 114 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.17844502891029615), 'Gfrac2_JV_nrmse_linear': np.float64(0.035294038142961844), 'Gfrac3_JV_nrmse_linear': np.float64(0.09331297096589092)} and parameters: {'l2.mu_n': -8.788996068110187, 'l2.mu_p': -7.069027754494031, 'l2.N_t_bulk': 16.278290081713497, 'l2.preLangevin': -2.392993200400541, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:56:30] optimpv.axBOtorchOptimizer: Trial 115 with parameters: {'l2.mu_n': -7.83540539846259, 'l2.mu_p': -7.461725746835134, 'l2.N_t_bulk': 17.391362347415885, 'l2.preLangevin': -2.9582161335026513, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.441296887308013e+28}
[INFO 12-08 08:56:30] optimpv.axBOtorchOptimizer: Trial 116 with parameters: {'l2.mu_n': -7.757834773187396, 'l2.mu_p': -7.833599468169406, 'l2.N_t_bulk': 16.372289449832433, 'l2.preLangevin': -2.5387209067130057, 'R_series': 1e-06, 'R_shunt': 41.5802583415714, 'l2.G_ehp': 1.451854407008718e+28}
[INFO 12-08 08:56:31] optimpv.axBOtorchOptimizer: Trial 115 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.02490600036522301), 'Gfrac2_JV_nrmse_linear': np.float64(0.024890912346401752), 'Gfrac3_JV_nrmse_linear': np.float64(0.02453237036480798)} and parameters: {'l2.mu_n': -7.83540539846259, 'l2.mu_p': -7.461725746835134, 'l2.N_t_bulk': 17.391362347415885, 'l2.preLangevin': -2.9582161335026513, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.441296887308013e+28}
[INFO 12-08 08:56:31] optimpv.axBOtorchOptimizer: Trial 116 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.019970135456522667), 'Gfrac2_JV_nrmse_linear': np.float64(0.00860372187882963), 'Gfrac3_JV_nrmse_linear': np.float64(0.013980793717551588)} and parameters: {'l2.mu_n': -7.757834773187396, 'l2.mu_p': -7.833599468169406, 'l2.N_t_bulk': 16.372289449832433, 'l2.preLangevin': -2.5387209067130057, 'R_series': 1e-06, 'R_shunt': 41.5802583415714, 'l2.G_ehp': 1.451854407008718e+28}
[INFO 12-08 08:56:33] optimpv.axBOtorchOptimizer: Trial 117 with parameters: {'l2.mu_n': -6.845848869031002, 'l2.mu_p': -9.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 6.472080322438978, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:56:33] optimpv.axBOtorchOptimizer: Trial 118 with parameters: {'l2.mu_n': -6.828995736225416, 'l2.mu_p': -9.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:56:33] optimpv.axBOtorchOptimizer: Trial 117 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.17476533015244217), 'Gfrac2_JV_nrmse_linear': np.float64(0.01592335274076309), 'Gfrac3_JV_nrmse_linear': np.float64(0.09057327146352706)} and parameters: {'l2.mu_n': -6.845848869031002, 'l2.mu_p': -9.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 6.472080322438978, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:56:33] optimpv.axBOtorchOptimizer: Trial 118 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.19849921008274687), 'Gfrac2_JV_nrmse_linear': np.float64(0.015730066028149286), 'Gfrac3_JV_nrmse_linear': np.float64(0.10770581049553432)} and parameters: {'l2.mu_n': -6.828995736225416, 'l2.mu_p': -9.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:56:35] optimpv.axBOtorchOptimizer: Trial 119 with parameters: {'l2.mu_n': -7.240675571306726, 'l2.mu_p': -8.174749524293162, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 2.093906519785888, 'l2.G_ehp': 1.416062028860111e+28}
[INFO 12-08 08:56:35] optimpv.axBOtorchOptimizer: Trial 120 with parameters: {'l2.mu_n': -6.998060143567072, 'l2.mu_p': -9.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.9885069085438927, 'R_series': 0.0026027515283677458, 'R_shunt': 0.01, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:56:36] optimpv.axBOtorchOptimizer: Trial 119 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.027963302597237616), 'Gfrac2_JV_nrmse_linear': np.float64(0.018710616581676102), 'Gfrac3_JV_nrmse_linear': np.float64(0.016989787796735583)} and parameters: {'l2.mu_n': -7.240675571306726, 'l2.mu_p': -8.174749524293162, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 2.093906519785888, 'l2.G_ehp': 1.416062028860111e+28}
