def get_gibbs(structure, db_file, eos="vinet", t_step=10, t_min=0, t_max=1000, mesh=(20, 20, 20),
plot=False):
# other eos options: birch_murnaghan, murnaghan
# The physical units of V and T are \AA^3 and K, respectively.
# The unit of eV for Helmholtz and Gibbs energies,
# J/K/mol for C_V and entropy, GPa for for bulk modulus and pressure are used.
try:
from phonopy import PhonopyQHA
except ImportError:
print("Install phonopy. Exiting.")
sys.exit()
phonon = get_phonopy(structure)
energies, volumes, force_constants = get_data(db_file, query={
"task_label": {"$regex": "gibbs*"}, "formula_pretty": structure.composition.reduced_formula})
temperatures = []
free_energy = []
entropy = []
cv = []
for f in force_constants:
phonon.set_force_constants(-np.array(f))
phonon.set_mesh(list(mesh))
phonon.set_thermal_properties(t_step=t_step, t_min=t_min, t_max=t_max)
t, g, e, c = phonon.get_thermal_properties()
temperatures.append(t)
free_energy.append(g)
entropy.append(e)
cv.append(c)
phonopy_qha = PhonopyQHA(volumes, energies, eos=eos, temperatures=temperatures[0],
free_energy=np.array(free_energy).T, cv=np.array(cv).T,
entropy=np.array(entropy).T, t_max=np.max(temperatures[0]))
# gibbs free energy
max_t_index = phonopy_qha._qha._max_t_index
G = phonopy_qha.get_gibbs_temperature()[:max_t_index]
T = phonopy_qha._qha._temperatures[:max_t_index]
if plot:
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
import matplotlib.pyplot as plt
plt.plot(T, G)
plt.savefig("Gibbs.pdf")
plt.show()
#phonopy_qha.plot_qha(thin_number=10, volume_temp_exp=None).show()
else:
return T, G
def get_qha(eos, temperatures, fe_phonon, cv, entropy, t_max=1000):
from phonopy import PhonopyQHA
import numpy as np
phonopy_qha = PhonopyQHA(eos.get_array('volumes'),
eos.get_array('energies'),
eos="vinet",
temperatures=np.array(temperatures),
free_energy=np.array(fe_phonon).T,
cv=np.array(cv),
entropy=np.array(entropy),
t_max=t_max,
verbose=False)
# testing
if __testing__:
import matplotlib.pyplot as plt
plt = phonopy_qha.plot_qha()
plt.show()
qha_results = {'qha_temperatures': phonopy_qha._qha._temperatures[:phonopy_qha._qha._max_t_index],
'helmholtz_volume': phonopy_qha.get_helmholtz_volume(),
'thermal_expansion': phonopy_qha.get_thermal_expansion(),
'volume_temperature': phonopy_qha.get_volume_temperature(),
'heat_capacity_P_numerical': phonopy_qha.get_heat_capacity_P_numerical(),
'volume_expansion': phonopy_qha.get_volume_expansion(),
'gibbs_temperature': phonopy_qha.get_gibbs_temperature()}
return qha_results
def volume_shift(self, volume_range=np.arange(-2.0, 2.0, 0.1)):
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
volumes = self.phonopy_qha._qha._volumes
energies = self.phonopy_qha._qha._electronic_energies
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
for i in volume_range:
volumesi = np.array(volumes) + i
print volumesi
phonopy_qha = PhonopyQHA(
volumesi,
energies,
eos=
"vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
cp = phonopy_qha.get_heat_capacity_P_numerical()
import matplotlib.pyplot as plt
import matplotlib.colors as colors
cNorm = colors.Normalize(vmin=volume_range[0],
vmax=volume_range[-1])
scalarMap = plt.cm.ScalarMappable(norm=cNorm,
cmap=plt.cm.get_cmap('plasma'))
ax.plot(phonopy_qha._qha._temperatures[:-3],
cp,
label='{}'.format(i),
color=scalarMap.to_rgba(i))
import matplotlib as mpl
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
mpl.colorbar.ColorbarBase(ax2,
cmap=plt.cm.get_cmap('plasma'),
norm=cNorm,
spacing='proportional',
ticks=volume_range,
boundaries=None,
format='%1i')
plt.show()
def volume_shift(self, volume_range=np.arange(-2.0, 2.0, 0.1)):
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
volumes = self.phonopy_qha._qha._volumes
energies = self.phonopy_qha._qha._electronic_energies
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
for i in volume_range:
volumesi = np.array(volumes) + i
print volumesi
phonopy_qha = PhonopyQHA(volumesi,
energies,
eos="vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
cp = phonopy_qha.get_heat_capacity_P_numerical()
import matplotlib.pyplot as plt
import matplotlib.colors as colors
cNorm = colors.Normalize(vmin=volume_range[0], vmax=volume_range[-1])
scalarMap = plt.cm.ScalarMappable(norm=cNorm, cmap=plt.cm.get_cmap('plasma'))
ax.plot(phonopy_qha._qha._temperatures[:-3], cp, label='{}'.format(i), color=scalarMap.to_rgba(i))
import matplotlib as mpl
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
mpl.colorbar.ColorbarBase(ax2, cmap=plt.cm.get_cmap('plasma'), norm=cNorm,
spacing='proportional', ticks=volume_range,
boundaries=None, format='%1i')
plt.show()
def get_phonopy_qha(energies, volumes, force_constants, structure, t_min, t_step, t_max, mesh, eos,
pressure=0):
"""
Return phonopy QHA interface.
Args:
energies (list):
volumes (list):
force_constants (list):
structure (Structure):
t_min (float): min temperature
t_step (float): temperature step
t_max (float): max temperature
mesh (list/tuple): reciprocal space density
eos (str): equation of state used for fitting the energies and the volumes.
options supported by phonopy: vinet, murnaghan, birch_murnaghan
pressure (float): in GPa, optional.
Returns:
PhonopyQHA
"""
from phonopy import Phonopy
from phonopy.structure.atoms import Atoms as PhonopyAtoms
from phonopy import PhonopyQHA
from phonopy.units import EVAngstromToGPa
phon_atoms = PhonopyAtoms(symbols=[str(s.specie) for s in structure],
scaled_positions=structure.frac_coords,
cell=structure.lattice.matrix)
scell = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
phonon = Phonopy(phon_atoms, scell)
# compute the required phonon thermal properties
temperatures = []
free_energy = []
entropy = []
cv = []
for f in force_constants:
phonon.set_force_constants(-np.array(f))
phonon.set_mesh(list(mesh))
phonon.set_thermal_properties(t_step=t_step, t_min=t_min, t_max=t_max)
t, g, e, c = phonon.get_thermal_properties()
temperatures.append(t)
free_energy.append(g)
entropy.append(e)
cv.append(c)
# add pressure contribution
energies = np.array(energies) + np.array(volumes) * pressure / EVAngstromToGPa
# quasi-harmonic approx
return PhonopyQHA(volumes, energies, eos=eos, temperatures=temperatures[0],
free_energy=np.array(free_energy).T, cv=np.array(cv).T,
entropy=np.array(entropy).T, t_max=np.max(temperatures[0]))
def __init__(self,
volumes,
electronic_energies,
temperatures,
free_energy,
cv,
entropy,
eos='vinet',
t_max=1000.0,
Z=1,
verbose=True):
self._qha = PhonopyQHA(volumes,
electronic_energies,
eos=eos,
temperatures=temperatures,
free_energy=fe_phonon,
cv=cv,
entropy=entropy,
t_max=t_max,
verbose=True)
self._Z = Z
cv = []
fe = []
for index in range(-5,6):
filename = "thermal_properties.yaml-%d" % index
print("Reading %s" % filename)
thermal_properties = yaml.load(open(filename),
Loader=Loader)['thermal_properties']
temperatures = [v['temperature'] for v in thermal_properties]
cv.append([v['heat_capacity'] for v in thermal_properties])
entropy.append([v['entropy'] for v in thermal_properties])
fe.append([v['free_energy'] for v in thermal_properties])
qha = PhonopyQHA(volumes,
energies,
temperatures=temperatures,
free_energy=np.transpose(fe),
cv=np.transpose(cv),
entropy=np.transpose(entropy),
t_max=400,
verbose=True)
# qha.plot_helmholtz_volume().show()
# qha.plot_volume_temperature().show()
qha.plot_thermal_expansion().show()
# plot = qha.plot_volume_expansion()
# if plot:
# plot.show()
# qha.plot_gibbs_temperature().show()
# qha.plot_bulk_modulus_temperature().show()
# qha.plot_heat_capacity_P_numerical().show()
# qha.plot_heat_capacity_P_polyfit().show()
# qha.plot_gruneisen_temperature().show()
cv = []
fe = []
for index in range(-5, 6):
filename = "thermal_properties.yaml-%d" % index
print("Reading %s" % filename)
thermal_properties = yaml.load(open(filename), Loader=Loader)["thermal_properties"]
temperatures = [v["temperature"] for v in thermal_properties]
cv.append([v["heat_capacity"] for v in thermal_properties])
entropy.append([v["entropy"] for v in thermal_properties])
fe.append([v["free_energy"] for v in thermal_properties])
qha = PhonopyQHA(
volumes,
energies,
temperatures=temperatures,
free_energy=np.transpose(fe),
cv=np.transpose(cv),
entropy=np.transpose(entropy),
t_max=400,
verbose=True,
)
# qha.plot_helmholtz_volume().show()
# qha.plot_volume_temperature().show()
qha.plot_thermal_expansion().show()
# plot = qha.plot_volume_expansion()
# if plot:
# plot.show()
# qha.plot_gibbs_temperature().show()
# qha.plot_bulk_modulus_temperature().show()
# qha.plot_heat_capacity_P_numerical().show()
# qha.plot_heat_capacity_P_polyfit().show()
def qha_prediction(wf, interval, min, max, use_all_data=True):
# max = wf.get_attribute('max')
# min = wf.get_attribute('min')
wf_complete_list = []
for step_name in ['pressure_expansions', 'collect_data']:
if wf.get_step(step_name):
wf_complete_list += list(
wf.get_step(step_name).get_sub_workflows())
wf_complete_list += list(
wf.get_step('start').get_sub_workflows()[0].get_step(
