def test_af_conductivity_without_antiresonant_gauss(phonons):
cond = Conductivity(phonons=phonons, method='qhgk', storage='memory')
cond.diffusivity_shape = 'gauss'
cond.diffusivity_bandwidth = phonons.bandwidth.reshape(
(phonons.n_k_points, phonons.n_modes))
cond = (cond.conductivity.sum(axis=0).diagonal().mean())
np.testing.assert_approx_equal(cond, 0.8305, significant=3)
def test_diffusivity_large_threshold(phonons):
cond = Conductivity(phonons=phonons, method='qhgk', storage='memory')
cond.diffusivity_threshold = 20
cond.diffusivity_bandwidth = phonons.bandwidth.reshape(
(phonons.n_k_points, phonons.n_modes))
cond.conductivity
np.testing.assert_array_almost_equal(cond.diffusivity.flatten().real[:10],
calculated_diffusivities_full,
decimal=3)
def phononics(ge_concentration='0'):
# Set up Forceconstants Object
folder_string = 'structures/1728_atom/aSiGe_C' + ge_concentration + '/'
atoms = read(folder_string + 'replicated_atoms.xyz', format='xyz')
forceconstants = ForceConstants(atoms=atoms, folder=folder_string + '/ald')
# Calculate the 2nd + 3rd Forceconstant matrices
lammps_inputs = {
'lmpcmds': [
"pair_style tersoff",
"pair_coeff * * forcefields/SiCGe.tersoff Si(D) Ge"
],
"log_file":
"min.log",
"keep_alive":
True
}
calc = LAMMPSlib(**lammps_inputs)
second = forceconstants.second
second.load(folder=folder_string + '/ald')
print('Third')
# second.calculate(calculator=calc)
third = forceconstants.third
third.calculate(calculator=calc, is_verbose=True)
# Create Phonon Object
phonons = Phonons(
forceconstants=forceconstants,
is_classic=False, # quantum stats
temperature=300, # 300 K
folder=folder_string,
third_bandwidth=0.5 / 4.135, # 0.5 eV smearing
broadening_shape='gauss') # shape of smearing
# Phononic Data
## These save files to help us look at phononic properties
## with our plotter (4_plotting.py). These properties are "lazy" which
## means they won't be calculated unless explicitly called, or required
## by another calculation.
np.save('frequency', phonons.frequency)
np.save('bandwidth', phonons.bandwidth)
np.save('diffusivity', phonons.diffusivity)
#np.save('participation', phonons.participation_ratio)
# Conductivity Object
# This will print out the total conductivity and save the contribution per mode
conductivity = Conductivity(phonons=phonons, method='qhgk').conductivity
np.save('conductivity', 1 / 3 * np.einsum('iaa->i', conductivity))
print("Thermal Conductivity (W/m/K): %.3f" %
conductivity.sum(axis=0).diagonal().mean())
def test_inverse_conductivity(phonons):
cond = np.abs(
np.mean(
Conductivity(
phonons=phonons, method='inverse',
storage='memory').conductivity.sum(axis=0).diagonal()))
np.testing.assert_approx_equal(cond, 256, significant=3)
def test_detailed_balance(phonons):
cond = Conductivity(phonons=phonons, method='inverse',
storage='memory').conductivity.sum(axis=0)
cond_ref = np.array([[531.17210271, 2.04926525, -2.42778198],
[2.04926665, 530.92668135, -3.18903786],
[-2.4277473, -3.18902935, 537.86321364]])
np.testing.assert_array_almost_equal(cond, cond_ref, decimal=3)
def test_inverse_finite_size_conductivity_ms(phonons):
cond_ms = np.abs(
Conductivity(phonons=phonons,
method='inverse',
storage='memory',
length=(1e4, 0, 0),
finite_length_method='ms').conductivity.sum(axis=0)[0, 0])
np.testing.assert_approx_equal(cond_ms, 180.718, significant=3)
def test_inverse_finite_size_conductivity_caltech(phonons):
cond_c = np.abs(
Conductivity(
phonons=phonons,
method='inverse',
storage='memory',
length=(1e4, 0, 0),
finite_length_method='caltech').conductivity.sum(axis=0)[0, 0])
np.testing.assert_approx_equal(cond_c, 162.985, significant=3)
def test_rta_finite_size_conductivity_matthiessen(phonons):
cond_m = np.abs(
Conductivity(
phonons=phonons,
method='rta',
storage='memory',
length=(1e4, 0, 0),
finite_length_method='matthiessen').conductivity.sum(axis=0)[0, 0])
np.testing.assert_approx_equal(cond_m, 187.157, significant=3)
def test_sc_conductivity(phonons):
cond = np.abs(