[INFO 12-08 08:56:36] optimpv.axBOtorchOptimizer: Trial 120 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.37404886787129565), 'Gfrac2_JV_nrmse_linear': np.float64(0.18447959220750337), 'Gfrac3_JV_nrmse_linear': np.float64(0.26180715314396924)} and parameters: {'l2.mu_n': -6.998060143567072, 'l2.mu_p': -9.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.9885069085438927, 'R_series': 0.0026027515283677458, 'R_shunt': 0.01, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:56:38] optimpv.axBOtorchOptimizer: Trial 121 with parameters: {'l2.mu_n': -9.0, 'l2.mu_p': -6.0, 'l2.N_t_bulk': 18.453765117657845, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:56:38] optimpv.axBOtorchOptimizer: Trial 122 with parameters: {'l2.mu_n': -7.204056841840389, 'l2.mu_p': -8.240034241075303, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 52.67076008958219, 'l2.G_ehp': 1.4983771659649784e+28}
[INFO 12-08 08:56:38] optimpv.axBOtorchOptimizer: Trial 121 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.20487338597999377), 'Gfrac2_JV_nrmse_linear': np.float64(0.06588216740762198), 'Gfrac3_JV_nrmse_linear': np.float64(0.12491283858728987)} and parameters: {'l2.mu_n': -9.0, 'l2.mu_p': -6.0, 'l2.N_t_bulk': 18.453765117657845, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:56:38] optimpv.axBOtorchOptimizer: Trial 122 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.027462530517662086), 'Gfrac2_JV_nrmse_linear': np.float64(0.019710834640396958), 'Gfrac3_JV_nrmse_linear': np.float64(0.019958364354626505)} and parameters: {'l2.mu_n': -7.204056841840389, 'l2.mu_p': -8.240034241075303, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 52.67076008958219, 'l2.G_ehp': 1.4983771659649784e+28}
[INFO 12-08 08:56:42] optimpv.axBOtorchOptimizer: Trial 123 with parameters: {'l2.mu_n': -7.4328485511764155, 'l2.mu_p': -8.066437801327218, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.8082691098055945, 'R_series': 1e-06, 'R_shunt': 12.997732160981952, 'l2.G_ehp': 1.4507968436599357e+28}
[INFO 12-08 08:56:42] optimpv.axBOtorchOptimizer: Trial 124 with parameters: {'l2.mu_n': -7.310606963757619, 'l2.mu_p': -7.987131718857676, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.9894198508866077, 'R_series': 1e-06, 'R_shunt': 24.687704461206025, 'l2.G_ehp': 1.3791099423601453e+28}
[INFO 12-08 08:56:42] optimpv.axBOtorchOptimizer: Trial 123 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.020156375680244167), 'Gfrac2_JV_nrmse_linear': np.float64(0.018450641431586437), 'Gfrac3_JV_nrmse_linear': np.float64(0.018499270748539624)} and parameters: {'l2.mu_n': -7.4328485511764155, 'l2.mu_p': -8.066437801327218, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.8082691098055945, 'R_series': 1e-06, 'R_shunt': 12.997732160981952, 'l2.G_ehp': 1.4507968436599357e+28}
[INFO 12-08 08:56:42] optimpv.axBOtorchOptimizer: Trial 124 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.025277528007361546), 'Gfrac2_JV_nrmse_linear': np.float64(0.02333212486864077), 'Gfrac3_JV_nrmse_linear': np.float64(0.02108802876978184)} and parameters: {'l2.mu_n': -7.310606963757619, 'l2.mu_p': -7.987131718857676, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -2.9894198508866077, 'R_series': 1e-06, 'R_shunt': 24.687704461206025, 'l2.G_ehp': 1.3791099423601453e+28}
[INFO 12-08 08:56:44] optimpv.axBOtorchOptimizer: Trial 125 with parameters: {'l2.mu_n': -6.9642023649471785, 'l2.mu_p': -8.103496948328816, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 4.216976386872093, 'l2.G_ehp': 1.5187882783160473e+28}