'start').get_sub_workflows())
if use_all_data:
# check data is stable
good = [
wf_test.get_attribute('pressure') for wf_test in wf_complete_list
if check_dos_stable(wf_test, tol=1e-6)
]
good = np.sort(good)
test_pressures = np.array(good)
test_pressures = test_pressures[np.unique(
np.round(test_pressures,
decimals=4), return_index=True)[1]].tolist()
else:
test_pressures = np.arange(min, max, interval).tolist()
volumes = []
stresses = []
electronic_energies = []
temperatures = []
fe_phonon = []
entropy = []
cv = []
if True:
for wf_test in wf_complete_list:
for pressure in test_pressures:
if wf_test.get_state() == 'FINISHED':
if np.isclose(wf_test.get_attribute('pressure'),
pressure,
atol=interval / 4,
rtol=0):
thermal_properties = wf_test.get_result(
'thermal_properties')
optimized_data = wf_test.get_result(
'optimized_structure_data')
final_structure = wf_test.get_result('final_structure')
electronic_energies.append(optimized_data.dict.energy)
volumes.append(final_structure.get_cell_volume())
stresses.append(pressure)
temperatures = thermal_properties.get_array(
'temperature')
fe_phonon.append(
thermal_properties.get_array('free_energy'))
entropy.append(thermal_properties.get_array('entropy'))
cv.append(thermal_properties.get_array('cv'))
if False:
test_pressures = []
for wf_test in wf_complete_list:
if wf_test.get_state() != 'ERROR':
repeated = False
for p in test_pressures:
if np.isclose(wf_test.get_attribute('pressure'),
p,
atol=interval / 4,
rtol=0):
repeated = True
if not repeated:
test_pressures.append(wf_test.get_attribute('pressure'))
thermal_properties = wf_test.get_result(
'thermal_properties')
optimized_data = wf_test.get_result(
'optimized_structure_data')
final_structure = wf_test.get_result('final_structure')
electronic_energies.append(optimized_data.dict.energy)
volumes.append(final_structure.get_cell_volume())
temperatures = thermal_properties.get_array('temperature')
fe_phonon.append(
thermal_properties.get_array('free_energy'))
entropy.append(thermal_properties.get_array('entropy'))
cv.append(thermal_properties.get_array('cv'))
if len(stresses) < 5:
# raise Exception('Not enough points for QHA prediction')
return None
sort_index = np.argsort(volumes)
stresses = np.array(stresses)[sort_index]
volumes = np.array(volumes)[sort_index]
electronic_energies = np.array(electronic_energies)[sort_index]
temperatures = np.array(temperatures)
fe_phonon = np.array(fe_phonon).T[:, sort_index]
entropy = np.array(entropy).T[:, sort_index]
cv = np.array(cv).T[:, sort_index]
# Calculate QHA properties
phonopy_qha = PhonopyQHA(
np.array(volumes),
np.array(electronic_energies),
eos="vinet",
temperatures=np.array(temperatures),
free_energy=np.array(fe_phonon),
cv=np.array(cv),
entropy=np.array(entropy),
# t_max=options.t_max,
verbose=False)
# Get data
volume_temperature = phonopy_qha.get_volume_temperature()
from scipy.optimize import curve_fit, OptimizeWarning
try:
# Fit to an exponential equation
def fitting_function(x, a, b, c):
return np.exp(-b * (x + a)) + c
p_b = 0.1
p_c = -200
p_a = -np.log(-p_c) / p_b - volumes[0]
popt, pcov = curve_fit(fitting_function,
volumes,
stresses,
p0=[p_a, p_b, p_c],
maxfev=100000)
min_stresses = fitting_function(volume_temperature, *popt)
except OptimizeWarning:
fit_vs = np.polyfit(volumes, stresses, 2)
min_stresses = np.array(
[np.polyval(fit_vs, i) for i in volume_temperature])
# if (np.max(min_stresses) - np.min(min_stresses)) < 1:
# return None
tolerance = 0.8
addition = (np.max(min_stresses) - np.min(min_stresses)) * tolerance
return np.min(min_stresses) - addition, np.max(min_stresses) + addition
def collect_data(self):
energies = []
volumes = []
# Get the calculations from workflow
from itertools import chain
# wf_list = list(chain(self.get_step(self.volume_expansions).get_sub_workflows(),
# self.get_step(self.start).get_sub_workflows()))
wf_list = self.get_step(self.volume_expansions).get_sub_workflows()
for wf in wf_list:
dos = wf.get_result('dos').get_array('total_dos')
# self.append_to_report('DOS')
# self.append_to_report('{}'.format(dos.tolist()))
energy = wf.get_result(
'optimized_structure_data').get_dict()['energy']
volume = wf.get_result('final_structure').get_cell_volume()
self.append_to_report('{} {}'.format(volume, energy))
energies.append(energy)
volumes.append(volume)
import numpy as np
# data = ArrayData()
# data.set_array('energy', np.array(energies))
# data.set_array('volume', np.array(volumes))
# data.store()
# self.add_result('data', data)
volumes = []
electronic_energies = []
fe_phonon = []
entropy = []
cv = []
thermal_list = []
structures = []
optimized_data = []
inline_params = {}
wf_list = list(
chain(
self.get_step(self.volume_expansions).get_sub_workflows(),
self.get_step(self.start).get_sub_workflows()))
i = 0
for wf in wf_list:
# Check if suitable for qha (no imaginary frequencies in DOS)
try:
freq = wf.get_result('dos').get_array('frequency')
dos = wf.get_result('dos').get_array('total_dos')
except:
continue
mask_neg = np.ma.masked_less(freq, 0.0).mask
mask_pos = np.ma.masked_greater(freq, 0.0).mask
int_neg = -np.trapz(np.multiply(dos[mask_neg], freq[mask_neg]),
x=freq[mask_neg])
int_pos = np.trapz(np.multiply(dos[mask_pos], freq[mask_pos]),
x=freq[mask_pos])
if int_neg / int_pos > 1e-6:
volume = wf.get_result('final_structure').get_cell_volume()
self.append_to_report(
'Volume {} not suitable for QHA (imaginary frequencies in DOS), skipping it'
.format(volume))
continue
# print ('int_neg: {} int_pos: {} rel: {}'.format(int_neg, int_pos, int_neg/int_pos))
# Get necessary data
thermal_list.append(wf.get_result('thermal_properties'))
structures.append(wf.get_result('final_structure'))
optimized_data.append(wf.get_result('optimized_structure_data'))
electronic_energies.append(
wf.get_result('optimized_structure_data').get_dict()['energy'])
volumes.append(wf.get_result('final_structure').get_cell_volume())
# fe_phonon.append(wf.get_result('thermal_properties').get_array('free_energy'))
# entropy.append(wf.get_result('thermal_properties').get_array('entropy'))
# cv.append(wf.get_result('thermal_properties').get_array('cv'))
temperatures = wf.get_result('thermal_properties').get_array(
'temperature')
temperature_fit = np.arange(
0, 1001,
10) #[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]
fe_phonon.append(
np.interp(
temperature_fit, temperatures,
wf.get_result('thermal_properties').get_array(
'free_energy')))
entropy.append(
np.interp(
temperature_fit, temperatures,
wf.get_result('thermal_properties').get_array('entropy')))
cv.append(
np.interp(temperature_fit, temperatures,
wf.get_result('thermal_properties').get_array('cv')))
temperatures = temperature_fit
i += 1
# inline_params['structure_{}'.format(i)] = wf.get_result('final_structure')
# inline_params['optimized_data_{}'.format(i)] = wf.get_result('optimized_structure_data')
# inline_params['thermal_properties_{}'.format(i)] = wf.get_result('thermal_properties')
# self.append_to_report('accepted: {} {}'.format(i, wf.get_result('thermal_properties').pk))
# entropy = []
# cv = []
# fe_phonon = []
# temperatures = None
# volumes = [value.get_cell_volume() for key, value in inline_params.items() if 'structure' in key.lower()]
# electronic_energies = [value.get_dict()['energy'] for key, value in inline_params.items() if 'optimized_data' in key.lower()]
#
# thermal_properties_list = [key for key, value in inline_params.items() if 'thermal_properties' in key.lower()]
# for key in thermal_properties_list:
# thermal_properties = inline_params[key]
# fe_phonon.append(thermal_properties.get_array('free_energy'))
# entropy.append(thermal_properties.get_array('entropy'))
# cv.append(thermal_properties.get_array('cv'))
# temperatures = thermal_properties.get_array('temperature')
from phonopy import PhonopyQHA
import numpy as np
# Arrange data sorted by volume and transform them to numpy array
sort_index = np.argsort(volumes)
volumes = np.array(volumes)[sort_index]
electronic_energies = np.array(electronic_energies)[sort_index]
temperatures = np.array(temperatures)
fe_phonon = np.array(fe_phonon).T[:, sort_index]
entropy = np.array(entropy).T[:, sort_index]
cv = np.array(cv).T[:, sort_index]
opt = np.argmin(electronic_energies)
# Check minimum energy volume is within the data
if np.ma.masked_less_equal(volumes, volumes[opt]).mask.all():
self.append_to_report(
'higher volume structures are necessary to compute')
if np.ma.masked_greater_equal(volumes, volumes[opt]).mask.all():
self.append_to_report(
'Lower volume structures are necessary to compute')
self.append_to_report('Dimensions T{} : FE{}'.format(
len(temperatures), len(fe_phonon)))
# Calculate QHA
phonopy_qha = PhonopyQHA(
np.array(volumes),
np.array(electronic_energies),
eos="vinet",
temperatures=np.array(temperatures),
free_energy=np.array(fe_phonon),
cv=np.array(cv),
entropy=np.array(entropy),
# t_max=options.t_max,
verbose=False)
#Get data
qha_temperatures = phonopy_qha._qha._temperatures[:phonopy_qha._qha.