np.mean(
Conductivity(
phonons=phonons,
method='sc',
max_n_iterations=71,
storage='memory').conductivity.sum(axis=0).diagonal()))
np.testing.assert_approx_equal(cond, 255, significant=3)
def test_af_conductivity_300(phonons):
phonons.temperature = 300
cond = Conductivity(phonons=phonons,
method='qhgk',
storage='memory',
diffusivity_bandwidth=0.025).conductivity.sum(
axis=0).diagonal().mean()
expected_cond = 0.532
np.testing.assert_approx_equal(cond, expected_cond, significant=2)
def test_sc_finite_size_conductivity_ms(phonons):
cond_ms = np.abs(
Conductivity(phonons=phonons,
method='sc',
max_n_iterations=71,
storage='memory',
length=(1e4, 0, 0),
finite_length_method='ms').conductivity.sum(axis=0)[0, 0])
np.testing.assert_approx_equal(cond_ms, 185.96, significant=3)
temperature = 300
# Create a phonon object
phonons = Phonons(forceconstants=forceconstants,
kpts=kpts,
is_classic=False,
temperature=temperature,
is_nw=True,
folder='ALD_CNT',
storage='numpy')
# Compute conductivity from direct inversion of
# scattering matrix for infinite size samples
# 29.92 is the rescale factor between
# the volume of the simulation box and the
# volume of the 10,0 Carbon Nanotube
inverse_conductivity = Conductivity(phonons=phonons,
method='inverse').conductivity
inverse_conductivity_matrix = inverse_conductivity.sum(axis=0)
print('Infinite size conductivity from inversion (W/m-K): %.3f' %
(29.92 * inverse_conductivity_matrix[2, 2]))
# Config finite size conductivity from direct inversion of scattering matrix
# Specific finite size length, in angstrom,
# along the direction of transport (z-axis)
# finite_length_method ='ms' for the Mckelvey-Schockley method
finite_size_conductivity_config = {
'method': 'inverse',
'length': (0, 0, 1000000000),
'finite_length_method': 'ms',
'storage': 'numpy'
}
finite_size_inverse_conductivity = Conductivity(
}
forceconstants = ForceConstants(atoms=atoms,
supercell=supercell,
folder='forceconstant')
forceconstants.second.calculate(calculator=LAMMPSlib(**lammps_inputs))
forceconstants.third.calculate(calculator=LAMMPSlib(**lammps_inputs))
n_replicas = np.prod(supercell)
kpts = [5, 5, 5]
temperature = 300
# # Create a phonon object
phonons = Phonons(forceconstants=forceconstants,
kpts=kpts,
is_classic=is_classic,
temperature=temperature,
folder='ald_out')
plotter.plot_dispersion(phonons)
print('Inverse conductivity in W/m/K')
print(Conductivity(phonons=phonons, method='inverse').conductivity.sum(axis=0))
print('RTA conductivity in W/m/K')
print(Conductivity(phonons=phonons, method='rta').conductivity.sum(axis=0))
plotter.plot_dos(phonons)
plotter.plot_vs_frequency(phonons, phonons.heat_capacity, 'cv')
plotter.plot_vs_frequency(phonons, phonons.bandwidth, 'gamma_THz')
plotter.plot_vs_frequency(phonons, phonons.phase_space, 'phase_space')
# Set up phonon object by passing in configuration details and the force constants object computed above
phonons = Phonons(forceconstants=forceconstants, **phonons_config)
### Set up the Conductivity object and thermal conductivity calculations ####
# Compute thermal conductivity (t.c.) by solving Boltzmann Transport
# Equation (BTE) with various of methods
# 'phonons': phonon object obtained from the above calculations
# 'method': specify methods to solve for BTE
# ('rta' for RTA,'sc' for self-consistent and 'inverse' for direct inversion of the scattering matrix)
print('\n')
inv_cond_matrix = (Conductivity(phonons=phonons,
method='inverse',
storage='formatted').conductivity.sum(axis=0))
print('Conductivity from inversion (W/m-K): %.3f' %
(np.mean(np.diag(inv_cond_matrix))))
print(inv_cond_matrix)
print('\n')
sc_cond_matrix = Conductivity(phonons=phonons,
method='sc',
n_iterations=20,
storage='formatted').conductivity.sum(axis=0)
print('Conductivity from self-consistent (W/m-K): %.3f' %
(np.mean(np.diag(sc_cond_matrix))))
print(sc_cond_matrix)
print('\n')
}
# Set up phonon object by passing in configuration details and the forceconstants object computed above
phonons = Phonons(forceconstants=forceconstants, **phonons_config)