[INFO 12-08 08:56:44] optimpv.axBOtorchOptimizer: Trial 126 with parameters: {'l2.mu_n': -6.573946950596141, 'l2.mu_p': -8.194238303331106, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 80.2168446669805, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:56:45] optimpv.axBOtorchOptimizer: Trial 125 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.040094351468561426), 'Gfrac2_JV_nrmse_linear': np.float64(0.044029406397492754), 'Gfrac3_JV_nrmse_linear': np.float64(0.0409228713478571)} and parameters: {'l2.mu_n': -6.9642023649471785, 'l2.mu_p': -8.103496948328816, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 4.216976386872093, 'l2.G_ehp': 1.5187882783160473e+28}
[INFO 12-08 08:56:45] optimpv.axBOtorchOptimizer: Trial 126 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.07344289293796477), 'Gfrac2_JV_nrmse_linear': np.float64(0.06993675103605103), 'Gfrac3_JV_nrmse_linear': np.float64(0.06837529427961297)} and parameters: {'l2.mu_n': -6.573946950596141, 'l2.mu_p': -8.194238303331106, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 80.2168446669805, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:56:48] optimpv.axBOtorchOptimizer: Trial 127 with parameters: {'l2.mu_n': -7.277578309959953, 'l2.mu_p': -8.222113340817664, 'l2.N_t_bulk': 16.954792217732845, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 14.888584868552229, 'l2.G_ehp': 1.4224932833636789e+28}
[INFO 12-08 08:56:48] optimpv.axBOtorchOptimizer: Trial 128 with parameters: {'l2.mu_n': -7.816283468997493, 'l2.mu_p': -7.724114115818639, 'l2.N_t_bulk': 17.01711116615199, 'l2.preLangevin': -2.597655527793881, 'R_series': 2.782484879457111e-06, 'R_shunt': 44.848486495786254, 'l2.G_ehp': 1.43793913871756e+28}
[INFO 12-08 08:56:49] optimpv.axBOtorchOptimizer: Trial 127 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.028402781920190606), 'Gfrac2_JV_nrmse_linear': np.float64(0.017872530101839562), 'Gfrac3_JV_nrmse_linear': np.float64(0.014188337164196931)} and parameters: {'l2.mu_n': -7.277578309959953, 'l2.mu_p': -8.222113340817664, 'l2.N_t_bulk': 16.954792217732845, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 14.888584868552229, 'l2.G_ehp': 1.4224932833636789e+28}
[INFO 12-08 08:56:49] optimpv.axBOtorchOptimizer: Trial 128 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.014853146350353456), 'Gfrac2_JV_nrmse_linear': np.float64(0.009955198107928174), 'Gfrac3_JV_nrmse_linear': np.float64(0.008802107106250739)} and parameters: {'l2.mu_n': -7.816283468997493, 'l2.mu_p': -7.724114115818639, 'l2.N_t_bulk': 17.01711116615199, 'l2.preLangevin': -2.597655527793881, 'R_series': 2.782484879457111e-06, 'R_shunt': 44.848486495786254, 'l2.G_ehp': 1.43793913871756e+28}
[INFO 12-08 08:56:51] optimpv.axBOtorchOptimizer: Trial 129 with parameters: {'l2.mu_n': -6.0, 'l2.mu_p': -6.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': 0.0, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:56:51] optimpv.axBOtorchOptimizer: Trial 130 with parameters: {'l2.mu_n': -6.0, 'l2.mu_p': -9.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}
[INFO 12-08 08:56:52] optimpv.axBOtorchOptimizer: Trial 129 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.2588490251905205), 'Gfrac2_JV_nrmse_linear': np.float64(0.2634171985617151), 'Gfrac3_JV_nrmse_linear': np.float64(0.26138310712429413)} and parameters: {'l2.mu_n': -6.0, 'l2.mu_p': -6.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': 0.0, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.3749133023443778e+28}
[INFO 12-08 08:56:52] optimpv.axBOtorchOptimizer: Trial 130 completed with results: {'Gfrac1_JV_nrmse_linear': np.float64(0.18339733136493513), 'Gfrac2_JV_nrmse_linear': np.float64(0.07359010321788499), 'Gfrac3_JV_nrmse_linear': np.float64(0.11739622376772163)} and parameters: {'l2.mu_n': -6.0, 'l2.mu_p': -9.0, 'l2.N_t_bulk': 16.0, 'l2.preLangevin': -3.0, 'R_series': 1e-06, 'R_shunt': 100.0, 'l2.G_ehp': 1.5589412339101684e+28}