_max_t_index]
helmholtz_volume = phonopy_qha.get_helmholtz_volume()
thermal_expansion = phonopy_qha.get_thermal_expansion()
volume_temperature = phonopy_qha.get_volume_temperature()
heat_capacity_P_numerical = phonopy_qha.get_heat_capacity_P_numerical()
volume_expansion = phonopy_qha.get_volume_expansion()
gibbs_temperature = phonopy_qha.get_gibbs_temperature()
qha_output = ArrayData()
qha_output.set_array('temperature', np.array(qha_temperatures))
qha_output.set_array('helmholtz_volume', np.array(helmholtz_volume))
qha_output.set_array('thermal_expansion', np.array(thermal_expansion))
qha_output.set_array('volume_temperature',
np.array(volume_temperature))
qha_output.set_array('heat_capacity_P_numerical',
np.array(heat_capacity_P_numerical))
qha_output.set_array('volume_expansion', np.array(volume_expansion))
qha_output.set_array('gibbs_temperature', np.array(gibbs_temperature))
qha_output.store()
# Test to leave something on folder
# phonopy_qha.plot_pdf_bulk_modulus_temperature()
# import matplotlib
# matplotlib.use('Agg')
repo_path = self._repo_folder.abspath
data_folder = self.current_folder.get_subfolder('DATA_FILES')
data_folder.create()
# phonopy_qha.plot_pdf_bulk_modulus_temperature(filename=repo_path + '/bulk_modulus-temperature.pdf')
phonopy_qha.write_bulk_modulus_temperature(filename='bm')
file = open('bm')
data_folder.create_file_from_filelike(file, 'bulk_modulus-temperature')
# qha_results = calculate_qha_inline(**inline_params)[1]
self.append_to_report('QHA properties calculated and retrieved')
self.add_result('qha', qha_output)
data = ArrayData()
data.set_array('energy', np.array(electronic_energies))
data.set_array('volume', np.array(volumes))
data.store()
self.add_result('data', data)
self.append_to_report('Finishing workflow_workflow')
self.next(self.exit)
cv = []
fe = []
for index in range(-5, 6):
filename = "thermal_properties.yaml-%d" % index
print "Reading", filename
thermal_properties = yaml.load(open(filename),
Loader=Loader)['thermal_properties']
temperatures = [v['temperature'] for v in thermal_properties]
cv.append([v['heat_capacity'] for v in thermal_properties])
entropy.append([v['entropy'] for v in thermal_properties])
fe.append([v['free_energy'] for v in thermal_properties])
qha = PhonopyQHA(volumes,
energies,
temperatures=temperatures,
free_energy=np.transpose(fe),
cv=np.transpose(cv),
entropy=np.transpose(entropy),
t_max=400,
verbose=True)
# qha.plot_helmholtz_volume().show()
# qha.plot_volume_temperature().show()
# qha.plot_thermal_expansion().show()
# plot = qha.plot_volume_expansion()
# if plot:
# plot.show()
# qha.plot_gibbs_temperature().show()
# qha.plot_bulk_modulus_temperature().show()
# qha.plot_heat_capacity_P_numerical().show()
# qha.plot_heat_capacity_P_polyfit().show()
qha.plot_gruneisen_temperature().show()
class QHA:
def __init__(self,
volumes,
electronic_energies,
temperatures,
free_energy,
cv,
entropy,
eos='vinet',
t_max=1000.0,
Z=1,
verbose=True):
self._qha = PhonopyQHA(volumes,
electronic_energies,
eos=eos,
temperatures=temperatures,
free_energy=fe_phonon,
cv=cv,
entropy=entropy,
t_max=t_max,
verbose=True)
self._Z = Z
def run(self):
self._set_mesh(distance=distance)
if self._run_mesh_sampling():
self._run_gruneisen()
return True
else:
return False
def get_lattice(self):
return self._lattice
def get_mesh(self):
return self._mesh
def get_mesh_gruneisen(self):
return self._gruneisen_mesh
def plot(self, plt, thin_number=10):
fig = plt.figure()
fig.subplots_adjust(left=0.09, right=0.97, bottom=0.09, top=0.95)
plt1 = fig.add_subplot(2, 3, 1)
plt1.tick_params(axis='both', which='major', labelsize=10.5)
self._qha.plot_volume_temperature(plt=plt)
plt2 = fig.add_subplot(2, 3, 2)
plt2.tick_params(axis='both', which='major', labelsize=10.5)
self._qha.plot_thermal_expansion(plt=plt)
plt3 = fig.add_subplot(2, 3, 3)
plt3.tick_params(axis='both', which='major', labelsize=10.5)
self._qha.plot_bulk_modulus_temperature(plt=plt,
ylabel="Bulk modulus (GPa)")
plt4 = fig.add_subplot(2, 3, 4)
plt4.tick_params(axis='both', which='major', labelsize=10.5)
self._qha.plot_heat_capacity_P_polyfit(plt=plt, Z=self._Z)
plt5 = fig.add_subplot(2, 3, 5)
plt5.tick_params(axis='both', which='major', labelsize=10.5)
self._qha.plot_gibbs_temperature(plt=plt,
ylabel='Gibbs free energy (eV)')
plt6 = fig.add_subplot(2, 3, 6)
plt6.tick_params(axis='both', which='major', labelsize=10.5)
self._qha.plot_helmholtz_volume(thin_number=thin_number,
plt=plt,
ylabel='Free energy (eV)')
def save_figure(self, plt):
plt.savefig("qha.png")
def get_gibbs(structure,
db_file,
eos="vinet",
t_step=10,
t_min=0,
t_max=1000,
mesh=(20, 20, 20),
plot=False):
# other eos options: birch_murnaghan, murnaghan
# The physical units of V and T are \AA^3 and K, respectively.
# The unit of eV for Helmholtz and Gibbs energies,
# J/K/mol for C_V and entropy, GPa for for bulk modulus and pressure are used.
from phonopy import PhonopyQHA
phonon = get_phonopy(structure)
energies, volumes, force_constants = get_data(
db_file,
query={
"task_label": {
"$regex": "gibbs*"
},
"formula_pretty": structure.composition.reduced_formula
})
temperatures = []
free_energy = []
entropy = []
cv = []
for f in force_constants:
phonon.set_force_constants(-np.array(f))
phonon.set_mesh(list(mesh))
phonon.set_thermal_properties(t_step=t_step, t_min=t_min, t_max=t_max)
t, g, e, c = phonon.get_thermal_properties()
temperatures.append(t)
free_energy.append(g)
entropy.append(e)
cv.append(c)
phonopy_qha = PhonopyQHA(volumes,
energies,
eos=eos,
temperatures=temperatures[0],
free_energy=np.array(free_energy).T,
cv=np.array(cv).T,
entropy=np.array(entropy).T,
t_max=np.max(temperatures[0]))
# gibbs free energy
max_t_index = phonopy_qha._qha._max_t_index
G = phonopy_qha.get_gibbs_temperature()[:max_t_index]
T = phonopy_qha._qha._temperatures[:max_t_index]
if plot:
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
import matplotlib.pyplot as plt
plt.plot(T, G)
plt.savefig("Gibbs.pdf")
plt.show()
#phonopy_qha.plot_qha(thin_number=10, volume_temp_exp=None).show()
else:
return T, G
def test_QHA_Si():
"""Test of QHA calculation by Si."""
