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
# set additional system parameters
self.system.process_scheme = [
DirectRate(initial_states=(s1, gs),
final_states=(gs, s1),
rate_constant_function=transfer_rate,
arguments={'custom_constant': 1},
description='transport s1'),
DirectRate(initial_states=(s1, s1),
final_states=(tt, ),
rate_constant_function=transfer_rate,
arguments={'custom_constant': 1},
description='merge s1-s1 -> tt'),
DirectRate(initial_states=(tt, ),
final_states=(gs, gs),
rate_constant_function=decay_rate,
description='decay tt'),
DirectRate(initial_states=(tt, gs),
final_states=(gs, tt),
rate_constant_function=decay_rate,
description='transport tt')
]
self.system.cutoff_radius = 10.0 # interaction cutoff radius in Angstrom
p = DirectRate(initial_states=(s1, s1),
final_states=(tt, ),
rate_constant_function=transfer_rate,
arguments={'custom_constant': 1},
description='custom2')
#print(p.get_transition_connections())
#exit()
# 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=50, # 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('tt')))
print('lifetime: {:9.5f} ns'.format(
analysis.lifetime('tt')))
print('diffusion length: {:9.5f} Angs'.format(
analysis.diffusion_length('tt')))
print('diffusion tensor (Angs^2/ns)')
print(analysis.diffusion_coeff_tensor('tt'))
print('diffusion length square tensor (Angs)')
print(analysis.diffusion_length_square_tensor('tt'))
test = {
'diffusion coefficient':
np.around(analysis.diffusion_coefficient('tt'), decimals=4),
'lifetime':
np.around(analysis.lifetime('tt'), decimals=4),
'diffusion length':
np.around(analysis.diffusion_length('tt'), decimals=4),
'diffusion tensor':
np.around(analysis.diffusion_coeff_tensor('tt'),
decimals=4).tolist(),
'diffusion length tensor':
np.around(np.sqrt(analysis.diffusion_length_square_tensor('tt')),
decimals=4).tolist()
}
ref = {
'diffusion coefficient': 0.4819,
'lifetime': 28.3726,
'diffusion length': 4.1018,
'diffusion tensor': [[0.4819]],
'diffusion length tensor': [[4.1018]]
}
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)
system_test_info(system)
#visualize_system(system, dipole='s1')
system.add_excitation_index('s1', 0)
# do the kinetic Monte Carlo simulation
trajectories = calculate_kmc_parallel(
system,
processors=2,
num_trajectories=50, # number of trajectories that will be simulated
max_steps=10, # maximum number of steps for trajectory allowed
silent=False)
# diffusion properties
analysis = TrajectoryAnalysis(trajectories)
for state in analysis.get_states():
print('\nState: {}\n--------------------------------'.format(state))
print('diffusion coefficient: {} angs^2/ns'.format(
analysis.diffusion_coefficient(state)))
print('lifetime: {} ns'.format(analysis.lifetime(state)))
print('diffusion length: {} angs'.format(analysis.diffusion_length(state)))
print('diffusion tensor (angs^2/ns)')
print(analysis.diffusion_coeff_tensor(state))
print(analysis.diffusion_coeff_tensor(state, unit_cell=system.supercell))
print('diffusion length tensor (angs)')
print(analysis.diffusion_length_square_tensor(state))
print(
analysis.diffusion_length_square_tensor(state,
system.add_excitation_random(s1, 2)
system_test_info(system)
visualize_system(system)
# exit()
trajectories = calculate_kmc(system,
num_trajectories=50, # number of trajectories that will be simulated
max_steps=1000, # maximum number of steps for trajectory allowed
silent=False)
store_trajectory_list(trajectories, 'singlet_fission.h5')
#trajectories = load_trajectory_list('singlet_fission.h5')
analysis = TrajectoryAnalysis(trajectories)
for s in ['s1', 't1']:
print('STATE: ', s)
print('diffusion coefficient (average): {} angs^2/ns'.format(analysis.diffusion_coefficient(s)))
print('lifetime: {} ns'.format(analysis.lifetime(s)))
print('diffusion length: {} angs'.format(analysis.diffusion_length(s)))
print('diffusion tensor')
print(analysis.diffusion_coeff_tensor(s))
print(analysis.diffusion_coeff_tensor(s, unit_cell=system.supercell))
print('diffusion length tensor')
print(analysis.diffusion_length_square_tensor(s))
print(analysis.diffusion_length_square_tensor(s, unit_cell=system.supercell))
# print(np.sqrt(analysis.diffusion_coeff_tensor()*analysis.lifetime()*2))
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, symmetric=True)]
# list of transfer functions by state
self.system.process_scheme = [
SimpleRate(initial_states=(s1, gs),
final_states=(gs, s1),
rate_constant=0.1),
SimpleRate(initial_states=(s1, ),
final_states=(gs, ),
rate_constant=0.01)
]
# some system analyze functions
system_test_info(self.system)
trajectories = calculate_kmc(
self.system,
num_trajectories=
500, # number of trajectories that will be simulated
max_steps=1000, # 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': 2.4439,
'lifetime': 92.7496,
'diffusion length': 30.1679,
'diffusion tensor': [[2.1338, 0.1308], [0.1308, 2.7539]],
'diffusion length tensor': [[798.7, 25.7], [25.7, 1021.5]]
}
# This is just for visual comparison (not accounted in the test)
decay = 0.01
transfer = 0.1
distance = 5
