def test_kmc_algorithm(self):
np.random.seed(
0
) # set random seed in order for the examples to reproduce the exact references
transitions = [
Transition(
s1,
gs,
tdm=[0.5, 0.2], # a.u.
reorganization_energy=0.08, # eV
symmetric=True)
]
vibrational_model = MarcusModel(transitions=transitions,
temperature=300) # Kelvin
# list of transfer functions by state
self.system.process_scheme = [
GoldenRule(initial_states=(s1, gs),
final_states=(gs, s1),
electronic_coupling_function=forster_coupling_extended,
description='Forster',
arguments={
'ref_index': 2,
'longitude': 2,
'n_divisions': 30,
'transitions': transitions
},
vibrations=vibrational_model),
DecayRate(initial_state=s1,
final_state=gs,
decay_rate_function=einstein_radiative_decay,
arguments={'transitions': transitions},
description='singlet_radiative_decay')
]
self.system.cutoff_radius = 10.0 # interaction cutoff radius in Angstrom
# some system analyze functions
system_test_info(self.system)
trajectories = calculate_kmc(
self.system,
num_trajectories=10, # number of trajectories that will be simulated
max_steps=100, # maximum number of steps for trajectory allowed
silent=True)
# Results analysis
analysis = TrajectoryAnalysis(trajectories)
print('diffusion coefficient: {:9.5f} Angs^2/ns'.format(
analysis.diffusion_coefficient('s1')))
print('lifetime: {:9.5f} ns'.format(
analysis.lifetime('s1')))
print('diffusion length: {:9.5f} Angs'.format(
analysis.diffusion_length('s1')))
print('diffusion tensor (Angs^2/ns)')
print(analysis.diffusion_coeff_tensor('s1'))
print('diffusion length square tensor (Angs)')
print(analysis.diffusion_length_square_tensor('s1'))
test = {
'diffusion coefficient':
np.around(analysis.diffusion_coefficient('s1'), decimals=4),
'lifetime':
np.around(analysis.lifetime('s1'), decimals=4),
'diffusion length':
np.around(analysis.diffusion_length('s1'), decimals=4),
'diffusion tensor':
np.around(analysis.diffusion_coeff_tensor('s1',
unit_cell=[[0.0, 0.5],
[0.2, 0.0]]),
decimals=4).tolist(),
'diffusion length tensor':
np.around(analysis.diffusion_length_square_tensor(
's1', unit_cell=[[0.0, 0.5], [0.2, 0.0]]),
decimals=6).tolist()
}
print(test)
ref = {
'diffusion coefficient': 1586.4162,
'lifetime': 0.3621,
'diffusion length': 47.9375,
'diffusion tensor': [[2048.6421, 3.4329], [3.4329, 1124.1904]],
'diffusion length tensor': [[3142.8, 42.0], [42.0, 1453.2]]
}
self.assertDictEqual(ref, test)
def test_kmc_algorithm(self):
np.random.seed(0) # set random seed in order for the examples to reproduce the exact references
transitions = [Transition(s1, gs,
tdm=[0.3, 0.1], # a.u.
reorganization_energy=0.08, # eV
symmetric=True)]
marcus = MarcusModel(transitions=transitions,
temperature=300) # Kelvin
# list of transfer functions by state
self.system.process_scheme = [GoldenRule(initial_states=(s1, gs), final_states=(gs, s1),
electronic_coupling_function=forster_coupling_extended,
description='Forster',
arguments={'ref_index': 2, 'longitude': 2, 'n_divisions': 30,
'transitions': transitions},
vibrations=marcus
),
DecayRate(initial_state=s1, final_state=gs,
decay_rate_function=einstein_radiative_decay,
arguments={'transitions': transitions},
description='singlet_radiative_decay')
]
self.system.cutoff_radius = 10.0 # interaction cutoff radius in Angstrom
# some system analyze functions
system_test_info(self.system)
trajectories = calculate_kmc(self.system,
num_trajectories=10, # number of trajectories that will be simulated
max_steps=100, # maximum number of steps for trajectory allowed
silent=True)
# Results analysis
analysis = TrajectoryAnalysis(trajectories)
print('diffusion coefficient: {:9.5f} Angs^2/ns'.format(analysis.diffusion_coefficient('s1')))
print('lifetime: {:9.5f} ns'.format(analysis.lifetime('s1')))
print('diffusion length: {:9.5f} Angs'.format(analysis.diffusion_length('s1')))
print('diffusion tensor (Angs^2/ns)')
print(analysis.diffusion_coeff_tensor('s1'))
print('diffusion length square tensor (Angs)')
print(analysis.diffusion_length_square_tensor('s1'))
test = {'diffusion coefficient': np.around(analysis.diffusion_coefficient('s1'), decimals=4),
'lifetime': np.around(analysis.lifetime('s1'), decimals=4),
'diffusion length': np.around(analysis.diffusion_length('s1'), decimals=4),
'diffusion tensor': np.around(analysis.diffusion_coeff_tensor('s1', unit_cell=[[5.0, 1.0],
[1.0, 5.0]]), decimals=4).tolist(),
'diffusion length tensor': np.around(analysis.diffusion_length_square_tensor('s1', unit_cell=[[5.0, 1.0],
[1.0, 5.0]]), decimals=4).tolist()
}
ref = {'diffusion coefficient': 120.7623,
'lifetime': 2.1215,
'diffusion length': 33.0968,
'diffusion tensor': [[198.6035, -72.076],
[-72.076, 98.3641]],
'diffusion length tensor': [[1606.8, -728.0],
[-728.0, 1144.0]]
}
self.maxDiff = None
__import__('sys').modules['unittest.util']._MAX_LENGTH = 999999999
self.assertDictEqual(ref, test)
