Design of Experiment: Optimize perovskite solar cells efficiency

This notebook is made to use optimPV for experimental design. Here, we show how to load some data from a presampling, and how to use optimPV to suggest the next set of experiment using Bayesian optimization. The goal here is to optimize the processing conditions for a perovskite solar cell to maximize the power conversion efficiency (PCE).

Note: The data used here is real data generated in the i-MEET and HI-ERN labs at the university of Erlangen-Nuremberg (FAU) by Jiyun Zhang for the paper: Autonomous Optimization of Air-Processed Perovskite Solar Cell in a 6D Parameter Space

[1]:

# Import necessary libraries
import warnings, os, sys, shutil
# remove warnings from the output
os.environ["PYTHONWARNINGS"] = "ignore"
warnings.filterwarnings(action='ignore', category=FutureWarning)
warnings.filterwarnings(action='ignore', category=UserWarning)
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import default_rng
import torch, copy, uuid
import pySIMsalabim as sim
from pySIMsalabim.experiments.JV_steady_state import *
import ax, logging
from ax.utils.notebook.plotting import init_notebook_plotting, render
init_notebook_plotting() # for Jupyter notebooks

try:
    from optimpv import *
    from optimpv.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 08-13 17:18:25] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.
[INFO 08-13 17:18:25] 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 data

[2]:

# Define the path to the data
data_dir =os.path.join(os.path.abspath('../'),'Data','6D_pero_opti') # path to the data directory

# Load the data
df = pd.read_csv(os.path.join(data_dir,'6D_pero_opti.csv'),sep=r'\s+') # load the data

# Display some information about the data
print(df.describe())

       Spin_Speed_1  Duration_t1  Spin_Speed_2  Dispense_Speed  Duration_t3  \
count     76.000000    76.000000     76.000000       76.000000    76.000000
mean    1520.486842    18.276316   2273.289474      234.697368    17.315789
std      458.510240     7.000188    501.488845       83.133470     7.919950
min      540.000000     5.000000   1021.000000       16.000000     5.000000
25%     1203.500000    13.750000   2019.500000      201.750000    10.750000
50%     1511.000000    18.000000   2385.500000      247.500000    17.000000
75%     1801.250000    22.000000   2671.000000      277.500000    23.250000
max     2579.000000    34.000000   3000.000000      396.000000    35.000000

       Spin_Speed_3        Jsc        Voc         FF       Pmax       Vmpp  \
count     76.000000  76.000000  76.000000  76.000000  76.000000  76.000000
mean    3717.789474  24.391395   1.021157   0.724692  18.320711   0.825778
std      917.207346   1.872929   0.108197   0.055290   3.486546   0.105413
min     2000.000000  12.231000   0.616300   0.448800   4.690000   0.463000
25%     3002.000000  24.173500   0.987650   0.704400  17.477000   0.785750
50%     3825.000000  24.615000   1.038750   0.734700  18.851500   0.835000
75%     4471.500000  25.279500   1.100725   0.759925  20.347250   0.895000
max     5000.000000  25.938000   1.162200   0.810900  23.729000   0.990100

           Rseries       Rshunt
count    76.000000    76.000000
mean   3720.112882  2763.793289
std    1487.516713  1956.263177
min      92.129000     2.990000
25%    3146.452500   228.750000
50%    3739.157000  3290.000000
75%    4567.406250  4285.000000
max    6513.151000  6860.000000

Define the parameters for the simulation

[3]:

params = [] # list of parameters to be optimized

Spin_Speed_1 = FitParam(name = 'Spin_Speed_1', value = 1000, bounds = [500,3000], value_type = 'int', display_name='Spin Speed 1', unit='rpm', axis_type = 'linear')
params.append(Spin_Speed_1)

Duration_t1 = FitParam(name = 'Duration_t1', value = 10, bounds = [5,35], value_type = 'int', display_name='Duration t1', unit='s', axis_type = 'linear')
params.append(Duration_t1)

Spin_Speed_2 = FitParam(name = 'Spin_Speed_2', value = 1000, bounds = [1000,3000], value_type = 'int', display_name='Spin Speed 2', unit='rpm', axis_type = 'linear')
params.append(Spin_Speed_2)

Dispense_Speed = FitParam(name = 'Dispense_Speed', value = 100, bounds = [10,400], value_type = 'int', display_name='Dispense Speed', unit='rpm', axis_type = 'linear')
params.append(Dispense_Speed)

Duration_t3 = FitParam(name = 'Duration_t3', value = 10, bounds = [5,35], value_type = 'int', display_name='Duration t3', unit='s', axis_type = 'linear')
params.append(Duration_t3)

Spin_Speed_3 = FitParam(name = 'Spin_Speed_3', value = 3000, bounds = [2000,5000], value_type = 'int', display_name='Spin Speed 3', unit='rpm', axis_type = 'linear')
params.append(Spin_Speed_3)

Run the optimization

[4]:

# Define the Agent and the target metric/loss function
from optimpv.general.SuggestOnlyAgent import SuggestOnlyAgent

suggest = SuggestOnlyAgent(params,exp_format='Pmax',minimize=False,tracking_exp_format=['Jsc','Voc','FF'],name=None)

[5]:

from optimpv.axBOtorch.axBOtorchOptimizer import axBOtorchOptimizer
from botorch.acquisition.logei import qLogNoisyExpectedImprovement
from ax.adapter.transforms.standardize_y import StandardizeY
from ax.adapter.transforms.unit_x import UnitX
from ax.adapter.transforms.remove_fixed import RemoveFixed
from ax.adapter.transforms.log import Log
from ax.generators.torch.botorch_modular.utils import ModelConfig
from ax.generators.torch.botorch_modular.surrogate import SurrogateSpec
from gpytorch.kernels import MaternKernel
from gpytorch.kernels import ScaleKernel
from botorch.models import SingleTaskGP
from botorch.models.map_saas import AdditiveMapSaasSingleTaskGP
from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
model_gen_kwargs_list = None
parameter_constraints = None

model_kwargs_list = [{"torch_device":torch.device("cuda" if torch.cuda.is_available() else "cpu"),'botorch_acqf_class':qLogNoisyExpectedImprovement,'transforms':[RemoveFixed, Log, UnitX, StandardizeY],'surrogate_spec':SurrogateSpec(model_configs=[ModelConfig(botorch_model_class=SaasFullyBayesianSingleTaskGP,covar_module_class=ScaleKernel, covar_module_options={'base_kernel':MaternKernel(nu=2.5, ard_num_dims=len(params))})])}]
model_kwargs_list =None

# Define the optimizer
optimizer = axBOtorchOptimizer(params = params, agents = suggest, models = ['BOTORCH_MODULAR'],n_batches = [1], batch_size = [6], ax_client = None,  max_parallelism = -1, model_kwargs_list = model_kwargs_list, model_gen_kwargs_list = None, name = 'ax_opti',suggest_only = True,existing_data=df,verbose_logging=True)

[6]:

optimizer.optimize() # run the optimization with ax

[WARNING 08-13 17:18:26] ax.api.client: Metric IMetric('Jsc') not found in optimization config, added as tracking metric.
[WARNING 08-13 17:18:26] ax.api.client: Metric IMetric('Voc') not found in optimization config, added as tracking metric.
[WARNING 08-13 17:18:26] ax.api.client: Metric IMetric('FF') not found in optimization config, added as tracking metric.
[INFO 08-13 17:18:26] ax.api.client: Trial 0 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 0 with parameters {'Spin_Speed_1': 2404, 'Duration_t1': 15, 'Spin_Speed_2': 1745, 'Dispense_Speed': 203, 'Duration_t3': 13, 'Spin_Speed_3': 4494} and raw data {'Pmax': np.float64(13.83), 'Jsc': np.float64(24.507), 'Voc': np.float64(0.91), 'FF': np.float64(0.6201)}
[INFO 08-13 17:18:26] ax.api.client: Trial 1 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 1 with parameters {'Spin_Speed_1': 1835, 'Duration_t1': 17, 'Spin_Speed_2': 2289, 'Dispense_Speed': 289, 'Duration_t3': 11, 'Spin_Speed_3': 4389} and raw data {'Pmax': np.float64(19.712), 'Jsc': np.float64(25.733), 'Voc': np.float64(1.009), 'FF': np.float64(0.7592)}
[INFO 08-13 17:18:26] ax.api.client: Trial 2 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 2 with parameters {'Spin_Speed_1': 1067, 'Duration_t1': 33, 'Spin_Speed_2': 2352, 'Dispense_Speed': 235, 'Duration_t3': 5, 'Spin_Speed_3': 2861} and raw data {'Pmax': np.float64(18.797), 'Jsc': np.float64(24.692), 'Voc': np.float64(1.0321), 'FF': np.float64(0.7376)}
[INFO 08-13 17:18:26] ax.api.client: Trial 3 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 3 with parameters {'Spin_Speed_1': 2276, 'Duration_t1': 18, 'Spin_Speed_2': 1821, 'Dispense_Speed': 26, 'Duration_t3': 15, 'Spin_Speed_3': 3241} and raw data {'Pmax': np.float64(12.903), 'Jsc': np.float64(24.195), 'Voc': np.float64(0.8197), 'FF': np.float64(0.6506)}
[INFO 08-13 17:18:26] ax.api.client: Trial 4 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 4 with parameters {'Spin_Speed_1': 2147, 'Duration_t1': 30, 'Spin_Speed_2': 2816, 'Dispense_Speed': 290, 'Duration_t3': 29, 'Spin_Speed_3': 4981} and raw data {'Pmax': np.float64(18.556), 'Jsc': np.float64(24.268), 'Voc': np.float64(0.9951), 'FF': np.float64(0.7684)}
[INFO 08-13 17:18:26] ax.api.client: Trial 5 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 5 with parameters {'Spin_Speed_1': 1152, 'Duration_t1': 28, 'Spin_Speed_2': 2734, 'Dispense_Speed': 122, 'Duration_t3': 8, 'Spin_Speed_3': 4856} and raw data {'Pmax': np.float64(17.962), 'Jsc': np.float64(24.481), 'Voc': np.float64(1.01), 'FF': np.float64(0.7264)}
[INFO 08-13 17:18:26] ax.api.client: Trial 6 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 6 with parameters {'Spin_Speed_1': 1240, 'Duration_t1': 23, 'Spin_Speed_2': 2993, 'Dispense_Speed': 190, 'Duration_t3': 19, 'Spin_Speed_3': 2560} and raw data {'Pmax': np.float64(18.726), 'Jsc': np.float64(25.575), 'Voc': np.float64(0.9926), 'FF': np.float64(0.7377)}
[INFO 08-13 17:18:26] ax.api.client: Trial 7 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 7 with parameters {'Spin_Speed_1': 2471, 'Duration_t1': 21, 'Spin_Speed_2': 1394, 'Dispense_Speed': 138, 'Duration_t3': 29, 'Spin_Speed_3': 2046} and raw data {'Pmax': np.float64(10.414), 'Jsc': np.float64(23.995), 'Voc': np.float64(0.685), 'FF': np.float64(0.6336)}
[INFO 08-13 17:18:26] ax.api.client: Trial 8 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 8 with parameters {'Spin_Speed_1': 907, 'Duration_t1': 9, 'Spin_Speed_2': 2766, 'Dispense_Speed': 314, 'Duration_t3': 22, 'Spin_Speed_3': 4323} and raw data {'Pmax': np.float64(17.342), 'Jsc': np.float64(24.283), 'Voc': np.float64(0.994), 'FF': np.float64(0.7185)}
[INFO 08-13 17:18:26] ax.api.client: Trial 9 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 9 with parameters {'Spin_Speed_1': 2034, 'Duration_t1': 18, 'Spin_Speed_2': 1021, 'Dispense_Speed': 90, 'Duration_t3': 7, 'Spin_Speed_3': 4776} and raw data {'Pmax': np.float64(5.922), 'Jsc': np.float64(12.231), 'Voc': np.float64(0.8321), 'FF': np.float64(0.5819)}
[INFO 08-13 17:18:26] ax.api.client: Trial 10 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 10 with parameters {'Spin_Speed_1': 540, 'Duration_t1': 19, 'Spin_Speed_2': 2383, 'Dispense_Speed': 16, 'Duration_t3': 23, 'Spin_Speed_3': 4632} and raw data {'Pmax': np.float64(15.154), 'Jsc': np.float64(23.558), 'Voc': np.float64(0.9297), 'FF': np.float64(0.6919)}
[INFO 08-13 17:18:26] ax.api.client: Trial 11 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 11 with parameters {'Spin_Speed_1': 1532, 'Duration_t1': 12, 'Spin_Speed_2': 1955, 'Dispense_Speed': 383, 'Duration_t3': 18, 'Spin_Speed_3': 4137} and raw data {'Pmax': np.float64(19.105), 'Jsc': np.float64(25.214), 'Voc': np.float64(0.9921), 'FF': np.float64(0.7637)}
[INFO 08-13 17:18:26] ax.api.client: Trial 12 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 12 with parameters {'Spin_Speed_1': 1024, 'Duration_t1': 8, 'Spin_Speed_2': 2631, 'Dispense_Speed': 367, 'Duration_t3': 21, 'Spin_Speed_3': 2135} and raw data {'Pmax': np.float64(17.644), 'Jsc': np.float64(24.944), 'Voc': np.float64(1.008), 'FF': np.float64(0.7018)}
[INFO 08-13 17:18:26] ax.api.client: Trial 13 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 13 with parameters {'Spin_Speed_1': 1768, 'Duration_t1': 22, 'Spin_Speed_2': 2927, 'Dispense_Speed': 77, 'Duration_t3': 10, 'Spin_Speed_3': 4208} and raw data {'Pmax': np.float64(18.34), 'Jsc': np.float64(24.301), 'Voc': np.float64(1.0172), 'FF': np.float64(0.742)}
[INFO 08-13 17:18:26] ax.api.client: Trial 14 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 14 with parameters {'Spin_Speed_1': 1598, 'Duration_t1': 29, 'Spin_Speed_2': 2592, 'Dispense_Speed': 396, 'Duration_t3': 23, 'Spin_Speed_3': 3688} and raw data {'Pmax': np.float64(16.013), 'Jsc': np.float64(23.929), 'Voc': np.float64(0.9692), 'FF': np.float64(0.6905)}
[INFO 08-13 17:18:26] ax.api.client: Trial 15 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 15 with parameters {'Spin_Speed_1': 691, 'Duration_t1': 14, 'Spin_Speed_2': 1661, 'Dispense_Speed': 248, 'Duration_t3': 19, 'Spin_Speed_3': 3523} and raw data {'Pmax': np.float64(16.773), 'Jsc': np.float64(24.251), 'Voc': np.float64(0.9949), 'FF': np.float64(0.6952)}
[INFO 08-13 17:18:26] ax.api.client: Trial 16 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 16 with parameters {'Spin_Speed_1': 1745, 'Duration_t1': 28, 'Spin_Speed_2': 1098, 'Dispense_Speed': 108, 'Duration_t3': 16, 'Spin_Speed_3': 2467} and raw data {'Pmax': np.float64(7.984), 'Jsc': np.float64(20.114), 'Voc': np.float64(0.6694), 'FF': np.float64(0.593)}
[INFO 08-13 17:18:26] ax.api.client: Trial 17 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 17 with parameters {'Spin_Speed_1': 2111, 'Duration_t1': 31, 'Spin_Speed_2': 2065, 'Dispense_Speed': 349, 'Duration_t3': 27, 'Spin_Speed_3': 2251} and raw data {'Pmax': np.float64(17.178), 'Jsc': np.float64(25.454), 'Voc': np.float64(0.917), 'FF': np.float64(0.7359)}
[INFO 08-13 17:18:26] ax.api.client: Trial 18 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 18 with parameters {'Spin_Speed_1': 621, 'Duration_t1': 26, 'Spin_Speed_2': 1221, 'Dispense_Speed': 51, 'Duration_t3': 26, 'Spin_Speed_3': 3907} and raw data {'Pmax': np.float64(17.19), 'Jsc': np.float64(25.549), 'Voc': np.float64(0.9484), 'FF': np.float64(0.7094)}