indices = list(range(11))
phonopy_qha = PhonopyQHA(
volumes=ev_vs_v[indices, 0],
electronic_energies=ev_vs_v[indices, 1],
eos="vinet",
temperatures=temperatures,
free_energy=fe_phonon[:, indices],
cv=cv[:, indices],
entropy=entropy[:, indices],
t_max=1000,
verbose=True,
)
t_indices = list(range(0, 101, 10))
# Bulk modulus without phonon
np.testing.assert_almost_equal(phonopy_qha.bulk_modulus,
0.5559133052877888)
# Thermal expansion
np.testing.assert_allclose(
[phonopy_qha.thermal_expansion[i] for i in t_indices],
thermal_expansion * 1e-6,
atol=1e-5,
)
# Helmholtz free energies vs volumes
np.testing.assert_allclose(phonopy_qha.helmholtz_volume[t_indices, 0],
helmholtz_volume,
atol=1e-5)
# Volume vs temperature
np.testing.assert_allclose(phonopy_qha.volume_temperature[t_indices],
volume_temperature,
atol=1e-5)
# Volume vs temperature
np.testing.assert_allclose(phonopy_qha.gibbs_temperature[t_indices],
gibbs_temperature,
atol=1e-5)
# Bulk modulus vs temperature
np.testing.assert_allclose(
phonopy_qha.bulk_modulus_temperature[t_indices],
bulkmodulus_temperature,
atol=1e-5,
)
# Cp vs temperature by numerical second derivative
np.testing.assert_allclose(
np.array(phonopy_qha.heat_capacity_P_numerical)[t_indices],
cp_temperature,
atol=0.01,
)
# Cp vs temperature by polynomial fittings of Cv and S
np.testing.assert_allclose(
np.array(phonopy_qha.heat_capacity_P_polyfit)[t_indices],
cp_temperature_polyfit,
atol=1e-5,
)
# Gruneisen parameters vs temperature
np.testing.assert_allclose(
np.array(phonopy_qha.gruneisen_temperature)[t_indices],
gruneisen_temperature,
atol=1e-5,
)
def __init__(self, args, load_data=False, verbose=False, tmin=None):
input_parameters = reading.read_parameters_from_input_file(args.input_file)
if 'structure_file_name_outcar' in input_parameters:
structure = reading.read_from_file_structure_outcar(input_parameters['structure_file_name_outcar'])
else:
structure = reading.read_from_file_structure_poscar(input_parameters['structure_file_name_poscar'])
structure.get_data_from_dict(input_parameters)
if 'supercell_phonon' in input_parameters:
supercell_phonon = input_parameters['supercell_phonon']
else:
supercell_phonon = np.identity(3)
structure.set_force_constants(get_force_constants_from_file(file_name=input_parameters['force_constants_file_name'],
fc_supercell=supercell_phonon))
if '_mesh_phonopy' in input_parameters:
mesh = input_parameters['_mesh_phonopy']
else:
mesh = [20, 20, 20] # Default
print ('mesh set to: {}'.format(mesh))
if 'bands' in input_parameters is None:
self._bands = structure.get_path_using_seek_path()
else:
self._bands = input_parameters['_band_ranges']
volumes, energies = read_v_e(args.ev, factor=1.0, volume_factor=1.0, pressure=args.pressure)
self._fc_fit = ForceConstantsFitting(structure,
files_temperature=args.ct_data,
files_volume=args.cv_data,
temperatures=args.temperatures,
volumes=volumes,
mesh=mesh,
ref_temperature=args.ref_temperature,
fitting_order=args.order,
tmin=tmin,
use_NAC=True)
if not load_data:
temperatures = self._fc_fit.get_temperature_range()
free_energy = []
entropy = []
cv = []
for v, e in zip(volumes, energies):
print ('Volume: {} Ang. Energy(U): {} eV'.format(v, e))
tp_data = self._fc_fit.get_thermal_properties(volume=v)
free_energy.append(tp_data[0])
entropy.append(tp_data[1])
cv.append(tp_data[2])
free_energy = np.array(free_energy).T
entropy = np.array(entropy).T
cv = np.array(cv).T
np.save('free_energy.npy', free_energy)
np.save('temperatures.npy', temperatures)
np.save('cv.npy', cv)
np.save('entropy.npy', entropy)
else:
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
self.phonopy_qha = PhonopyQHA(volumes,
energies,
eos="vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
# Write data files to disk
self.phonopy_qha.write_bulk_modulus_temperature()
self.phonopy_qha.write_gibbs_temperature()
self.phonopy_qha.write_heat_capacity_P_numerical()
self.phonopy_qha.write_gruneisen_temperature()
self.phonopy_qha.write_thermal_expansion()
self.phonopy_qha.write_helmholtz_volume()
self.phonopy_qha.write_volume_expansion()
self.phonopy_qha.write_volume_temperature()
if verbose:
self.phonopy_qha.plot_qha().show()
class QuasiparticlesQHA():
def __init__(self, args, load_data=False, verbose=False, tmin=None):
input_parameters = reading.read_parameters_from_input_file(args.input_file)
if 'structure_file_name_outcar' in input_parameters:
structure = reading.read_from_file_structure_outcar(input_parameters['structure_file_name_outcar'])
else:
structure = reading.read_from_file_structure_poscar(input_parameters['structure_file_name_poscar'])
structure.get_data_from_dict(input_parameters)
if 'supercell_phonon' in input_parameters:
supercell_phonon = input_parameters['supercell_phonon']
else:
supercell_phonon = np.identity(3)
structure.set_force_constants(get_force_constants_from_file(file_name=input_parameters['force_constants_file_name'],
fc_supercell=supercell_phonon))
if '_mesh_phonopy' in input_parameters:
mesh = input_parameters['_mesh_phonopy']
else:
mesh = [20, 20, 20] # Default
print ('mesh set to: {}'.format(mesh))
if 'bands' in input_parameters is None:
self._bands = structure.get_path_using_seek_path()
else:
self._bands = input_parameters['_band_ranges']
volumes, energies = read_v_e(args.ev, factor=1.0, volume_factor=1.0, pressure=args.pressure)
self._fc_fit = ForceConstantsFitting(structure,
files_temperature=args.ct_data,
files_volume=args.cv_data,
temperatures=args.temperatures,
volumes=volumes,
mesh=mesh,
ref_temperature=args.ref_temperature,
fitting_order=args.order,
tmin=tmin,
use_NAC=True)
if not load_data:
temperatures = self._fc_fit.get_temperature_range()
free_energy = []
entropy = []
cv = []
for v, e in zip(volumes, energies):
print ('Volume: {} Ang. Energy(U): {} eV'.format(v, e))
tp_data = self._fc_fit.get_thermal_properties(volume=v)
free_energy.append(tp_data[0])
entropy.append(tp_data[1])
cv.append(tp_data[2])
free_energy = np.array(free_energy).T
entropy = np.array(entropy).T
cv = np.array(cv).T
np.save('free_energy.npy', free_energy)
np.save('temperatures.npy', temperatures)
np.save('cv.npy', cv)
np.save('entropy.npy', entropy)
else:
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
self.phonopy_qha = PhonopyQHA(volumes,
energies,
eos="vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
# Write data files to disk
self.phonopy_qha.write_bulk_modulus_temperature()
self.phonopy_qha.write_gibbs_temperature()
self.phonopy_qha.write_heat_capacity_P_numerical()
self.phonopy_qha.write_gruneisen_temperature()
self.phonopy_qha.write_thermal_expansion()
self.phonopy_qha.write_helmholtz_volume()
self.phonopy_qha.write_volume_expansion()
self.phonopy_qha.write_volume_temperature()
if verbose:
self.phonopy_qha.plot_qha().show()
# Designed for test only
def volume_shift(self, volume_range=np.arange(-2.0, 2.0, 0.1)):
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
volumes = self.phonopy_qha._qha._volumes
energies = self.phonopy_qha._qha._electronic_energies
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
for i in volume_range:
volumesi = np.array(volumes) + i
print volumesi
phonopy_qha = PhonopyQHA(volumesi,
energies,
eos="vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
cp = phonopy_qha.get_heat_capacity_P_numerical()
import matplotlib.pyplot as plt
import matplotlib.colors as colors
cNorm = colors.Normalize(vmin=volume_range[0], vmax=volume_range[-1])
scalarMap = plt.cm.ScalarMappable(norm=cNorm, cmap=plt.cm.get_cmap('plasma'))
ax.plot(phonopy_qha._qha._temperatures[:-3], cp, label='{}'.format(i), color=scalarMap.to_rgba(i))
import matplotlib as mpl
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
mpl.colorbar.ColorbarBase(ax2, cmap=plt.cm.get_cmap('plasma'), norm=cNorm,
spacing='proportional', ticks=volume_range,
boundaries=None, format='%1i')
plt.show()
def plot_dos_gradient(self):