# Electronic couplings in eV for the closest neighbor molecule in the indicated direction
singlet_couplings = [12.61e-3, # a
41.85e-3, # ab
41.85e-3, # ab
27.51e-3] # b
triplet_couplings = [0.0e-3, # a
7.2e-3, # ab
7.2e-3, # ab
1.2e-3] # b
# Vibrations
vibrational_model = MarcusModel(reorganization_energies={(gs, s1): 0.07,
(s1, gs): 0.07,
(gs, t1): 0.07, # assuming triplet same reorganization
(t1, gs): 0.07}, # energy as singlet
temperature=300)
#################################################################################
# 2D model (plane a-b) , not diffusion in C
molecule = Molecule()
system = crystal_system(molecules=[molecule, molecule], # molecule to use as reference
scaled_site_coordinates=[[0.0, 0.0],
[0.5, 0.5]],
unitcell=[[7.3347, 0.0000],
[-0.2242, 6.0167]],
dimensions=[4, 4], # supercell size
orientations=[[0.0, 0.0, np.pi/8],
[0.0, 0.0, -np.pi/8]]) # if element is None then random, if list then Rx Ry Rz
# set initial exciton
system.add_excitation_index(s1, 0)
system.add_excitation_index(s1, 1)
# set additional system parameters
system.process_scheme = [
GoldenRule(
initial_states=(s1, gs),
final_states=(gs, s1),
electronic_coupling_function=forster_coupling,
description='Forster coupling',
arguments={
'ref_index': 1,
'transitions': transitions
},
vibrations=MarcusModel(transitions=transitions) # eV
),
DecayRate(initial_state=s1,
final_state=gs,
decay_rate_function=einstein_radiative_decay,
arguments={'transitions': transitions},
description='custom decay rate')
]
system.cutoff_radius = 8 # interaction cutoff radius in Angstrom
# some system analyze functions
system_test_info(system)
visualize_system(system)
# do the kinetic Monte Carlo simulation
num_trajectories = 500 # number of trajectories that will be simulated
max_steps = 100 # maximum number of steps for trajectory allowed
system = regular_system(molecule=molecule,
lattice={'size': [3, 3],
'parameters': [3.0, 3.0]}, # Angstroms
orientation=[0, 0, 0])
visualize_system(system)
system.cutoff_radius = 4.0 # Angstroms
system.process_scheme = [GoldenRule(initial_states=(s1, gs), final_states=(gs, s1),
electronic_coupling_function=forster_coupling,
arguments={'ref_index': 1,
'transition_moment': transition_moment},
vibrations=MarcusModel(),
description='forster'),
DecayRate(initial_state=s1, final_state=gs,
decay_rate_function=einstein_radiative_decay,
arguments={'transition_moment': transition_moment},
description='decay')]
system.add_excitation_index(s1, 1)
system_test_info(system)
trajectories = calculate_kmc(system,
num_trajectories=5, # number of trajectories that will be simulated
max_steps=1000, # maximum number of steps for trajectory allowed
silent=False)
def test_kmc_algorithm_2(self):
np.random.seed(
0
) # set random seed in order for the examples to reproduce the exact references
transitions = [
Transition(s1,
gs,
tdm=[0.01],
reorganization_energy=0.07,
symmetric=True)
]
# set additional system parameters
self.system.process_scheme = [
GoldenRule(initial_states=(s1, gs),
final_states=(gs, s1),
electronic_coupling_function=forster_coupling,
description='Forster coupling',
arguments={
'ref_index': 1,
'transitions': transitions
},
vibrations=MarcusModel(transitions=transitions)),
DecayRate(initial_state=s1,
final_state=gs,
decay_rate_function=decay_rate,
description='custom decay rate')
]
self.system.cutoff_radius = 10.0 # interaction cutoff radius in Angstrom
# some system analyze functions
system_test_info(self.system)
trajectories = calculate_kmc(
self.system,
num_trajectories=10, # number of trajectories that will be simulated
max_steps=10000, # maximum number of steps for trajectory allowed
silent=True)
# Results analysis
analysis = TrajectoryAnalysis(trajectories)
print('diffusion coefficient: {:9.5f} Angs^2/ns'.format(
analysis.diffusion_coefficient('s1')))
print('lifetime: {:9.5f} ns'.format(
analysis.lifetime('s1')))
print('diffusion length: {:9.5f} Angs'.format(
analysis.diffusion_length('s1')))
print('diffusion tensor (Angs^2/ns)')
print(analysis.diffusion_coeff_tensor('s1'))
print('diffusion length square tensor (Angs)')
print(analysis.diffusion_length_square_tensor('s1'))
test = {
'diffusion coefficient':
np.around(analysis.diffusion_coefficient('s1'), decimals=4),
'lifetime':
np.around(analysis.lifetime('s1'), decimals=4),
'diffusion length':
np.around(analysis.diffusion_length('s1'), decimals=4),
'diffusion tensor':
np.around(analysis.diffusion_coeff_tensor('s1'),
decimals=4).tolist(),
'diffusion length tensor':
np.around(np.sqrt(analysis.diffusion_length_square_tensor('s1')),
decimals=6).tolist()
}
print(test)
ref = {
'diffusion coefficient': 0.4265,
'lifetime': 24.1692,
'diffusion length': 5.4498,
'diffusion tensor': [[0.4265]],
'diffusion length tensor': [[5.449771]]
}
self.assertDictEqual(ref, test)