[INFO 08-13 17:18:26] ax.api.client: Trial 19 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 19 with parameters {'Spin_Speed_1': 1442, 'Duration_t1': 9, 'Spin_Speed_2': 1297, 'Dispense_Speed': 306, 'Duration_t3': 9, 'Spin_Speed_3': 3972} and raw data {'Pmax': np.float64(18.583), 'Jsc': np.float64(24.824), 'Voc': np.float64(0.9231), 'FF': np.float64(0.8109)}
[INFO 08-13 17:18:26] ax.api.client: Trial 20 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 20 with parameters {'Spin_Speed_1': 810, 'Duration_t1': 33, 'Spin_Speed_2': 1776, 'Dispense_Speed': 258, 'Duration_t3': 6, 'Spin_Speed_3': 4599} and raw data {'Pmax': np.float64(14.294), 'Jsc': np.float64(24.913), 'Voc': np.float64(0.8165), 'FF': np.float64(0.7027)}
[INFO 08-13 17:18:26] ax.api.client: Trial 21 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 21 with parameters {'Spin_Speed_1': 1308, 'Duration_t1': 32, 'Spin_Speed_2': 2558, 'Dispense_Speed': 146, 'Duration_t3': 12, 'Spin_Speed_3': 3111} and raw data {'Pmax': np.float64(18.165), 'Jsc': np.float64(24.87), 'Voc': np.float64(0.9743), 'FF': np.float64(0.7497)}
[INFO 08-13 17:18:26] ax.api.client: Trial 22 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 22 with parameters {'Spin_Speed_1': 1333, 'Duration_t1': 20, 'Spin_Speed_2': 1878, 'Dispense_Speed': 212, 'Duration_t3': 24, 'Spin_Speed_3': 3386} and raw data {'Pmax': np.float64(19.824), 'Jsc': np.float64(25.625), 'Voc': np.float64(1.0253), 'FF': np.float64(0.7546)}
[INFO 08-13 17:18:26] ax.api.client: Trial 23 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 23 with parameters {'Spin_Speed_1': 863, 'Duration_t1': 34, 'Spin_Speed_2': 1535, 'Dispense_Speed': 104, 'Duration_t3': 25, 'Spin_Speed_3': 3160} and raw data {'Pmax': np.float64(18.115), 'Jsc': np.float64(25.641), 'Voc': np.float64(1.0022), 'FF': np.float64(0.7049)}
[INFO 08-13 17:18:26] ax.api.client: Trial 24 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 24 with parameters {'Spin_Speed_1': 675, 'Duration_t1': 26, 'Spin_Speed_2': 1604, 'Dispense_Speed': 66, 'Duration_t3': 21, 'Spin_Speed_3': 2720} and raw data {'Pmax': np.float64(16.732), 'Jsc': np.float64(24.788), 'Voc': np.float64(0.9533), 'FF': np.float64(0.7081)}
[INFO 08-13 17:18:26] ax.api.client: Trial 25 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 25 with parameters {'Spin_Speed_1': 1420, 'Duration_t1': 12, 'Spin_Speed_2': 2015, 'Dispense_Speed': 277, 'Duration_t3': 7, 'Spin_Speed_3': 3621} and raw data {'Pmax': np.float64(18.876), 'Jsc': np.float64(24.213), 'Voc': np.float64(1.0209), 'FF': np.float64(0.7637)}
[INFO 08-13 17:18:26] ax.api.client: Trial 26 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 26 with parameters {'Spin_Speed_1': 1973, 'Duration_t1': 24, 'Spin_Speed_2': 2137, 'Dispense_Speed': 218, 'Duration_t3': 16, 'Spin_Speed_3': 2822} and raw data {'Pmax': np.float64(15.688), 'Jsc': np.float64(24.022), 'Voc': np.float64(0.9163), 'FF': np.float64(0.7127)}
[INFO 08-13 17:18:26] ax.api.client: Trial 27 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 27 with parameters {'Spin_Speed_1': 2349, 'Duration_t1': 15, 'Spin_Speed_2': 1315, 'Dispense_Speed': 35, 'Duration_t3': 18, 'Spin_Speed_3': 2282} and raw data {'Pmax': np.float64(4.69), 'Jsc': np.float64(16.955), 'Voc': np.float64(0.6163), 'FF': np.float64(0.4488)}
[INFO 08-13 17:18:26] ax.api.client: Trial 28 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 28 with parameters {'Spin_Speed_1': 2211, 'Duration_t1': 10, 'Spin_Speed_2': 2465, 'Dispense_Speed': 156, 'Duration_t3': 13, 'Spin_Speed_3': 2585} and raw data {'Pmax': np.float64(17.522), 'Jsc': np.float64(24.37), 'Voc': np.float64(0.9586), 'FF': np.float64(0.7501)}
[INFO 08-13 17:18:26] ax.api.client: Trial 29 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 29 with parameters {'Spin_Speed_1': 1632, 'Duration_t1': 20, 'Spin_Speed_2': 2233, 'Dispense_Speed': 328, 'Duration_t3': 14, 'Spin_Speed_3': 3025} and raw data {'Pmax': np.float64(19.463), 'Jsc': np.float64(24.711), 'Voc': np.float64(1.0388), 'FF': np.float64(0.7582)}
[INFO 08-13 17:18:26] ax.api.client: Trial 30 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 30 with parameters {'Spin_Speed_1': 1881, 'Duration_t1': 25, 'Spin_Speed_2': 1449, 'Dispense_Speed': 360, 'Duration_t3': 10, 'Spin_Speed_3': 3843} and raw data {'Pmax': np.float64(13.608), 'Jsc': np.float64(23.312), 'Voc': np.float64(0.9011), 'FF': np.float64(0.6478)}
[INFO 08-13 17:18:26] ax.api.client: Trial 31 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 31 with parameters {'Spin_Speed_1': 953, 'Duration_t1': 13, 'Spin_Speed_2': 1167, 'Dispense_Speed': 177, 'Duration_t3': 27, 'Spin_Speed_3': 3463} and raw data {'Pmax': np.float64(16.27), 'Jsc': np.float64(23.852), 'Voc': np.float64(0.948), 'FF': np.float64(0.7195)}
[INFO 08-13 17:18:26] ax.api.client: Trial 32 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 32 with parameters {'Spin_Speed_1': 1474, 'Duration_t1': 14, 'Spin_Speed_2': 2324, 'Dispense_Speed': 250, 'Duration_t3': 19, 'Spin_Speed_3': 3910} and raw data {'Pmax': np.float64(22.973), 'Jsc': np.float64(25.429), 'Voc': np.float64(1.1523), 'FF': np.float64(0.784)}
[INFO 08-13 17:18:26] ax.api.client: Trial 33 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 33 with parameters {'Spin_Speed_1': 1515, 'Duration_t1': 16, 'Spin_Speed_2': 2676, 'Dispense_Speed': 269, 'Duration_t3': 6, 'Spin_Speed_3': 3552} and raw data {'Pmax': np.float64(22.298), 'Jsc': np.float64(24.912), 'Voc': np.float64(1.1458), 'FF': np.float64(0.7811)}
[INFO 08-13 17:18:26] ax.api.client: Trial 34 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 34 with parameters {'Spin_Speed_1': 1165, 'Duration_t1': 23, 'Spin_Speed_2': 2063, 'Dispense_Speed': 241, 'Duration_t3': 32, 'Spin_Speed_3': 2863} and raw data {'Pmax': np.float64(21.484), 'Jsc': np.float64(25.132), 'Voc': np.float64(1.1228), 'FF': np.float64(0.7614)}
[INFO 08-13 17:18:26] ax.api.client: Trial 35 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 35 with parameters {'Spin_Speed_1': 1913, 'Duration_t1': 13, 'Spin_Speed_2': 2671, 'Dispense_Speed': 222, 'Duration_t3': 22, 'Spin_Speed_3': 5000} and raw data {'Pmax': np.float64(20.381), 'Jsc': np.float64(25.68), 'Voc': np.float64(1.082), 'FF': np.float64(0.7335)}
[INFO 08-13 17:18:26] ax.api.client: Trial 36 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 36 with parameters {'Spin_Speed_1': 1421, 'Duration_t1': 8, 'Spin_Speed_2': 2021, 'Dispense_Speed': 279, 'Duration_t3': 25, 'Spin_Speed_3': 3002} and raw data {'Pmax': np.float64(19.312), 'Jsc': np.float64(24.773), 'Voc': np.float64(1.086), 'FF': np.float64(0.7178)}
[INFO 08-13 17:18:26] ax.api.client: Trial 37 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 37 with parameters {'Spin_Speed_1': 1840, 'Duration_t1': 5, 'Spin_Speed_2': 2401, 'Dispense_Speed': 360, 'Duration_t3': 10, 'Spin_Speed_3': 3825} and raw data {'Pmax': np.float64(19.06), 'Jsc': np.float64(24.99), 'Voc': np.float64(1.063), 'FF': np.float64(0.7175)}
[INFO 08-13 17:18:26] ax.api.client: Trial 38 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 38 with parameters {'Spin_Speed_1': 1476, 'Duration_t1': 13, 'Spin_Speed_2': 2036, 'Dispense_Speed': 242, 'Duration_t3': 25, 'Spin_Speed_3': 5000} and raw data {'Pmax': np.float64(20.529), 'Jsc': np.float64(25.533), 'Voc': np.float64(1.07), 'FF': np.float64(0.7514)}
[INFO 08-13 17:18:26] ax.api.client: Trial 39 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 39 with parameters {'Spin_Speed_1': 1096, 'Duration_t1': 20, 'Spin_Speed_2': 2295, 'Dispense_Speed': 247, 'Duration_t3': 17, 'Spin_Speed_3': 2231} and raw data {'Pmax': np.float64(19.226), 'Jsc': np.float64(24.83), 'Voc': np.float64(1.0592), 'FF': np.float64(0.731)}