### Set up the Conductivity object and thermal conductivity calculations ####
# Compute thermal conductivity (t.c.) by solving Boltzmann Transport
# Equation (BTE) with various of methods
# 'phonons': phonon object obtained from the above calculations
# 'method': specify methods to solve for BTE
# ('rta' for Relaxiation Time Approxmiation (RTA))
print('\n')
rta_cond_matrix = Conductivity(phonons=phonons,
method='rta').conductivity.sum(axis=0)
print('Conductivity from RTA (W/m-K): %.3f' %
(np.mean(np.diag(rta_cond_matrix))))
print(rta_cond_matrix)
# Define the base folder to contain plots
# 'base_folder':name of the base folder
folder = get_folder_from_label(phonons, base_folder='plots')
if not os.path.exists(folder):
os.makedirs(folder)
# Define a Boolean flag to specify if figure window pops during simulation
is_show_fig = False
# Plot cumulative conductivity from RTA method
rta_full_cond = Conductivity(phonons=phonons, method='rta').conductivity
plt.show()
### Set up the Conductivity object and thermal conductivity calculations ####
# Compute thermal conductivity (t.c.) by solving Boltzmann Transport
# Equation (BTE) with various of methods.
# 'phonons': phonon object obtained from the above calculations
# 'method': specify methods to solve for BTE
# ('rta' for RTA,'sc' for self-consistent and 'inverse' for direct inversion of the scattering matrix)
# 'storage': Format to storage conductivity and mean free path data ('formatted' for ASCII format data, 'numpy'
# for python numpy array and 'memory' for quick calculations, no data stored)
print('\n')
inv_cond_matrix = (Conductivity(phonons=phonons,
method='inverse',
storage='memory').conductivity.sum(axis=0))
print('Inverse conductivity (W/m-K): %.3f' %
(np.mean(np.diag(inv_cond_matrix))))
print(inv_cond_matrix)
print('\n')
sc_cond_matrix = Conductivity(phonons=phonons,
method='sc',
n_iterations=20,
storage='memory').conductivity.sum(axis=0)
print('Self-consistent conductivity (W/m-K): %.3f' %
(np.mean(np.diag(sc_cond_matrix))))
print(sc_cond_matrix)
print('\n')
k = 5
# Config phonon object
phonons_config = {
'kpts': [k, k, k],
'is_classic': False,
'temperature': 300,
'folder': 'ald_si_hiphive',
'is_tf_backend': False,
'storage': 'numpy'
}
phonons = Phonons(forceconstants=forceconstants, **phonons_config)
# Compute thermal conductivity with various methods
print('\n')
rta_cond_matrix = Conductivity(phonons=phonons,
method='rta').conductivity.sum(axis=0)
print('Rta conductivity (W/mK): %.3f' % (np.mean(np.diag(rta_cond_matrix))))
print(rta_cond_matrix)
print('\n')
sc_cond_matrix = Conductivity(phonons=phonons, method='sc',
n_iterations=20).conductivity.sum(axis=0)
print('Self-consistent conductivity (W/mK): %.3f' %
(np.mean(np.diag(sc_cond_matrix))))
print(sc_cond_matrix)
print('\n')
qhgk_cond_matrix = Conductivity(phonons=phonons,
method='qhgk').conductivity.sum(axis=0)
print('Qhgk conductivity (W/mK): %.3f' %
(-1 * np.mean(np.diag(qhgk_cond_matrix))))
supercell = np.array([3, 3, 3])
# forceconstants = ForceConstants.import_from_dlpoly_folder('si-dlpoly', supercell)
forceconstants = ForceConstants.from_folder('structures', supercell=supercell, format='lammps')
k = 5
kpts = [k, k, k]
is_classic = False
temperature = 300
# # Create a phonon object
phonons = Phonons(forceconstants=forceconstants,
kpts=kpts,
is_classic=is_classic,
temperature=temperature)
print('AF conductivity')
print(Conductivity(phonons=phonons, method='qhgk').conductivity.sum(axis=0))
plt.scatter(phonons.frequency.flatten()[3:], phonons.bandwidth.flatten()[3:], s=5)
plt.ylabel('gamma_THz', fontsize=16, fontweight='bold')
plt.xlabel("$\\nu$ (Thz)", fontsize=16, fontweight='bold')
plt.show()
plt.scatter(phonons.frequency.flatten()[3:], phonons.phase_space.flatten()[3:], s=5)
plt.ylabel('ps', fontsize=16, fontweight='bold')
plt.xlabel("$\\nu$ (Thz)", fontsize=16, fontweight='bold')
plt.show()