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
volumes = self.phonopy_qha.get_volume_temperature()
temperatures = self.fc_fit.get_temperature_range()
fig, ax = plt.subplots(1,1)
for t, v in zip(temperatures[::40], volumes[::20]):
print ('temperature: {} K'.format(t))
dos = self.fc_fit.get_dos(t, v)
cNorm = colors.Normalize(vmin=temperatures[0], vmax=temperatures[-1])
scalarMap = plt.cm.ScalarMappable(norm=cNorm, cmap=plt.cm.get_cmap('plasma'))
ax.plot(dos[0], dos[1], color=scalarMap.to_rgba(t))
plt.suptitle('Phonon density of states')
plt.xlabel('Frequency [THz]')
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
colorbar.ColorbarBase(ax2, cmap=plt.cm.get_cmap('plasma'), norm=cNorm,
spacing='proportional', ticks=temperatures[::40],
boundaries=None, format='%1i')
plt.show()
def plot_band_structure_gradient(self, tmin=300, tmax=1600, tstep=100):
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
def replace_list(text_string):
substitutions = {'GAMMA': u'$\Gamma$',
}
for item in substitutions.iteritems():
text_string = text_string.replace(item[0], item[1])
return text_string
volumes = self.phonopy_qha.get_volume_temperature()
cNorm = colors.Normalize(vmin=tmin, vmax=tmax)
fig, ax = plt.subplots(1,1)
for t in np.arange(tmin, tmax, tstep):
print ('temperature: {} K'.format(t))
v = self.get_volume_at_temperature(t)
scalarMap = plt.cm.ScalarMappable(norm=cNorm, cmap=plt.cm.get_cmap('plasma'))
band_data = self.fc_fit.get_band_structure(t, v, band_ranges=self._bands['ranges'])
for i, freq in enumerate(band_data[1]):
ax.plot(band_data[1][i], band_data[2][i], color=scalarMap.to_rgba(t))
# plt.axes().get_xaxis().set_visible(False)
#plt.axes().get_xaxis().set_ticks([])
plt.ylabel('Frequency [THz]')
plt.xlabel('Wave vector')
plt.xlim([0, band_data[1][-1][-1]])
plt.axhline(y=0, color='k', ls='dashed')
plt.suptitle('Phonon dispersion')
if 'labels' in self._bands:
plt.rcParams.update({'mathtext.default': 'regular'})
labels = self._bands['labels']
labels_e = []
x_labels = []
for i in range(len(band_data[1])):
if labels[i][0] == labels[i - 1][1]:
labels_e.append(replace_list(labels[i][0]))
else:
labels_e.append(
replace_list(labels[i - 1][1]) + '/' + replace_list(labels[i][0]))
x_labels.append(band_data[1][i][0])
x_labels.append(band_data[1][-1][-1])
labels_e.append(replace_list(labels[-1][1]))
labels_e[0] = replace_list(labels[0][0])
plt.xticks(x_labels, labels_e, rotation='horizontal')
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
colorbar.ColorbarBase(ax2, cmap=plt.cm.get_cmap('plasma'), norm=cNorm,
spacing='proportional', ticks=np.arange(tmin, tmax, tstep),
boundaries=None, format='%1i')
plt.show()
def get_volume_at_temperature(self, temperature):
temperatures = self.get_qha_temperatures()
volumes = self.phonopy_qha.get_volume_temperature()
volume = np.interp(temperature, temperatures, volumes)
return volume
def plot_band_structure_constant_pressure(self, temperature=300, external_data=None):
import matplotlib.pyplot as plt
def replace_list(text_string):
substitutions = {'GAMMA': u'$\Gamma$',
}
for item in substitutions.iteritems():
text_string = text_string.replace(item[0], item[1])
return text_string
volume = self.get_volume_at_temperature(temperature)
fig, ax = plt.subplots(1,1)
band_data = self.fc_fit.get_band_structure(temperature, volume, band_ranges=self._bands['ranges'])
for i, freq in enumerate(band_data[1]):
ax.plot(band_data[1][i], band_data[2][i], color='r')
#plt.axes().get_xaxis().set_ticks([])
plt.ylabel('Frequency [THz]')
plt.xlabel('Wave vector')
plt.xlim([0, band_data[1][-1][-1]])
plt.axhline(y=0, color='k', ls='dashed')
plt.suptitle('Phonon dispersion')
if 'labels' in self._bands:
plt.rcParams.update({'mathtext.default': 'regular'})
labels = self._bands['labels']
labels_e = []
x_labels = []
for i in range(len(band_data[1])):
if labels[i][0] == labels[i - 1][1]:
labels_e.append(replace_list(labels[i][0]))
else:
labels_e.append(
replace_list(labels[i - 1][1]) + '/' + replace_list(labels[i][0]))
x_labels.append(band_data[1][i][0])
x_labels.append(band_data[1][-1][-1])
labels_e.append(replace_list(labels[-1][1]))
labels_e[0] = replace_list(labels[0][0])
plt.xticks(x_labels, labels_e, rotation='horizontal')
ax.plot(external_data[:, 0], external_data[:, 1], 'o', color='b')
plt.show()
def get_qha_temperatures(self):
max_t_index = self.phonopy_qha._qha._get_max_t_index(self.phonopy_qha._qha._temperatures)
temperatures = self.phonopy_qha._qha._temperatures[:max_t_index]
return temperatures
def get_FC_constant_pressure(self):
temperatures = self.get_qha_temperatures()
volumes = self.phonopy_qha.get_volume_temperature()
for t, v in zip(temperatures[::20], volumes[::20]):
fc = self.fc_fit.get_total_force_constants(temperature=t, volume=v)
write_FORCE_CONSTANTS(fc.get_array(), filename='FC_{}'.format(t))
def get_total_shift_constant_pressure(self, qpoint=(0, 0, 0)):
qindex = self.fc_fit.qpoint_to_index(qpoint)
volumes = self.phonopy_qha.get_volume_temperature()
temperatures = self.get_qha_temperatures()
h_frequencies, ev = self.fc_fit.get_harmonic_frequencies_and_eigenvectors()
chk_shift_matrix = []
for v, t in zip(volumes, temperatures):
total_shifts = self.fc_fit.get_total_shifts(volume=v, temperature=t)
chk_shift_matrix.append(total_shifts)
chk_shift_matrix = np.array(chk_shift_matrix).T
return chk_shift_matrix[:, qindex]
def plot_total_shift_constant_pressure(self, qpoint=(0, 0, 0), branch=None):
import matplotlib.pyplot as plt
temperatures = self.get_qha_temperatures()
chk_shift_matrix = self.get_total_shift_constant_pressure(qpoint=qpoint)
plt.suptitle('Total frequency shift at wave vector={} (relative to {} K)'.format(qpoint, self.fc_fit.ref_temperature))
plt.xlabel('Temperature [K]')
plt.ylabel('Frequency shift [THz]')
if branch is None:
plt.plot(temperatures, chk_shift_matrix.T, '-')
else:
plt.title('Branch {}'.format(branch))
plt.plot(temperatures, chk_shift_matrix[branch].T, '-')
plt.show()
@property
def fc_fit(self):
return self._fc_fit
def calculate_qha_inline(**kwargs):
from phonopy import PhonopyQHA
import numpy as np
# thermal_properties_list = [key for key, value in kwargs.items() if 'thermal_properties' in key.lower()]
# optimized_structure_data_list = [key for key, value in kwargs.items() if 'optimized_structure_data' in key.lower()]
structure_list = [
key for key, value in kwargs.items()
if 'final_structure' in key.lower()
]
volumes = []
electronic_energies = []
fe_phonon = []
entropy = []
cv = []
for i in range(len(structure_list)):
# volumes.append(kwargs.pop(key).get_cell_volume())
volumes.append(
kwargs.pop('final_structure_{}'.format(i)).get_cell_volume())
electronic_energies.append(
kwargs.pop('optimized_structure_data_{}'.format(i)).dict.energy)
thermal_properties = kwargs.pop('thermal_properties_{}'.format(i))
temperatures = thermal_properties.get_array('temperature')
fe_phonon.append(thermal_properties.get_array('free_energy'))
entropy.append(thermal_properties.get_array('entropy'))
cv.append(thermal_properties.get_array('cv'))
sort_index = np.argsort(volumes)
temperatures = np.array(temperatures)
volumes = np.array(volumes)[sort_index]
electronic_energies = np.array(electronic_energies)[sort_index]
fe_phonon = np.array(fe_phonon).T[:, sort_index]
entropy = np.array(entropy).T[:, sort_index]
cv = np.array(cv).T[:, sort_index]
print volumes
print electronic_energies
print temperatures
print fe_phonon
print cv
print entropy
# Calculate QHA
phonopy_qha = PhonopyQHA(
volumes,
electronic_energies,
eos="vinet",
temperatures=temperatures,
free_energy=fe_phonon,
cv=cv,
entropy=entropy,
# t_max=options.t_max,
verbose=True)
try:
qha_output = ArrayData()
qha_output.set_array('temperature', np.array([1, 2, 3, 4]))
qha_output.store()
except:
print qha_output
exit()
# Get data
qha_temperatures = phonopy_qha._qha._temperatures[:phonopy_qha._qha.