[INFO 08-13 17:18:26] ax.api.client: Trial 40 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 40 with parameters {'Spin_Speed_1': 2126, 'Duration_t1': 21, 'Spin_Speed_2': 2903, 'Dispense_Speed': 266, 'Duration_t3': 9, 'Spin_Speed_3': 5000} and raw data {'Pmax': np.float64(21.355), 'Jsc': np.float64(25.093), 'Voc': np.float64(1.1027), 'FF': np.float64(0.7718)}
[INFO 08-13 17:18:26] ax.api.client: Trial 41 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 41 with parameters {'Spin_Speed_1': 1600, 'Duration_t1': 18, 'Spin_Speed_2': 2444, 'Dispense_Speed': 187, 'Duration_t3': 32, 'Spin_Speed_3': 4233} and raw data {'Pmax': np.float64(20.017), 'Jsc': np.float64(24.613), 'Voc': np.float64(1.1195), 'FF': np.float64(0.7264)}
[INFO 08-13 17:18:26] ax.api.client: Trial 42 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 42 with parameters {'Spin_Speed_1': 1184, 'Duration_t1': 29, 'Spin_Speed_2': 1946, 'Dispense_Speed': 191, 'Duration_t3': 35, 'Spin_Speed_3': 4204} and raw data {'Pmax': np.float64(17.623), 'Jsc': np.float64(24.369), 'Voc': np.float64(1.0772), 'FF': np.float64(0.6713)}
[INFO 08-13 17:18:26] ax.api.client: Trial 43 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 43 with parameters {'Spin_Speed_1': 1210, 'Duration_t1': 18, 'Spin_Speed_2': 1933, 'Dispense_Speed': 325, 'Duration_t3': 21, 'Spin_Speed_3': 2000} and raw data {'Pmax': np.float64(18.409), 'Jsc': np.float64(23.966), 'Voc': np.float64(1.1008), 'FF': np.float64(0.6978)}
[INFO 08-13 17:18:26] ax.api.client: Trial 44 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 44 with parameters {'Spin_Speed_1': 1220, 'Duration_t1': 15, 'Spin_Speed_2': 2546, 'Dispense_Speed': 198, 'Duration_t3': 11, 'Spin_Speed_3': 2783} and raw data {'Pmax': np.float64(18.206), 'Jsc': np.float64(23.437), 'Voc': np.float64(1.0966), 'FF': np.float64(0.7084)}
[INFO 08-13 17:18:26] ax.api.client: Trial 45 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 45 with parameters {'Spin_Speed_1': 1570, 'Duration_t1': 5, 'Spin_Speed_2': 2329, 'Dispense_Speed': 211, 'Duration_t3': 19, 'Spin_Speed_3': 4466} and raw data {'Pmax': np.float64(20.256), 'Jsc': np.float64(24.425), 'Voc': np.float64(1.1057), 'FF': np.float64(0.75)}
[INFO 08-13 17:18:26] ax.api.client: Trial 46 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 46 with parameters {'Spin_Speed_1': 925, 'Duration_t1': 22, 'Spin_Speed_2': 2808, 'Dispense_Speed': 268, 'Duration_t3': 5, 'Spin_Speed_3': 2000} and raw data {'Pmax': np.float64(16.54), 'Jsc': np.float64(24.617), 'Voc': np.float64(1.0551), 'FF': np.float64(0.6368)}
[INFO 08-13 17:18:26] ax.api.client: Trial 47 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 47 with parameters {'Spin_Speed_1': 1631, 'Duration_t1': 22, 'Spin_Speed_2': 2902, 'Dispense_Speed': 281, 'Duration_t3': 5, 'Spin_Speed_3': 3450} and raw data {'Pmax': np.float64(19.7), 'Jsc': np.float64(23.938), 'Voc': np.float64(1.1056), 'FF': np.float64(0.7443)}
[INFO 08-13 17:18:26] ax.api.client: Trial 48 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 48 with parameters {'Spin_Speed_1': 1720, 'Duration_t1': 5, 'Spin_Speed_2': 2707, 'Dispense_Speed': 260, 'Duration_t3': 10, 'Spin_Speed_3': 4488} and raw data {'Pmax': np.float64(16.123), 'Jsc': np.float64(24.05), 'Voc': np.float64(0.9692), 'FF': np.float64(0.6917)}
[INFO 08-13 17:18:26] ax.api.client: Trial 49 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 49 with parameters {'Spin_Speed_1': 1451, 'Duration_t1': 9, 'Spin_Speed_2': 2149, 'Dispense_Speed': 256, 'Duration_t3': 27, 'Spin_Speed_3': 3416} and raw data {'Pmax': np.float64(19.168), 'Jsc': np.float64(23.916), 'Voc': np.float64(1.1053), 'FF': np.float64(0.7251)}
[INFO 08-13 17:18:26] ax.api.client: Trial 50 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 50 with parameters {'Spin_Speed_1': 2358, 'Duration_t1': 19, 'Spin_Speed_2': 2981, 'Dispense_Speed': 298, 'Duration_t3': 13, 'Spin_Speed_3': 3658} and raw data {'Pmax': np.float64(22.046), 'Jsc': np.float64(24.857), 'Voc': np.float64(1.1243), 'FF': np.float64(0.7888)}
[INFO 08-13 17:18:26] ax.api.client: Trial 51 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 51 with parameters {'Spin_Speed_1': 1490, 'Duration_t1': 19, 'Spin_Speed_2': 2611, 'Dispense_Speed': 270, 'Duration_t3': 5, 'Spin_Speed_3': 4888} and raw data {'Pmax': np.float64(20.158), 'Jsc': np.float64(24.51), 'Voc': np.float64(1.084), 'FF': np.float64(0.7587)}
[INFO 08-13 17:18:26] ax.api.client: Trial 52 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 52 with parameters {'Spin_Speed_1': 1790, 'Duration_t1': 20, 'Spin_Speed_2': 2696, 'Dispense_Speed': 227, 'Duration_t3': 5, 'Spin_Speed_3': 5000} and raw data {'Pmax': np.float64(20.336), 'Jsc': np.float64(23.6), 'Voc': np.float64(1.112), 'FF': np.float64(0.7749)}
[INFO 08-13 17:18:26] ax.api.client: Trial 53 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 53 with parameters {'Spin_Speed_1': 1120, 'Duration_t1': 18, 'Spin_Speed_2': 2238, 'Dispense_Speed': 273, 'Duration_t3': 31, 'Spin_Speed_3': 3881} and raw data {'Pmax': np.float64(17.739), 'Jsc': np.float64(23.336), 'Voc': np.float64(1.0387), 'FF': np.float64(0.7318)}
[INFO 08-13 17:18:26] ax.api.client: Trial 54 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 54 with parameters {'Spin_Speed_1': 1509, 'Duration_t1': 18, 'Spin_Speed_2': 2552, 'Dispense_Speed': 254, 'Duration_t3': 17, 'Spin_Speed_3': 4265} and raw data {'Pmax': np.float64(20.647), 'Jsc': np.float64(24.658), 'Voc': np.float64(1.1007), 'FF': np.float64(0.7607)}
[INFO 08-13 17:18:26] ax.api.client: Trial 55 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 55 with parameters {'Spin_Speed_1': 1317, 'Duration_t1': 18, 'Spin_Speed_2': 2336, 'Dispense_Speed': 262, 'Duration_t3': 23, 'Spin_Speed_3': 4092} and raw data {'Pmax': np.float64(19.101), 'Jsc': np.float64(24.566), 'Voc': np.float64(1.0401), 'FF': np.float64(0.7475)}
[INFO 08-13 17:18:26] ax.api.client: Trial 56 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 56 with parameters {'Spin_Speed_1': 1671, 'Duration_t1': 16, 'Spin_Speed_2': 2613, 'Dispense_Speed': 271, 'Duration_t3': 17, 'Spin_Speed_3': 3640} and raw data {'Pmax': np.float64(20.922), 'Jsc': np.float64(24.468), 'Voc': np.float64(1.1097), 'FF': np.float64(0.7705)}
[INFO 08-13 17:18:26] ax.api.client: Trial 57 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 57 with parameters {'Spin_Speed_1': 1596, 'Duration_t1': 19, 'Spin_Speed_2': 2668, 'Dispense_Speed': 237, 'Duration_t3': 12, 'Spin_Speed_3': 4999} and raw data {'Pmax': np.float64(19.808), 'Jsc': np.float64(24.306), 'Voc': np.float64(1.088), 'FF': np.float64(0.749)}
[INFO 08-13 17:18:26] ax.api.client: Trial 58 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 58 with parameters {'Spin_Speed_1': 1428, 'Duration_t1': 15, 'Spin_Speed_2': 2488, 'Dispense_Speed': 292, 'Duration_t3': 13, 'Spin_Speed_3': 4281} and raw data {'Pmax': np.float64(17.798), 'Jsc': np.float64(23.984), 'Voc': np.float64(1.032), 'FF': np.float64(0.7191)}
[INFO 08-13 17:18:26] ax.api.client: Trial 59 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 59 with parameters {'Spin_Speed_1': 1513, 'Duration_t1': 15, 'Spin_Speed_2': 2388, 'Dispense_Speed': 198, 'Duration_t3': 18, 'Spin_Speed_3': 4580} and raw data {'Pmax': np.float64(19.671), 'Jsc': np.float64(24.109), 'Voc': np.float64(1.0785), 'FF': np.float64(0.7565)}