print('analytical model')
print('----------------')
data = get_analytical_model(distance, analysis.n_dim, transfer, decay)
print('results analytical:', data)
self.assertDictEqual(ref, test)
system_test_info(system)
visualize_system(system)
# do the kinetic Monte Carlo simulation
trajectories = calculate_kmc(
system,
num_trajectories=5, # number of trajectories that will be simulated
max_steps=100, # maximum number of steps for trajectory allowed
silent=False)
# specific trajectory plot
trajectories[0].plot_graph().show()
trajectories[0].plot_2d().show()
# resulting trajectories analysis
analysis = TrajectoryAnalysis(trajectories)
print('diffusion coefficient: {:9.5e} Angs^2/ns'.format(
analysis.diffusion_coefficient()))
print('lifetime: {:9.5e} ns'.format(analysis.lifetime()))
print('diffusion length: {:9.5e} Angs'.format(
analysis.diffusion_length()))
for state in analysis.get_states():
print('\nState: {}\n--------------------------------'.format(state))
print('diffusion coefficient: {:9.5e} Angs^2/ns'.format(
analysis.diffusion_coefficient(state)))
print('lifetime: {:9.5e} ns'.format(analysis.lifetime(state)))
print('diffusion length: {:9.5e} Angs'.format(
analysis.diffusion_length(state)))
print('diffusion tensor (angs^2/ns)')
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)
analysis = TrajectoryAnalysis(trajectories)
print('diffusion coefficient (average): {} angs^2/ns'.format(analysis.diffusion_coefficient('s1')))
print('lifetime: {} ns'.format(analysis.lifetime('s1')))
print('diffusion length: {} angs'.format(analysis.diffusion_length('s1')))
print('diffusion tensor')
print(analysis.diffusion_coeff_tensor('s1'))
print('diffusion length tensor')
print(analysis.diffusion_length_square_tensor('s1'))
# print(np.sqrt(analysis.diffusion_coeff_tensor()*analysis.lifetime()*2))
plt = analysis.plot_2d()
plt.figure()
analysis.plot_distances()
plt.show()
def test_kmc_algorithm(self):
np.random.seed(
0
) # set random seed in order for the examples to reproduce the exact references
# set additional system parameters
self.system.process_scheme = [
DirectRate(initial_states=(s1, gs),
final_states=(gs, s1),
rate_constant_function=transfer_rate,
arguments={'custom_constant': 1},
description='transport'),
DirectRate(initial_states=(s1, ),
final_states=(gs, ),
rate_constant_function=decay_rate,
description='custom decay rate'),
DirectRate(initial_states=(s2, s2),
final_states=(gs, s1),
rate_constant_function=decay_rate,
description='merge'),
DirectRate(initial_states=(s1, gs),
final_states=(s2, s2),
rate_constant_function=decay_rate,
description='split'),
DirectRate(initial_states=(s2, gs),
final_states=(gs, s2),
rate_constant_function=transfer_rate,
arguments={'custom_constant': 1},
description='transport 2'),
DirectRate(initial_states=(s2, ),
final_states=(gs, ),
rate_constant_function=decay_rate,
description='custom decay rate 2'),
]
#print([s.label for s in self.system.decay_scheme[0].initial], '--',
# [s.label for s in self.system.decay_scheme[0].final])
#print([s.label for s in self.system.decay_scheme[1].initial], '--',
# [s.label for s in self.system.decay_scheme[1].final])
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=50, # number of trajectories that will be simulated
max_steps=18, # maximum number of steps for trajectory allowed
silent=True)
# Results analysis
analysis = TrajectoryAnalysis(trajectories)
for traj in trajectories:
print('diff:', traj.get_diffusion('s1'))
print('diff none:', traj.get_diffusion(None))
#print('path:', traj._trajectory_node_path())
#print('path:', trajectories[0]._trajectory_node_path_new())
#print('-> diff:', trajectories[8].get_diffusion('s1'))
#print('-> diff none:', trajectories[8].get_diffusion(None))
#print('path:', trajectories[8]._trajectory_node_path())
#trajectories[0].plot_graph().show()
#exit()
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=4).tolist()
}
print(test)
ref = {
'diffusion coefficient': 3.5571,
'lifetime': 2.2493,
'diffusion length': 3.9478,
'diffusion tensor': [[3.5571]],
'diffusion length tensor': [[3.9478]]
}
self.assertDictEqual(ref, test)
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)
if parallel_run:
trajectories = calculate_kmc_parallel(
system,
processors=6,
num_trajectories=100, # number of trajectories that will be simulated
max_steps=100000, # maximum number of steps for trajectory allowed
silent=False)
else:
trajectories = calculate_kmc(
system,
num_trajectories=10, # number of trajectories that will be simulated
max_steps=100000, # maximum number of steps for trajectory allowed
silent=False)
# Results analysis
analysis = TrajectoryAnalysis(trajectories)
print('diffusion coefficient: {:9.5e} Angs^2/ns'.format(
analysis.diffusion_coefficient('s1')))
print('lifetime: {:9.5e} ns'.format(analysis.lifetime('s1')))
print('diffusion length: {:9.5e} 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'))
analysis.plot_exciton_density('s1').show()
analysis.plot_distances('s1').show()
analysis.plot_histogram('s1', normalized=True, bins=20).show()