_max_t_index]
helmholtz_volume = phonopy_qha.get_helmholtz_volume()
thermal_expansion = phonopy_qha.get_thermal_expansion()
volume_temperature = phonopy_qha.get_volume_temperature()
heat_capacity_P_numerical = phonopy_qha.get_heat_capacity_P_numerical()
volume_expansion = phonopy_qha.get_volume_expansion()
gibbs_temperature = phonopy_qha.get_gibbs_temperature()
qha_output = ArrayData()
qha_output.set_array('temperature', np.array(qha_temperatures))
qha_output.set_array('helmholtz_volume', np.array(helmholtz_volume))
qha_output.set_array('thermal_expansion', np.array(thermal_expansion))
qha_output.set_array('volume_temperature', np.array(volume_temperature))
qha_output.set_array('heat_capacity_P_numerical',
np.array(heat_capacity_P_numerical))
qha_output.set_array('volume_expansion', np.array(volume_expansion))
qha_output.set_array('gibbs_temperature', np.array(gibbs_temperature))
qha_output.store()
return {'qha_data': qha_output}
def _calculate_quasiharmonic_phonon(self):
energies = []
volumes = []
phonons = []
T = []
F = []
S = []
Cv = []
for i, task in enumerate(self._tasks):
energies.append(task.get_energy())
volumes.append(task.get_cell().get_volume())
if self._sampling_mesh is not None:
phonon = task.get_phonon()
phonon.set_mesh(self._sampling_mesh)
phonon.set_thermal_properties(
t_step=self._t_step, t_max=self._t_max + self._t_step * 2.5, t_min=self._t_min
)
(temperatures, free_energies, entropies, heat_capacities) = phonon.get_thermal_properties()
T.append(temperatures)
F.append(free_energies)
S.append(entropies)
Cv.append(heat_capacities)
phonon.write_yaml_thermal_properties("thermal_properties-%02d.yaml" % i)
if self._sampling_mesh:
qha = PhonopyQHA(
volumes,
energies,
temperatures=T[0],
free_energy=np.transpose(F),
cv=np.transpose(Cv),
entropy=np.transpose(S),
t_max=self._t_max,
verbose=False,
)
qha.write_helmholtz_volume()
qha.write_volume_temperature()
qha.write_thermal_expansion()
qha.write_volume_expansion()
qha.write_gibbs_temperature()
qha.write_bulk_modulus_temperature()
qha.write_heat_capacity_P_numerical()
qha.write_heat_capacity_P_polyfit()
qha.write_gruneisen_temperature()
w = open("e-v.dat", "w")
w.write("# cell volume energy of cell other than phonon\n")
for e, v in zip(energies, volumes):
w.write("%20.13f %20.13f\n" % (v, e))
self._quasiharmonic_phonon = None
def _calculate_quasiharmonic_phonon(self):
energies = []
volumes = []
phonons = []
T = []
F = []
S = []
Cv = []
for i, task in enumerate(self._tasks):
energies.append(task.get_energy())
volumes.append(task.get_cell().get_volume())
if self._sampling_mesh is not None:
phonon = task.get_phonon()
phonon.set_mesh(self._sampling_mesh)
phonon.set_thermal_properties(t_step=self._t_step,
t_max=self._t_max +
self._t_step * 2.5,
t_min=self._t_min)
(temperatures, free_energies, entropies,
heat_capacities) = phonon.get_thermal_properties()
T.append(temperatures)
F.append(free_energies)
S.append(entropies)
Cv.append(heat_capacities)
phonon.write_yaml_thermal_properties(
"thermal_properties-%02d.yaml" % i)
if self._sampling_mesh:
qha = PhonopyQHA(volumes,
energies,
temperatures=T[0],
free_energy=np.transpose(F),
cv=np.transpose(Cv),
entropy=np.transpose(S),
t_max=self._t_max,
verbose=False)
qha.write_helmholtz_volume()
qha.write_volume_temperature()
qha.write_thermal_expansion()
qha.write_volume_expansion()
qha.write_gibbs_temperature()
qha.write_bulk_modulus_temperature()
qha.write_heat_capacity_P_numerical()
qha.write_heat_capacity_P_polyfit()
qha.write_gruneisen_temperature()
w = open("e-v.dat", 'w')
w.write("# cell volume energy of cell other than phonon\n")
for e, v in zip(energies, volumes):
w.write("%20.13f %20.13f\n" % (v, e))
self._quasiharmonic_phonon = None
cv = []
fe = []
for index in range(-5,6):
filename = "thermal_properties.yaml-%d" % index
print "Reading", filename
thermal_properties = yaml.load(open(filename),
Loader=Loader)['thermal_properties']
temperatures = [v['temperature'] for v in thermal_properties]
cv.append([v['heat_capacity'] for v in thermal_properties])
entropy.append([v['entropy'] for v in thermal_properties])
fe.append([v['free_energy'] for v in thermal_properties])
qha = PhonopyQHA(volumes,
energies,
temperatures=temperatures,
free_energy=np.transpose(fe),
cv=np.transpose(cv),
entropy=np.transpose(entropy),
t_max=400,
verbose=True)
# qha.plot_helmholtz_volume().show()
# qha.plot_volume_temperature().show()
# qha.plot_thermal_expansion().show()
# plot = qha.plot_volume_expansion()
# if plot:
# plot.show()
# qha.plot_gibbs_temperature().show()
# qha.plot_bulk_modulus_temperature().show()
# qha.plot_heat_capacity_P_numerical().show()
# qha.plot_heat_capacity_P_polyfit().show()
qha.plot_gruneisen_temperature().show()
volumes = np.array(volumes)[sort_index]
electronic_energies = np.array(electronic_energies)[sort_index]
temperatures = np.array(temperatures)
fe_phonon = np.array(fe_phonon).T[:, sort_index]
entropy = np.array(entropy).T[:, sort_index]
cv = np.array(cv).T[:, sort_index]
opt = np.argmin(electronic_energies)
# Calculate QHA
phonopy_qha = PhonopyQHA(
np.array(volumes),
np.array(electronic_energies),
eos="vinet",
temperatures=np.array(temperatures),
free_energy=np.array(fe_phonon),
cv=np.array(cv),
entropy=np.array(entropy),
# t_max=options.t_max,
verbose=False)
# Get data
qha_temperatures = phonopy_qha._qha._temperatures[:phonopy_qha._qha.
_max_t_index]
helmholtz_volume = phonopy_qha.get_helmholtz_volume()
thermal_expansion = phonopy_qha.get_thermal_expansion()
volume_temperature = phonopy_qha.get_volume_temperature()
heat_capacity_P_numerical = phonopy_qha.get_heat_capacity_P_numerical()
volume_expansion = phonopy_qha.get_volume_expansion()
gibbs_temperature = phonopy_qha.get_gibbs_temperature()
def calculate_qha_inline(**kwargs):
from phonopy import PhonopyQHA
from phonopy.structure.atoms import Atoms as PhonopyAtoms
import numpy as np
# structures = kwargs.pop('structures')
# optimized_data = kwargs.pop('optimized_data')
# thermodyamic_properties = kwargs.pop('thermodyamic_properties')
entropy = []
cv = []
# volumes = []
fe_phonon = []
temperatures = None
# structures = [key for key, value in kwargs.items() if 'structure' in key.lower()]
# for key in structures:
# volumes.append(kwargs.pop(key).get_cell_volume())
volumes = [
value.get_cell_volume() for key, value in kwargs.items()
if 'structure' in key.lower()
]
electronic_energies = [
value.get_dict()['energy'] for key, value in kwargs.items()
if 'optimized_data' in key.lower()
]
thermal_properties_list = [
key for key, value in kwargs.items()
if 'thermal_properties' in key.lower()
]
for key in thermal_properties_list:
thermal_properties = kwargs[key]
fe_phonon.append(thermal_properties.get_array('free_energy'))
entropy.append(thermal_properties.get_array('entropy'))
cv.append(thermal_properties.get_array('cv'))
temperatures = thermal_properties.get_array('temperature')
# Arrange data sorted by volume and transform them to numpy array
sort_index = np.argsort(volumes)
volumes = np.array(volumes)[sort_index]
electronic_energies = np.array(electronic_energies)[sort_index]
temperatures = np.array(temperatures)
fe_phonon = np.array(fe_phonon).T[:, sort_index]
entropy = np.array(entropy).T[:, sort_index]
cv = np.array(cv).T[:, sort_index]
opt = np.argmin(electronic_energies)
# Check minimum energy volume is within the data
if np.ma.masked_less_equal(volumes, volumes[opt]).mask.all():
print('higher volume structures are necessary to compute')
exit()
if np.ma.masked_greater_equal(volumes, volumes[opt]).mask.all():
print('Lower volume structures are necessary to compute')
exit()
# print volumes.shape
# print electronic_energies.shape
# print temperatures.shape
# print fe_phonon.shape
# print cv.shape
# print entropy.shape
qha_output = ArrayData()
qha_output.set_array('volumes', volumes)
qha_output.set_array('electronic_energies', electronic_energies)
qha_output.set_array('temperatures', temperatures)
qha_output.set_array('fe_phonon', fe_phonon)
qha_output.set_array('cv', cv)
qha_output.set_array('entropy', entropy)
# Calculate QHA
phonopy_qha = PhonopyQHA(
np.array(volumes),
np.array(electronic_energies),
eos="vinet",
temperatures=np.array(temperatures),
free_energy=np.array(fe_phonon),
cv=np.array(cv),
entropy=np.array(entropy),
# t_max=options.t_max,
verbose=False)
# print phonopy_qha.get_gibbs_temperature()
qha_output = ArrayData()