[INFO 08-13 17:18:26] ax.api.client: Trial 60 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 60 with parameters {'Spin_Speed_1': 2134, 'Duration_t1': 20, 'Spin_Speed_2': 2989, 'Dispense_Speed': 238, 'Duration_t3': 18, 'Spin_Speed_3': 4014} and raw data {'Pmax': np.float64(21.242), 'Jsc': np.float64(24.934), 'Voc': np.float64(1.121), 'FF': np.float64(0.76)}
[INFO 08-13 17:18:26] ax.api.client: Trial 61 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 61 with parameters {'Spin_Speed_1': 1568, 'Duration_t1': 20, 'Spin_Speed_2': 2755, 'Dispense_Speed': 238, 'Duration_t3': 17, 'Spin_Speed_3': 4749} and raw data {'Pmax': np.float64(18.827), 'Jsc': np.float64(24.086), 'Voc': np.float64(1.0857), 'FF': np.float64(0.72)}
[INFO 08-13 17:18:26] ax.api.client: Trial 62 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 62 with parameters {'Spin_Speed_1': 1647, 'Duration_t1': 14, 'Spin_Speed_2': 2413, 'Dispense_Speed': 293, 'Duration_t3': 25, 'Spin_Speed_3': 4326} and raw data {'Pmax': np.float64(20.698), 'Jsc': np.float64(25.671), 'Voc': np.float64(1.1022), 'FF': np.float64(0.7315)}
[INFO 08-13 17:18:26] ax.api.client: Trial 63 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 63 with parameters {'Spin_Speed_1': 1403, 'Duration_t1': 18, 'Spin_Speed_2': 2463, 'Dispense_Speed': 256, 'Duration_t3': 25, 'Spin_Speed_3': 3426} and raw data {'Pmax': np.float64(17.674), 'Jsc': np.float64(24.424), 'Voc': np.float64(1.0295), 'FF': np.float64(0.7029)}
[INFO 08-13 17:18:26] ax.api.client: Trial 64 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 64 with parameters {'Spin_Speed_1': 1303, 'Duration_t1': 17, 'Spin_Speed_2': 2400, 'Dispense_Speed': 246, 'Duration_t3': 17, 'Spin_Speed_3': 4827} and raw data {'Pmax': np.float64(17.823), 'Jsc': np.float64(24.306), 'Voc': np.float64(1.0286), 'FF': np.float64(0.7129)}
[INFO 08-13 17:18:26] ax.api.client: Trial 65 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 65 with parameters {'Spin_Speed_1': 1677, 'Duration_t1': 19, 'Spin_Speed_2': 2765, 'Dispense_Speed': 267, 'Duration_t3': 16, 'Spin_Speed_3': 4181} and raw data {'Pmax': np.float64(17.82), 'Jsc': np.float64(24.573), 'Voc': np.float64(1.0604), 'FF': np.float64(0.6839)}
[INFO 08-13 17:18:26] ax.api.client: Trial 66 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 66 with parameters {'Spin_Speed_1': 1553, 'Duration_t1': 13, 'Spin_Speed_2': 2396, 'Dispense_Speed': 273, 'Duration_t3': 16, 'Spin_Speed_3': 3785} and raw data {'Pmax': np.float64(21.257), 'Jsc': np.float64(25.938), 'Voc': np.float64(1.0426), 'FF': np.float64(0.7023)}
[INFO 08-13 17:18:26] ax.api.client: Trial 67 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 67 with parameters {'Spin_Speed_1': 2579, 'Duration_t1': 21, 'Spin_Speed_2': 3000, 'Dispense_Speed': 228, 'Duration_t3': 13, 'Spin_Speed_3': 5000} and raw data {'Pmax': np.float64(19.282), 'Jsc': np.float64(25.413), 'Voc': np.float64(1.0169), 'FF': np.float64(0.7461)}
[INFO 08-13 17:18:26] ax.api.client: Trial 68 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 68 with parameters {'Spin_Speed_1': 1515, 'Duration_t1': 16, 'Spin_Speed_2': 2676, 'Dispense_Speed': 269, 'Duration_t3': 6, 'Spin_Speed_3': 3552} and raw data {'Pmax': np.float64(23.285), 'Jsc': np.float64(25.744), 'Voc': np.float64(1.1467), 'FF': np.float64(0.7887)}
[INFO 08-13 17:18:26] ax.api.client: Trial 69 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 69 with parameters {'Spin_Speed_1': 1165, 'Duration_t1': 23, 'Spin_Speed_2': 2063, 'Dispense_Speed': 241, 'Duration_t3': 32, 'Spin_Speed_3': 2863} and raw data {'Pmax': np.float64(23.729), 'Jsc': np.float64(25.791), 'Voc': np.float64(1.1622), 'FF': np.float64(0.7916)}
[INFO 08-13 17:18:26] ax.api.client: Trial 70 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 70 with parameters {'Spin_Speed_1': 1913, 'Duration_t1': 13, 'Spin_Speed_2': 2671, 'Dispense_Speed': 222, 'Duration_t3': 22, 'Spin_Speed_3': 5000} and raw data {'Pmax': np.float64(19.358), 'Jsc': np.float64(25.335), 'Voc': np.float64(1.0285), 'FF': np.float64(0.7429)}
[INFO 08-13 17:18:26] ax.api.client: Trial 71 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 71 with parameters {'Spin_Speed_1': 1421, 'Duration_t1': 8, 'Spin_Speed_2': 2021, 'Dispense_Speed': 279, 'Duration_t3': 25, 'Spin_Speed_3': 3002} and raw data {'Pmax': np.float64(21.494), 'Jsc': np.float64(25.441), 'Voc': np.float64(1.1046), 'FF': np.float64(0.7649)}
[INFO 08-13 17:18:26] ax.api.client: Trial 72 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 72 with parameters {'Spin_Speed_1': 1840, 'Duration_t1': 5, 'Spin_Speed_2': 2401, 'Dispense_Speed': 360, 'Duration_t3': 10, 'Spin_Speed_3': 3825} and raw data {'Pmax': np.float64(20.553), 'Jsc': np.float64(25.85), 'Voc': np.float64(1.0397), 'FF': np.float64(0.7647)}
[INFO 08-13 17:18:26] ax.api.client: Trial 73 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 73 with parameters {'Spin_Speed_1': 1476, 'Duration_t1': 13, 'Spin_Speed_2': 2036, 'Dispense_Speed': 242, 'Duration_t3': 25, 'Spin_Speed_3': 5000} and raw data {'Pmax': np.float64(20.888), 'Jsc': np.float64(25.261), 'Voc': np.float64(1.0881), 'FF': np.float64(0.7599)}
[INFO 08-13 17:18:26] ax.api.client: Trial 74 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 74 with parameters {'Spin_Speed_1': 1096, 'Duration_t1': 20, 'Spin_Speed_2': 2295, 'Dispense_Speed': 247, 'Duration_t3': 17, 'Spin_Speed_3': 2231} and raw data {'Pmax': np.float64(23.128), 'Jsc': np.float64(25.638), 'Voc': np.float64(1.1548), 'FF': np.float64(0.7812)}
[INFO 08-13 17:18:26] ax.api.client: Trial 75 marked COMPLETED.
[INFO 08-13 17:18:26] optimpv.axBOtorchOptimizer: Attached trial 75 with parameters {'Spin_Speed_1': 925, 'Duration_t1': 22, 'Spin_Speed_2': 2808, 'Dispense_Speed': 268, 'Duration_t3': 5, 'Spin_Speed_3': 2000} and raw data {'Pmax': np.float64(23.055), 'Jsc': np.float64(25.874), 'Voc': np.float64(1.1494), 'FF': np.float64(0.7752)}
[INFO 08-13 17:18:37] ax.api.client: Generated new trial 76 with parameters {'Spin_Speed_1': 2271, 'Duration_t1': 18, 'Spin_Speed_2': 3000, 'Dispense_Speed': 341, 'Duration_t3': 5, 'Spin_Speed_3': 2000}using GenerationNode BOTORCH_MODULAR.
[INFO 08-13 17:18:37] ax.api.client: Generated new trial 77 with parameters {'Spin_Speed_1': 2776, 'Duration_t1': 14, 'Spin_Speed_2': 3000, 'Dispense_Speed': 400, 'Duration_t3': 21, 'Spin_Speed_3': 5000}using GenerationNode BOTORCH_MODULAR.
[INFO 08-13 17:18:37] ax.api.client: Generated new trial 78 with parameters {'Spin_Speed_1': 2022, 'Duration_t1': 20, 'Spin_Speed_2': 3000, 'Dispense_Speed': 250, 'Duration_t3': 5, 'Spin_Speed_3': 2000}using GenerationNode BOTORCH_MODULAR.
[INFO 08-13 17:18:37] ax.api.client: Generated new trial 79 with parameters {'Spin_Speed_1': 2972, 'Duration_t1': 34, 'Spin_Speed_2': 3000, 'Dispense_Speed': 384, 'Duration_t3': 5, 'Spin_Speed_3': 4285}using GenerationNode BOTORCH_MODULAR.
[INFO 08-13 17:18:37] ax.api.client: Generated new trial 80 with parameters {'Spin_Speed_1': 1311, 'Duration_t1': 16, 'Spin_Speed_2': 2213, 'Dispense_Speed': 219, 'Duration_t3': 35, 'Spin_Speed_3': 2000}using GenerationNode BOTORCH_MODULAR.
[INFO 08-13 17:18:37] ax.api.client: Generated new trial 81 with parameters {'Spin_Speed_1': 2722, 'Duration_t1': 15, 'Spin_Speed_2': 3000, 'Dispense_Speed': 350, 'Duration_t3': 35, 'Spin_Speed_3': 2000}using GenerationNode BOTORCH_MODULAR.
[INFO 08-13 17:18:37] optimpv.axBOtorchOptimizer: Suggesting 6 trials without running the agents.

