def test_define_material():
from solcore import ParameterSystem, MaterialSystem
this_dir = os.path.split(__file__)[0]
create_new_material(
"SiGeSn",
os.path.join(this_dir, "data", "SiGeSn_n.txt"),
os.path.join(this_dir, "data", "SiGeSn_k.txt"),
os.path.join(this_dir, "data", "SiGeSn_params.txt"),
)
assert "SiGeSn" in ParameterSystem().database.sections()
assert "SiGeSn".lower() in MaterialSystem().sources
SiGeSn = material("SiGeSn")()
assert SiGeSn.n(400e-9) == approx(4.175308391752484)
assert SiGeSn.k(400e-9) == approx(2.3037424963866306)
When adding custom materials - or getting the refractive index database - the
information will be stored in the Solcore's users folder. These can be setup by setting
the SOLCORE_USER_DATA environmental variable to your prefered location or, by default,
it will be in your home directory, in a directory called .solcore.
"""
"""
EXAMPLE 1
need to have n and k data, and a parameter file in the correct format -
see examples of parameter files in e.g. material_data/Adachi/binaries.txt
create a new material, silicon-germanium-tin, from input files. Here,
the parameters in SiGeSn_params.txt have been copied directly from Ge.
"""
create_new_material('SiGeSn', 'data/SiGeSn_n.txt', 'data/SiGeSn_k.txt',
'data/SiGeSn_params.txt')
# can now create an instance of it like any Solcore material
SiGeSn = material('SiGeSn')()
plt.figure()
plt.plot(
si(np.arange(300, 1700, 5), 'nm') * 1e9,
SiGeSn.n(si(np.arange(300, 1700, 5), 'nm')))
plt.plot(
si(np.arange(300, 1700, 5), 'nm') * 1e9,
SiGeSn.k(si(np.arange(300, 1700, 5), 'nm')))
plt.xlabel('Wavelength (nm)')
plt.ylabel('SiGeSn n / k')
# First, using a purely optical TMM simulation to calculate the photogenerated current in each sub-cell. The thing to
# optimize is then the current of the current-limiting cell in the structure; in other words we want to *maximize* the
# lowest sub-cell current, to achieve current-matching with the highest possible current.
# Since differential evolution does a minimization, we are actually minimizing the negative of
# this value.
# Once we have good initial values for the total layer thicknesses, use full electrical simulations to determine the
# n and p type layer thicknesses to calculate a maximum possible efficiency for the 4J device.
# To use yabox/the optimization module for the DE, we need to define a class which sets up the problem and has an 'evaluate' function, which
# will actually calculate the value we are trying to minimize for a set of parameters.
# add SiGeSn optical constants to the database
create_new_material(
'SiGeSn', 'SiGeSn_n.txt', 'SiGeSn_k.txt',
'SiGeSn_params.txt') # Note: comment out this line after the material
# has been added to avoid being asked if you want to overwrite it.
# define class for the optimization:
class calc_min_Jsc():
def __init__(self):
# initialize an instance of the class; set some information which will be used in each iteration of the calculation:
# materials, wavelengths, the light source
T = 298
wl = np.linspace(300, 1900, 800)
# materials
information will be stored in the Solcore's users folder. These can be setup by setting
the SOLCORE_USER_DATA environmental variable to your prefered location or, by default,
it will be in your home directory, in a directory called .solcore.
"""
"""
EXAMPLE 1
need to have n and k data, and a parameter file in the correct format -
see examples of parameter files in e.g. material_data/Adachi/binaries.txt
create a new material, silicon-germanium-tin, from input files. Here,
the parameters in SiGeSn_params.txt have been copied directly from Ge.
"""
create_new_material('SiGeSn', 'data/SiGeSn_n.txt', 'data/SiGeSn_k.txt', 'data/SiGeSn_params.txt')
# Note that the final argument, the parameter file, is optional - if you do not
# provide it, a material will be added with optical constants only, so it can be
# used for optical calculations.
# can now create an instance of it like any Solcore material
SiGeSn = material('SiGeSn')()
plt.figure()
plt.plot(si(np.arange(300, 1700, 5), 'nm')*1e9, SiGeSn.n(si(np.arange(300, 1700, 5), 'nm')))
plt.plot(si(np.arange(300, 1700, 5), 'nm')*1e9, SiGeSn.k(si(np.arange(300, 1700, 5), 'nm')))
plt.xlabel('Wavelength (nm)')
plt.ylabel('SiGeSn n / k')
# To set up the use of custom materials, first we need to tell Solcore some things about where to put custom materials. for this,
# we use the add_source function from config_tools, although we could also manually edit
# the solcore configuration file (which should be in your home directory).
# You need to add two things to the config file: where to put the n and k data for new
# materials added to the database, and where to put the other parameters (these can all
# go in the same file).
home_folder = os.path.expanduser('~')
custom_nk_path = os.path.join(home_folder, 'Solcore/custommats')
nk_db_path = os.path.join(home_folder, 'Solcore/NK.db')
param_path = os.path.join(home_folder, 'Solcore/custom_params.txt')
add_source('Others', 'custom_mats', custom_nk_path)
add_source('Others', 'nk', nk_db_path)
add_source('Parameters', 'custom', param_path)
import pandas as pd
names = ['GaAs_subs', 'InAlP', 'InGaP', 'SiGeSn_29111']
for name in names[:3]:
data = pd.read_csv('data/'+name+'.txt', sep='\t', names=['wl', 'n', 'k'])
data['wl'] = data['wl']*1e-9
np.savetxt('data/'+name+'_n.txt', data[['wl', 'n']])
np.savetxt('data/'+name+'_k.txt', data[['wl', 'k']])
sol_names = ['GaAs_WVASE', 'InAlP_WVASE', 'InGaP_WVASE', 'SiGeSn']
for i1, name in enumerate(sol_names):
create_new_material(name, 'data/'+names[i1]+'_n.txt', 'data/'+names[i1]+'_k.txt')