qha_output.set_array('volumes', volumes)
qha_output.set_array('electronic_energies', electronic_energies)
qha_output.set_array('temperatures', temperatures)
qha_output.set_array('fe_phonon', fe_phonon)
qha_output.set_array('cv', cv)
qha_output.set_array('entropy', entropy)
return {'qha_output': qha_output}
#Get data
helmholtz_volume = phonopy_qha.get_helmholtz_volume()
thermal_expansion = phonopy_qha.get_thermal_expansion()
volume_temperature = phonopy_qha.get_volume_temperature()
heat_capacity_P_numerical = phonopy_qha.get_heat_capacity_P_numerical()
volume_expansion = phonopy_qha.get_volume_expansion()
gibbs_temperature = phonopy_qha.get_gibbs_temperature()
qha_output = ArrayData()
# qha_output.set_array('temperature', temperatures)
# qha_output.set_array('helmholtz_volume', np.array(helmholtz_volume))
# qha_output.set_array('thermal_expansion', np.array(thermal_expansion))
# qha_output.set_array('volume_temperature', np.array(volume_temperature))
# qha_output.set_array('heat_capacity_P_numerical', np.array(heat_capacity_P_numerical))
# qha_output.set_array('volume_expansion', np.array(volume_expansion))
# qha_output.set_array('gibbs_temperature', np.array(gibbs_temperature))
# qha_output.store()
return {'qha_output': qha_output}
def __init__(self, args, load_data=False, verbose=False, tmin=None):
input_parameters = reading.read_parameters_from_input_file(
args.input_file)
if 'structure_file_name_outcar' in input_parameters:
structure = reading.read_from_file_structure_outcar(
input_parameters['structure_file_name_outcar'])
else:
structure = reading.read_from_file_structure_poscar(
input_parameters['structure_file_name_poscar'])
structure.get_data_from_dict(input_parameters)
if 'supercell_phonon' in input_parameters:
supercell_phonon = input_parameters['supercell_phonon']
else:
supercell_phonon = np.identity(3)
structure.set_force_constants(
get_force_constants_from_file(
file_name=input_parameters['force_constants_file_name'],
fc_supercell=supercell_phonon))
if '_mesh_phonopy' in input_parameters:
mesh = input_parameters['_mesh_phonopy']
else:
mesh = [20, 20, 20] # Default
print('mesh set to: {}'.format(mesh))
if 'bands' in input_parameters is None:
self._bands = structure.get_path_using_seek_path()
else:
self._bands = input_parameters['_band_ranges']
volumes, energies = read_v_e(args.ev,
factor=1.0,
volume_factor=1.0,
pressure=args.pressure)
self._fc_fit = ForceConstantsFitting(
structure,
files_temperature=args.ct_data,
files_volume=args.cv_data,
temperatures=args.temperatures,
volumes=volumes,
mesh=mesh,
ref_temperature=args.ref_temperature,
fitting_order=args.order,
tmin=tmin,
use_NAC=True)
if not load_data:
temperatures = self._fc_fit.get_temperature_range()
free_energy = []
entropy = []
cv = []
for v, e in zip(volumes, energies):
print('Volume: {} Ang. Energy(U): {} eV'.format(v, e))
tp_data = self._fc_fit.get_thermal_properties(volume=v)
free_energy.append(tp_data[0])
entropy.append(tp_data[1])
cv.append(tp_data[2])
free_energy = np.array(free_energy).T
entropy = np.array(entropy).T
cv = np.array(cv).T
np.save('free_energy.npy', free_energy)
np.save('temperatures.npy', temperatures)
np.save('cv.npy', cv)
np.save('entropy.npy', entropy)
else:
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
self.phonopy_qha = PhonopyQHA(
volumes,
energies,
eos="vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
# Write data files to disk
self.phonopy_qha.write_bulk_modulus_temperature()
self.phonopy_qha.write_gibbs_temperature()
self.phonopy_qha.write_heat_capacity_P_numerical()
self.phonopy_qha.write_gruneisen_temperature()
self.phonopy_qha.write_thermal_expansion()
self.phonopy_qha.write_helmholtz_volume()
self.phonopy_qha.write_volume_expansion()
self.phonopy_qha.write_volume_temperature()
if verbose:
self.phonopy_qha.plot_qha().show()
class QuasiparticlesQHA():
def __init__(self, args, load_data=False, verbose=False, tmin=None):
input_parameters = reading.read_parameters_from_input_file(
args.input_file)
if 'structure_file_name_outcar' in input_parameters:
structure = reading.read_from_file_structure_outcar(
input_parameters['structure_file_name_outcar'])
else:
structure = reading.read_from_file_structure_poscar(
input_parameters['structure_file_name_poscar'])
structure.get_data_from_dict(input_parameters)
if 'supercell_phonon' in input_parameters:
supercell_phonon = input_parameters['supercell_phonon']
else:
supercell_phonon = np.identity(3)
structure.set_force_constants(
get_force_constants_from_file(
file_name=input_parameters['force_constants_file_name'],
fc_supercell=supercell_phonon))
if '_mesh_phonopy' in input_parameters:
mesh = input_parameters['_mesh_phonopy']
else:
mesh = [20, 20, 20] # Default
print('mesh set to: {}'.format(mesh))
if 'bands' in input_parameters is None:
self._bands = structure.get_path_using_seek_path()
else:
self._bands = input_parameters['_band_ranges']
volumes, energies = read_v_e(args.ev,
factor=1.0,
volume_factor=1.0,
pressure=args.pressure)
self._fc_fit = ForceConstantsFitting(
structure,
files_temperature=args.ct_data,
files_volume=args.cv_data,
temperatures=args.temperatures,
volumes=volumes,
mesh=mesh,
ref_temperature=args.ref_temperature,
fitting_order=args.order,
tmin=tmin,
use_NAC=True)
if not load_data:
temperatures = self._fc_fit.get_temperature_range()
free_energy = []
entropy = []
cv = []
for v, e in zip(volumes, energies):
print('Volume: {} Ang. Energy(U): {} eV'.format(v, e))
tp_data = self._fc_fit.get_thermal_properties(volume=v)
free_energy.append(tp_data[0])
entropy.append(tp_data[1])
cv.append(tp_data[2])
free_energy = np.array(free_energy).T
entropy = np.array(entropy).T
cv = np.array(cv).T
np.save('free_energy.npy', free_energy)
np.save('temperatures.npy', temperatures)
np.save('cv.npy', cv)
np.save('entropy.npy', entropy)
else:
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
self.phonopy_qha = PhonopyQHA(
volumes,
energies,
eos="vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
# Write data files to disk
self.phonopy_qha.write_bulk_modulus_temperature()
self.phonopy_qha.write_gibbs_temperature()
self.phonopy_qha.write_heat_capacity_P_numerical()
self.phonopy_qha.write_gruneisen_temperature()
self.phonopy_qha.write_thermal_expansion()
self.phonopy_qha.write_helmholtz_volume()
self.phonopy_qha.write_volume_expansion()
self.phonopy_qha.write_volume_temperature()
if verbose:
self.phonopy_qha.plot_qha().show()
# Designed for test only
def volume_shift(self, volume_range=np.arange(-2.0, 2.0, 0.1)):
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
volumes = self.phonopy_qha._qha._volumes
energies = self.phonopy_qha._qha._electronic_energies
free_energy = np.load('free_energy.npy')
temperatures = np.load('temperatures.npy')
cv = np.load('cv.npy')
entropy = np.load('entropy.npy')
for i in volume_range:
volumesi = np.array(volumes) + i
print volumesi
phonopy_qha = PhonopyQHA(
volumesi,
energies,
eos=
"vinet", # options: 'vinet', 'murnaghan' or 'birch_murnaghan'
temperatures=temperatures,
free_energy=free_energy,
cv=cv,
entropy=entropy,
t_max=self.fc_fit.get_temperature_range()[-1],
verbose=False)
cp = phonopy_qha.get_heat_capacity_P_numerical()
import matplotlib.pyplot as plt
import matplotlib.colors as colors
cNorm = colors.Normalize(vmin=volume_range[0],
vmax=volume_range[-1])
scalarMap = plt.cm.ScalarMappable(norm=cNorm,
cmap=plt.cm.get_cmap('plasma'))
ax.plot(phonopy_qha._qha._temperatures[:-3],
cp,
label='{}'.format(i),
color=scalarMap.to_rgba(i))
import matplotlib as mpl
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
mpl.colorbar.ColorbarBase(ax2,
cmap=plt.cm.get_cmap('plasma'),
norm=cNorm,
spacing='proportional',
ticks=volume_range,
boundaries=None,
format='%1i')
plt.show()
def plot_dos_gradient(self):
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
volumes = self.phonopy_qha.get_volume_temperature()
temperatures = self.fc_fit.get_temperature_range()
fig, ax = plt.subplots(1, 1)
for t, v in zip(temperatures[::40], volumes[::20]):
print('temperature: {} K'.format(t))
dos = self.fc_fit.get_dos(t, v)
cNorm = colors.Normalize(vmin=temperatures[0],
vmax=temperatures[-1])
scalarMap = plt.cm.ScalarMappable(norm=cNorm,
cmap=plt.cm.get_cmap('plasma'))
ax.plot(dos[0], dos[1], color=scalarMap.to_rgba(t))
plt.suptitle('Phonon density of states')
plt.xlabel('Frequency [THz]')
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