# get the best parameters and update the params list in the optimizer and the agent
ax_client = optimizer.ax_client # get the ax client
optimizer.update_params_with_best_balance() # update the params list in the optimizer with the best parameters
jv_main.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:
    print(p.name, 'fitted value:', p.value)

print('\nSimSS command line:')
print(jv_main.get_SIMsalabim_clean_cmd(jv_main.params)) # print the simss command line with the best parameters

Best parameters:
l2.mu_n fitted value: 2.2827372108182763e-08
l2.mu_p fitted value: 1.240602139562191e-08
l2.N_t_bulk fitted value: 4.108628687633856e+17
l2.preLangevin fitted value: 0.0020092521403935067
R_series fitted value: 1.4807686976146275e-05
R_shunt fitted value: 30.503890174763733
l2.G_ehp fitted value: 1.4373871841225316e+28

SimSS command line:
./simss -l2.mu_n 2.2827372108182763e-08 -l2.mu_p 1.2406021395621912e-08 -l2.N_t_bulk 4.108628687633856e+17 -l2.preLangevin 0.0020092521403935067 -R_series 1.4807686976146275e-05 -R_shunt 30.503890174763733 -l2.G_ehp 1.4373871841225316e+28

# Plot optimization results
data = ax_client.summarize()
all_metrics = optimizer.all_metrics
plt.figure()
plt.plot(np.minimum.accumulate(data[all_metrics]), label="Best value seen so far")
plt.yscale("log")
plt.xlabel("Iteration")
plt.ylabel("Target metric: " + metric + " with " + loss + " loss")
plt.legend()
plt.title("Best value seen so far")

plt.show()

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 = ax_client.get_pareto_frontier(use_model_predictions=False)

cm = matplotlib.colormaps.get_cmap('viridis')
df = get_df_from_ax(params, optimizer)
# create pareto df
dum_dic = {}
for eto in pareto:
    for metr in optimizer.all_metrics:
        if metr not in dum_dic.keys():
            dum_dic[metr] = []
        dum_dic[metr].append(eto[1][metr][0])
df_pareto = pd.DataFrame(dum_dic)

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.xlabel(c[0])
    plt.ylabel(c[1])
    plt.xscale('log')
    plt.yscale('log')


    plt.show()

# Plot the density of the exploration of the parameters
# this gives a nice visualization of where the optimizer focused its exploration and may show some correlation between the parameters
plot_dens = True
if plot_dens:
    from optimpv.posterior.exploration_density import *
    best_parameters = {}
    for p in optimizer.params:
        best_parameters[p.name] = p.value

    fig_dens, ax_dens = plot_density_exploration(params, optimizer = optimizer, best_parameters = best_parameters, optimizer_type = 'ax')

# rerun the simulation with the best parameters
yfit = jv_main.run(parameters={}) # run the simulation with the best parameters
res_dic = jv_main.run_Ax(parameters={})
keys = list(res_dic.keys())
print('Best combined target metric:', res_dic[keys[0]])
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()

Best combined target metric: 0.007295326231327441

# 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('PM6_L8BO',session_path)
sim.clean_up_output('BM_HTL',session_path)
sim.clean_up_output('simulation_setup_PM6_L8BO',session_path)