[7]:

# 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
suggest.params = optimizer.params # update the params list in the agent with the best parameters

print("Best parameters found:")
for p in optimizer.params:
    print(f"{p.name}: {p.value} {p.unit} ")

Best parameters found:
Spin_Speed_1: 1165 rpm
Duration_t1: 23 s
Spin_Speed_2: 2063 rpm
Dispense_Speed: 241 rpm
Duration_t3: 32 s
Spin_Speed_3: 2863 rpm

[8]:

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

print("Best value seen so far is ", max(data[all_metrics[0]]), "at iteration ", int(data[all_metrics[0]].idxmin()))

plt.show()

Best value seen so far is  23.729 at iteration  27
../_images/examples_Exp_design_11_1.png 
[9]:

# print the parameters to that are running in data
to_run_next = data[data['trial_status'] == 'RUNNING']
print("Parameters to run next:")
print(to_run_next)

Parameters to run next:
    trial_index arm_name trial_status  generation_node  Jsc  Voc  FF  Pmax  \
76           76     76_0      RUNNING  BOTORCH_MODULAR  NaN  NaN NaN   NaN
77           77     77_0      RUNNING  BOTORCH_MODULAR  NaN  NaN NaN   NaN
78           78     78_0      RUNNING  BOTORCH_MODULAR  NaN  NaN NaN   NaN
79           79     79_0      RUNNING  BOTORCH_MODULAR  NaN  NaN NaN   NaN
80           80     80_0      RUNNING  BOTORCH_MODULAR  NaN  NaN NaN   NaN
81           81     81_0      RUNNING  BOTORCH_MODULAR  NaN  NaN NaN   NaN

    Spin_Speed_1  Duration_t1  Spin_Speed_2  Dispense_Speed  Duration_t3  \
76          2271           18          3000             341            5
77          2776           14          3000             400           21
78          2022           20          3000             250            5
79          2972           34          3000             384            5
80          1311           16          2213             219           35
81          2722           15          3000             350           35

    Spin_Speed_3
76          2000
77          5000
78          2000
79          4285
80          2000
81          2000

[10]:

cards = ax_client.compute_analyses(display=True)
Overview of the Entire Optimization Process

This analysis provides an overview of the entire optimization process. It includes visualizations of the results obtained so far, insights into the parameter and metric relationships learned by the Ax model, diagnostics such as model fit, and health checks to assess the overall health of the experiment.

Results Analysis

Result Analyses provide a high-level overview of the results of the optimization process so far with respect to the metrics specified in experiment design.

Metric Effects: Predicted and observed effects for all arms in the experiment

These pair of plots visualize the metric effects for each arm, with the Ax model predictions on the left and the raw observed data on the right. The predicted effects apply shrinkage for noise and adjust for non-stationarity in the data, so they are more representative of the reproducible effects that will manifest in a long-term validation experiment.

Metric Effects Pair for Pmax

Modeled Arm Effects on Pmax

Modeled effects on Pmax. This plot visualizes predictions of the true metric changes for each arm based on Ax's model. This is the expected delta you would expect if you (re-)ran that arm. This plot helps in anticipating the outcomes and performance of arms based on the model's predictions. Note, flat predictions across arms indicate that the model predicts that there is no effect, meaning if you were to re-run the experiment, the delta you would see would be small and fall within the confidence interval indicated in the plot.