colorbar.ColorbarBase(ax2,
cmap=plt.cm.get_cmap('plasma'),
norm=cNorm,
spacing='proportional',
ticks=temperatures[::40],
boundaries=None,
format='%1i')
plt.show()
def plot_band_structure_gradient(self, tmin=300, tmax=1600, tstep=100):
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
def replace_list(text_string):
substitutions = {
'GAMMA': u'$\Gamma$',
}
for item in substitutions.iteritems():
text_string = text_string.replace(item[0], item[1])
return text_string
volumes = self.phonopy_qha.get_volume_temperature()
cNorm = colors.Normalize(vmin=tmin, vmax=tmax)
fig, ax = plt.subplots(1, 1)
for t in np.arange(tmin, tmax, tstep):
print('temperature: {} K'.format(t))
v = self.get_volume_at_temperature(t)
scalarMap = plt.cm.ScalarMappable(norm=cNorm,
cmap=plt.cm.get_cmap('plasma'))
band_data = self.fc_fit.get_band_structure(
t, v, band_ranges=self._bands['ranges'])
for i, freq in enumerate(band_data[1]):
ax.plot(band_data[1][i],
band_data[2][i],
color=scalarMap.to_rgba(t))
# plt.axes().get_xaxis().set_visible(False)
#plt.axes().get_xaxis().set_ticks([])
plt.ylabel('Frequency [THz]')
plt.xlabel('Wave vector')
plt.xlim([0, band_data[1][-1][-1]])
plt.axhline(y=0, color='k', ls='dashed')
plt.suptitle('Phonon dispersion')
if 'labels' in self._bands:
plt.rcParams.update({'mathtext.default': 'regular'})
labels = self._bands['labels']
labels_e = []
x_labels = []
for i in range(len(band_data[1])):
if labels[i][0] == labels[i - 1][1]:
labels_e.append(replace_list(labels[i][0]))
else:
labels_e.append(
replace_list(labels[i - 1][1]) + '/' +
replace_list(labels[i][0]))
x_labels.append(band_data[1][i][0])
x_labels.append(band_data[1][-1][-1])
labels_e.append(replace_list(labels[-1][1]))
labels_e[0] = replace_list(labels[0][0])
plt.xticks(x_labels, labels_e, rotation='horizontal')
ax2 = fig.add_axes([0.93, 0.1, 0.02, 0.8])
colorbar.ColorbarBase(ax2,
cmap=plt.cm.get_cmap('plasma'),
norm=cNorm,
spacing='proportional',
ticks=np.arange(tmin, tmax, tstep),
boundaries=None,
format='%1i')
plt.show()
def get_volume_at_temperature(self, temperature):
temperatures = self.get_qha_temperatures()
volumes = self.phonopy_qha.get_volume_temperature()
volume = np.interp(temperature, temperatures, volumes)
return volume
def plot_band_structure_constant_pressure(self,
temperature=300,
external_data=None):
import matplotlib.pyplot as plt
def replace_list(text_string):
substitutions = {
'GAMMA': u'$\Gamma$',
}
for item in substitutions.iteritems():
text_string = text_string.replace(item[0], item[1])
return text_string
volume = self.get_volume_at_temperature(temperature)
fig, ax = plt.subplots(1, 1)
band_data = self.fc_fit.get_band_structure(
temperature, volume, band_ranges=self._bands['ranges'])
for i, freq in enumerate(band_data[1]):
ax.plot(band_data[1][i], band_data[2][i], color='r')
#plt.axes().get_xaxis().set_ticks([])
plt.ylabel('Frequency [THz]')
plt.xlabel('Wave vector')
plt.xlim([0, band_data[1][-1][-1]])
plt.axhline(y=0, color='k', ls='dashed')
plt.suptitle('Phonon dispersion')
if 'labels' in self._bands:
plt.rcParams.update({'mathtext.default': 'regular'})
labels = self._bands['labels']
labels_e = []
x_labels = []
for i in range(len(band_data[1])):
if labels[i][0] == labels[i - 1][1]:
labels_e.append(replace_list(labels[i][0]))
else:
labels_e.append(
replace_list(labels[i - 1][1]) + '/' +
replace_list(labels[i][0]))
x_labels.append(band_data[1][i][0])
x_labels.append(band_data[1][-1][-1])
labels_e.append(replace_list(labels[-1][1]))
labels_e[0] = replace_list(labels[0][0])
plt.xticks(x_labels, labels_e, rotation='horizontal')
ax.plot(external_data[:, 0], external_data[:, 1], 'o', color='b')
plt.show()
def get_qha_temperatures(self):
max_t_index = self.phonopy_qha._qha._get_max_t_index(
self.phonopy_qha._qha._temperatures)
temperatures = self.phonopy_qha._qha._temperatures[:max_t_index]
return temperatures
def get_FC_constant_pressure(self):
temperatures = self.get_qha_temperatures()
volumes = self.phonopy_qha.get_volume_temperature()
for t, v in zip(temperatures[::20], volumes[::20]):
fc = self.fc_fit.get_total_force_constants(temperature=t, volume=v)
write_FORCE_CONSTANTS(fc.get_array(), filename='FC_{}'.format(t))
def get_total_shift_constant_pressure(self, qpoint=(0, 0, 0)):
qindex = self.fc_fit.qpoint_to_index(qpoint)
volumes = self.phonopy_qha.get_volume_temperature()
temperatures = self.get_qha_temperatures()
h_frequencies, ev = self.fc_fit.get_harmonic_frequencies_and_eigenvectors(
)
chk_shift_matrix = []
for v, t in zip(volumes, temperatures):
total_shifts = self.fc_fit.get_total_shifts(volume=v,
temperature=t)
chk_shift_matrix.append(total_shifts)
chk_shift_matrix = np.array(chk_shift_matrix).T
return chk_shift_matrix[:, qindex]
def plot_total_shift_constant_pressure(self,
qpoint=(0, 0, 0),
branch=None):
import matplotlib.pyplot as plt
temperatures = self.get_qha_temperatures()
chk_shift_matrix = self.get_total_shift_constant_pressure(
qpoint=qpoint)
plt.suptitle(
'Total frequency shift at wave vector={} (relative to {} K)'.
format(qpoint, self.fc_fit.ref_temperature))
plt.xlabel('Temperature [K]')
plt.ylabel('Frequency shift [THz]')
if branch is None:
plt.plot(temperatures, chk_shift_matrix.T, '-')
else:
plt.title('Branch {}'.format(branch))
plt.plot(temperatures, chk_shift_matrix[branch].T, '-')
plt.show()
@property
def fc_fit(self):
return self._fc_fit
def calculate_qha_inline(**kwargs):
from phonopy import PhonopyQHA
from phonon_common import get_helmholtz_volume_from_phonopy_qha
import numpy as np
# thermal_properties_list = [key for key, value in kwargs.items() if 'thermal_properties' in key.lower()]
# optimized_structure_data_list = [key for key, value in kwargs.items() if 'optimized_structure_data' in key.lower()]
structure_list = [
key for key, value in kwargs.items()
if 'final_structure' in key.lower()
]
volumes = []
electronic_energies = []
fe_phonon = []
entropy = []
cv = []
for i in range(len(structure_list)):
# volumes.append(kwargs.pop(key).get_cell_volume())
volumes.append(
kwargs.pop('final_structure_{}'.format(i)).get_cell_volume())
electronic_energies.append(
kwargs.pop('optimized_structure_data_{}'.format(i)).dict.energy)
thermal_properties = kwargs.pop('thermal_properties_{}'.format(i))
temperatures = thermal_properties.get_array('temperature')
fe_phonon.append(thermal_properties.get_array('free_energy'))
entropy.append(thermal_properties.get_array('entropy'))
cv.append(thermal_properties.get_array('cv'))
sort_index = np.argsort(volumes)
temperatures = np.array(temperatures)
volumes = np.array(volumes)[sort_index]
electronic_energies = np.array(electronic_energies)[sort_index]
fe_phonon = np.array(fe_phonon).T[:, sort_index]
entropy = np.array(entropy).T[:, sort_index]
cv = np.array(cv).T[:, sort_index]
# Calculate QHA
phonopy_qha = PhonopyQHA(
np.array(volumes),
np.array(electronic_energies),
eos="vinet",
temperatures=np.array(temperatures),
free_energy=np.array(fe_phonon),
cv=np.array(cv),
entropy=np.array(entropy),
# t_max=options.t_max,
verbose=False)
# Get data
free_energy_volume_fitting = get_helmholtz_volume_from_phonopy_qha(
phonopy_qha)
qha_temperatures = phonopy_qha._qha._temperatures[:phonopy_qha._qha.
_max_t_index]
helmholtz_volume = phonopy_qha.get_helmholtz_volume()
thermal_expansion = phonopy_qha.get_thermal_expansion()
volume_temperature = phonopy_qha.get_volume_temperature()
heat_capacity_P_numerical = phonopy_qha.get_heat_capacity_P_numerical()
volume_expansion = phonopy_qha.get_volume_expansion()
gibbs_temperature = phonopy_qha.get_gibbs_temperature()
qha_output = ArrayData()
qha_output.set_array('temperatures', np.array(qha_temperatures))
#qha_output.set_array('helmholtz_volume', np.array(helmholtz_volume))
qha_output.set_array('thermal_expansion', np.array(thermal_expansion))
qha_output.set_array('volume_temperature', np.array(volume_temperature))
qha_output.set_array('heat_capacity_P_numerical',
np.array(heat_capacity_P_numerical))
qha_output.set_array('volume_expansion', np.array(volume_expansion))
qha_output.set_array('gibbs_temperature', np.array(gibbs_temperature))
qha_output.set_array('helmholtz_volume_points',
np.array(free_energy_volume_fitting['points']))
qha_output.set_array('helmholtz_volume_fit',
np.array(free_energy_volume_fitting['fit']))
qha_output.set_array('helmholtz_volume_minimum',
np.array(free_energy_volume_fitting['minimum']))
return {'qha_output': qha_output}