# -- Compare phonon life times at different level of theory -- #
# The following shows a comparison of phonon life times
# computed using Relaxation Time Approximation (RTA) and at direct inversion
# of scattering matrix (inverse) methods.
# 'n_phonons': number of phonons in the simulation
# 'band_width': phonon bandwdith (THz) computed from diagonal elements
# of scattering matrix
band_width = phonons.bandwidth.flatten(order='C')
tau_RTA = (band_width[3:]) ** (-1)
# Compute life times from direct inversion by dividing
# the mean free path from inversion by the group velocities
velocity = phonons.velocity.real.reshape((phonons.n_phonons, 3))
mean_free_path_inversion = Conductivity(phonons=phonons, method='inverse', storage='numpy').mean_free_path
tau_inversion = np.zeros_like(mean_free_path_inversion)
for alpha in range(3):
for mu in range(len(velocity)):
if velocity[mu, alpha] != 0:
tau_inversion[mu, alpha] = np.abs(np.divide(mean_free_path_inversion[mu, alpha],
velocity[mu, alpha]))
else:
# phonon life times remain zero at zero group velocities
tau_inversion[mu, alpha] = 0
plt.figure()
plt.plot(frequency[3:], tau_inversion[3:, 0], 'r.', label=r'$\tau_{inv,x}$')
plt.plot(frequency[3:], tau_inversion[3:, 1], 'b.', label=r'$\tau_{inv,y}$')
plt.plot(frequency[3:], tau_inversion[3:, 2], 'k.', label=r'$\tau_{inv,z}$')
from kaldo.controllers import plotter
from kaldo.forceconstants import ForceConstants
from kaldo.conductivity import Conductivity
from kaldo.phonons import Phonons
fold = 'ald'
kpts = [5, 5, 5]
supercell = [5, 5, 5]
temperature = 300
folder = 'fc'
forceconstant = ForceConstants.from_folder(folder=folder,
supercell=[5, 5, 5],
format='shengbte-qe')
for k in [5]:
kpts = [k, k, k]
phonons = Phonons(forceconstants=forceconstant,
kpts=kpts,
is_classic=False,
temperature=300,
folder='ald',
is_tf_backend=True,
grid_type='C')
print('Inverse conductivity W/m/K')
print(
Conductivity(phonons=phonons, method='inverse',
storage='memory').conductivity.sum(axis=0))
# 'folder': name of folder containing phonon property and thermal conductivity calculations
# 'storage': Format to storage phonon properties ('formatted' for ASCII format data, 'numpy'
# for python numpy array and 'memory' for quick calculations, no data stored)
phonons_config = {'is_classic': False,
'temperature': 300, #'temperature'=300K
'folder': 'ALD_aSi512',
'third_bandwidth':0.5/4.135, # 0.5 eV is used here.
'broadening_shape':'triangle',
'storage': 'numpy'}
# Set up phonon object by passing in configuration details and the forceconstants object computed above
phonons = Phonons(forceconstants=forceconstants, **phonons_config)
### Set up the Conductivity object and thermal conductivity calculations ####
# Compute thermal conductivity (t.c.) by solving Boltzmann Transport
# Equation (BTE) with various of methods
# 'phonons': phonon object obtained from the above calculations
# 'method': specify methods to solve for BTE
# ('qhgk' for Quasi-Harmonic Green Kubo (QHGK))
# 'storage': Format to storage phonon properties ('formatted' for ASCII format data, 'numpy'
# for python numpy array and 'memory' for quick calculations, no data stored)
print('\n')
qhgk_cond = Conductivity(phonons=phonons, method='qhgk', storage='numpy')
qhgk_cond.diffusivity_bandwidth = phonons.bandwidth
print('Conductivity from QHGK (W/m-K): %.3f' % (np.mean(np.diag(qhgk_cond.conductivity.sum(axis=0)))))
print(qhgk_cond.conductivity.sum(axis=0))
def test_qhgk_conductivity(phonons):
cond = Conductivity(phonons=phonons, method='qhgk',
storage='memory').conductivity.sum(axis=0)
cond = np.abs(np.mean(cond.diagonal()))
np.testing.assert_approx_equal(cond, 0.996, significant=2)