Observed Arm Effects on Pmax

Observed effects on Pmax. This plot visualizes the effects from previously-run arms on a specific metric, providing insights into their performance. This plot allows one to compare and contrast the effectiveness of different arms, highlighting which configurations have yielded the most favorable outcomes.

Summary for ax_opti

High-level summary of the `Trial`-s in this `Experiment`

trial_index arm_name trial_status generation_node Jsc Voc FF Pmax Spin_Speed_1 Duration_t1 Spin_Speed_2 Dispense_Speed Duration_t3 Spin_Speed_3
0 0 0_0 COMPLETED None 24.507 0.9100 0.6201 13.830 2404 15 1745 203 13 4494
1 1 1_0 COMPLETED None 25.733 1.0090 0.7592 19.712 1835 17 2289 289 11 4389
2 2 2_0 COMPLETED None 24.692 1.0321 0.7376 18.797 1067 33 2352 235 5 2861
3 3 3_0 COMPLETED None 24.195 0.8197 0.6506 12.903 2276 18 1821 26 15 3241
4 4 4_0 COMPLETED None 24.268 0.9951 0.7684 18.556 2147 30 2816 290 29 4981
5 5 5_0 COMPLETED None 24.481 1.0100 0.7264 17.962 1152 28 2734 122 8 4856
6 6 6_0 COMPLETED None 25.575 0.9926 0.7377 18.726 1240 23 2993 190 19 2560
7 7 7_0 COMPLETED None 23.995 0.6850 0.6336 10.414 2471 21 1394 138 29 2046
8 8 8_0 COMPLETED None 24.283 0.9940 0.7185 17.342 907 9 2766 314 22 4323
9 9 9_0 COMPLETED None 12.231 0.8321 0.5819 5.922 2034 18 1021 90 7 4776
10 10 10_0 COMPLETED None 23.558 0.9297 0.6919 15.154 540 19 2383 16 23 4632
11 11 11_0 COMPLETED None 25.214 0.9921 0.7637 19.105 1532 12 1955 383 18 4137
12 12 12_0 COMPLETED None 24.944 1.0080 0.7018 17.644 1024 8 2631 367 21 2135
13 13 13_0 COMPLETED None 24.301 1.0172 0.7420 18.340 1768 22 2927 77 10 4208
14 14 14_0 COMPLETED None 23.929 0.9692 0.6905 16.013 1598 29 2592 396 23 3688
15 15 15_0 COMPLETED None 24.251 0.9949 0.6952 16.773 691 14 1661 248 19 3523
16 16 16_0 COMPLETED None 20.114 0.6694 0.5930 7.984 1745 28 1098 108 16 2467
17 17 17_0 COMPLETED None 25.454 0.9170 0.7359 17.178 2111 31 2065 349 27 2251
18 18 18_0 COMPLETED None 25.549 0.9484 0.7094 17.190 621 26 1221 51 26 3907
19 19 19_0 COMPLETED None 24.824 0.9231 0.8109 18.583 1442 9 1297 306 9 3972
20 20 20_0 COMPLETED None 24.913 0.8165 0.7027 14.294 810 33 1776 258 6 4599
21 21 21_0 COMPLETED None 24.870 0.9743 0.7497 18.165 1308 32 2558 146 12 3111
22 22 22_0 COMPLETED None 25.625 1.0253 0.7546 19.824 1333 20 1878 212 24 3386
23 23 23_0 COMPLETED None 25.641 1.0022 0.7049 18.115 863 34 1535 104 25 3160
24 24 24_0 COMPLETED None 24.788 0.9533 0.7081 16.732 675 26 1604 66 21 2720
25 25 25_0 COMPLETED None 24.213 1.0209 0.7637 18.876 1420 12 2015 277 7 3621
26 26 26_0 COMPLETED None 24.022 0.9163 0.7127 15.688 1973 24 2137 218 16 2822
27 27 27_0 COMPLETED None 16.955 0.6163 0.4488 4.690 2349 15 1315 35 18 2282
28 28 28_0 COMPLETED None 24.370 0.9586 0.7501 17.522 2211 10 2465 156 13 2585
29 29 29_0 COMPLETED None 24.711 1.0388 0.7582 19.463 1632 20 2233 328 14 3025
30 30 30_0 COMPLETED None 23.312 0.9011 0.6478 13.608 1881 25 1449 360 10 3843
31 31 31_0 COMPLETED None 23.852 0.9480 0.7195 16.270 953 13 1167 177 27 3463
32 32 32_0 COMPLETED None 25.429 1.1523 0.7840 22.973 1474 14 2324 250 19 3910
33 33 33_0 COMPLETED None 24.912 1.1458 0.7811 22.298 1515 16 2676 269 6 3552
34 34 34_0 COMPLETED None 25.132 1.1228 0.7614 21.484 1165 23 2063 241 32 2863
35 35 35_0 COMPLETED None 25.680 1.0820 0.7335 20.381 1913 13 2671 222 22 5000
36 36 36_0 COMPLETED None 24.773 1.0860 0.7178 19.312 1421 8 2021 279 25 3002
37 37 37_0 COMPLETED None 24.990 1.0630 0.7175 19.060 1840 5 2401 360 10 3825
38 38 38_0 COMPLETED None 25.533 1.0700 0.7514 20.529 1476 13 2036 242 25 5000
39 39 39_0 COMPLETED None 24.830 1.0592 0.7310 19.226 1096 20 2295 247 17 2231
40 40 40_0 COMPLETED None 25.093 1.1027 0.7718 21.355 2126 21 2903 266 9 5000
41 41 41_0 COMPLETED None 24.613 1.1195 0.7264 20.017 1600 18 2444 187 32 4233
42 42 42_0 COMPLETED None 24.369 1.0772 0.6713 17.623 1184 29 1946 191 35 4204
43 43 43_0 COMPLETED None 23.966 1.1008 0.6978 18.409 1210 18 1933 325 21 2000
44 44 44_0 COMPLETED None 23.437 1.0966 0.7084 18.206 1220 15 2546 198 11 2783
45 45 45_0 COMPLETED None 24.425 1.1057 0.7500 20.256 1570 5 2329 211 19 4466
46 46 46_0 COMPLETED None 24.617 1.0551 0.6368 16.540 925 22 2808 268 5 2000
47 47 47_0 COMPLETED None 23.938 1.1056 0.7443 19.700 1631 22 2902 281 5 3450
48 48 48_0 COMPLETED None 24.050 0.9692 0.6917 16.123 1720 5 2707 260 10 4488
49 49 49_0 COMPLETED None 23.916 1.1053 0.7251 19.168 1451 9 2149 256 27 3416
50 50 50_0 COMPLETED None 24.857 1.1243 0.7888 22.046 2358 19 2981 298 13 3658
51 51 51_0 COMPLETED None 24.510 1.0840 0.7587 20.158 1490 19 2611 270 5 4888
52 52 52_0 COMPLETED None 23.600 1.1120 0.7749 20.336 1790 20 2696 227 5 5000
53 53 53_0 COMPLETED None 23.336 1.0387 0.7318 17.739 1120 18 2238 273 31 3881
54 54 54_0 COMPLETED None 24.658 1.1007 0.7607 20.647 1509 18 2552 254 17 4265
55 55 55_0 COMPLETED None 24.566 1.0401 0.7475 19.101 1317 18 2336 262 23 4092
56 56 56_0 COMPLETED None 24.468 1.1097 0.7705 20.922 1671 16 2613 271 17 3640
57 57 57_0 COMPLETED None 24.306 1.0880 0.7490 19.808 1596 19 2668 237 12 4999
58 58 58_0 COMPLETED None 23.984 1.0320 0.7191 17.798 1428 15 2488 292 13 4281
59 59 59_0 COMPLETED None 24.109 1.0785 0.7565 19.671 1513 15 2388 198 18 4580
60 60 60_0 COMPLETED None 24.934 1.1210 0.7600 21.242 2134 20 2989 238 18 4014
61 61 61_0 COMPLETED None 24.086 1.0857 0.7200 18.827 1568 20 2755 238 17 4749
62 62 62_0 COMPLETED None 25.671 1.1022 0.7315 20.698 1647 14 2413 293 25 4326
63 63 63_0 COMPLETED None 24.424 1.0295 0.7029 17.674 1403 18 2463 256 25 3426
64 64 64_0 COMPLETED None 24.306 1.0286 0.7129 17.823 1303 17 2400 246 17 4827
65 65 65_0 COMPLETED None 24.573 1.0604 0.6839 17.820 1677 19 2765 267 16 4181
66 66 66_0 COMPLETED None 25.938 1.0426 0.7023 21.257 1553 13 2396 273 16 3785
67 67 67_0 COMPLETED None 25.413 1.0169 0.7461 19.282 2579 21 3000 228 13 5000
68 68 33_0 COMPLETED None 25.744 1.1467 0.7887 23.285 1515 16 2676 269 6 3552
69 69 34_0 COMPLETED None 25.791 1.1622 0.7916 23.729 1165 23 2063 241 32 2863
70 70 35_0 COMPLETED None 25.335 1.0285 0.7429 19.358 1913 13 2671 222 22 5000
71 71 36_0 COMPLETED None 25.441 1.1046 0.7649 21.494 1421 8 2021 279 25 3002
72 72 37_0 COMPLETED None 25.850 1.0397 0.7647 20.553 1840 5 2401 360 10 3825
73 73 38_0 COMPLETED None 25.261 1.0881 0.7599 20.888 1476 13 2036 242 25 5000
74 74 39_0 COMPLETED None 25.638 1.1548 0.7812 23.128 1096 20 2295 247 17 2231
75 75 46_0 COMPLETED None 25.874 1.1494 0.7752 23.055 925 22 2808 268 5 2000
76 76 76_0 RUNNING BOTORCH_MODULAR NaN NaN NaN NaN 2271 18 3000 341 5 2000
77 77 77_0 RUNNING BOTORCH_MODULAR NaN NaN NaN NaN 2776 14 3000 400 21 5000
78 78 78_0 RUNNING BOTORCH_MODULAR NaN NaN NaN NaN 2022 20 3000 250 5 2000
79 79 79_0 RUNNING BOTORCH_MODULAR NaN NaN NaN NaN 2972 34 3000 384 5 4285
80 80 80_0 RUNNING BOTORCH_MODULAR NaN NaN NaN NaN 1311 16 2213 219 35 2000
81 81 81_0 RUNNING BOTORCH_MODULAR NaN NaN NaN NaN 2722 15 3000 350 35 2000
Insights Analysis

Insight Analyses display information to help understand the underlying experiment i.e parameter and metric relationships learned by the Ax model.Use this information to better understand your experiment space and users.

Top Surfaces Analysis: Parameter sensitivity, slice, and contour plots

The top surfaces analysis displays three analyses in one. First, it shows parameter sensitivities, which shows the sensitivity of the metrics in the experiment to the most important parameters. Subsetting to only the most important parameters, it then shows slice plots and contour plots for each metric in the experiment, displaying the relationship between the metric and the most important parameters.

Sensitivity Analysis for Pmax

Understand how each parameter affects Pmax according to a second-order sensitivity analysis.

Slice Plots: Metric effects by parameter value

These plots show the relationship between a metric and a parameter. They show the predicted values of the metric on the y-axis as a function of the parameter on the x-axis while keeping all other parameters fixed at their status_quo value (or mean value if status_quo is unavailable).

Spin_Speed_2 vs. Pmax

The slice plot provides a one-dimensional view of predicted outcomes for Pmax as a function of a single parameter, while keeping all other parameters fixed at their status_quo value (or mean value if status_quo is unavailable). This visualization helps in understanding the sensitivity and impact of changes in the selected parameter on the predicted metric outcomes.

Spin_Speed_1 vs. Pmax

The slice plot provides a one-dimensional view of predicted outcomes for Pmax as a function of a single parameter, while keeping all other parameters fixed at their status_quo value (or mean value if status_quo is unavailable). This visualization helps in understanding the sensitivity and impact of changes in the selected parameter on the predicted metric outcomes.

Contour Plots: Metric effects by parameter values

These plots show the relationship between a metric and two parameters. They show the predicted values of the metric (indicated by color) as a function of the two parameters on the x- and y-axes while keeping all other parameters fixed at their status_quo value (or mean value if status_quo is unavailable).

Spin_Speed_1, Spin_Speed_2 vs. Pmax

The contour plot visualizes the predicted outcomes for Pmax across a two-dimensional parameter space, with other parameters held fixed at their status_quo value (or mean value if status_quo is unavailable). This plot helps in identifying regions of optimal performance and understanding how changes in the selected parameters influence the predicted outcomes. Contour lines represent levels of constant predicted values, providing insights into the gradient and potential optima within the parameter space.

Diagnostic Analysis

Diagnostic Analyses provide information about the optimization process and the quality of the model fit. You can use this information to understand if the experimental design should be adjusted to improve optimization quality.

Cross Validation for Pmax

The cross-validation plot displays the model fit for each metric in the experiment. It employs a leave-one-out approach, where the model is trained on all data except one sample, which is used for validation. The plot shows the predicted outcome for the validation set on the y-axis against its actual value on the x-axis. Points that align closely with the dotted diagonal line indicate a strong model fit, signifying accurate predictions. Additionally, the plot includes 95% confidence intervals that provide insight into the noise in observations and the uncertainty in model predictions. A horizontal, flat line of predictions indicates that the model has not picked up on sufficient signal in the data, and instead is just predicting the mean.

[11]:

from ax.utils.notebook.plotting import render# for plotting in notebook
from ax.plot.slice import plot_slice
from ax.plot.scatter import interact_fitted
from ax.adapter.cross_validation import cross_validate
from ax.plot.diagnostic import interact_cross_validation
from sklearn.metrics import r2_score

model = optimizer.ax_client._generation_strategy.adapter  # get the model from the ax client
cv_results = cross_validate(model)

# calculate the r2
observed, predicted = [], []
for i in range(len(cv_results)):
    observed.append(cv_results[i].observed.data.means[0])
    predicted.append(cv_results[i].predicted.means[0])
r2 = r2_score(observed, predicted)
print(f'R2 = {r2}')
render(interact_cross_validation(cv_results))

obj_type = 'Pmax' # objective type
p_idx = 0
for p_idx in range(len(optimizer.params)):
    x = ax.plot.slice.plot_slice(model=model,param_name= optimizer.params[p_idx].name, metric_name= obj_type).data['data'][1]['x']
    y = ax.plot.slice.plot_slice(model=model,param_name=optimizer.params[p_idx].name, metric_name= obj_type).data['data'][1]['y']
    x1 = ax.plot.slice.plot_slice(model=model,param_name= optimizer.params[p_idx].name, metric_name= obj_type).data['data'][0]['x']
    y1 = ax.plot.slice.plot_slice(model=model,param_name= optimizer.params[p_idx].name, metric_name= obj_type).data['data'][0]['y']
    # cut x1 and y1 to the same length as x and y
    x_low = x1[:len(x)]
    y_low = y1[:len(y)]
    x_high = x1[len(x):]
    y_high = y1[len(y):][::-1]

    plt.figure(figsize=(8,6))
    plt.plot(x,y)
    # shade the area between the two curves
    plt.fill_between(x, y_low, y_high, color='C0', alpha=0.5)
    # plt.fill_between(x, y, y_high, color='gray', alpha=0.5)
    plt.plot(data[optimizer.params[p_idx].name],data[obj_type],'C0o')
    plt.xlabel(optimizer.params[p_idx].display_name)
    plt.ylabel(obj_type)
    plt.tight_layout()
    plt.show()


R2 = 0.5014779101341558
../_images/examples_Exp_design_14_2.png
../_images/examples_Exp_design_14_3.png
../_images/examples_Exp_design_14_4.png
../_images/examples_Exp_design_14_5.png
../_images/examples_Exp_design_14_6.png
../_images/examples_Exp_design_14_7.png 
[12]:

# 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.posterior import *
    params_orig_dict, 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')
../_images/examples_Exp_design_15_0.png