def get_dos(self, partial_dos=False, npts_mu=10000, T=None):
"""
Return a Dos object interpolating bands
Args:
partial_dos: if True, projections will be interpolated as well
and partial doses will be return. Projections must be available
in the loader.
npts_mu: number of energy points of the Dos
T: parameter used to smooth the Dos
"""
spin = self.data.spin if isinstance(self.data.spin, int) else 1
energies, densities, vvdos, cdos = BL.BTPDOS(self.eband,
self.vvband,
npts=npts_mu)
if T is not None:
densities = BL.smoothen_DOS(energies, densities, T)
tdos = Dos(self.efermi / units.eV, energies / units.eV,
{Spin(spin): densities})
if partial_dos:
tdos = self.get_partial_doses(tdos=tdos, npts_mu=npts_mu, T=T)
return tdos
def __init__(self, BztInterpolator, temp_r=np.arange(100, 1400, 100),
doping=10. ** np.arange(16, 23), npts_mu=4000, CRTA=1e-14, margin=None):
self.CRTA = CRTA
self.temp_r = temp_r
self.doping = doping
self.dosweight = BztInterpolator.data.dosweight
lattvec = BztInterpolator.data.get_lattvec()
self.epsilon, self.dos, self.vvdos, self.cdos = BL.BTPDOS(BztInterpolator.eband, BztInterpolator.vvband,
npts=npts_mu, cband=BztInterpolator.cband)
if margin is None:
margin = 9. * units.BOLTZMANN * temp_r.max()
mur_indices = np.logical_and(self.epsilon > self.epsilon.min() + margin,
self.epsilon < self.epsilon.max() - margin)
self.mu_r = self.epsilon[mur_indices]
N, L0, L1, L2, Lm11 = BL.fermiintegrals(
self.epsilon, self.dos, self.vvdos, mur=self.mu_r, Tr=temp_r, dosweight=self.dosweight, cdos=self.cdos)
self.efermi = BztInterpolator.data.fermi / units.eV
self.mu_r_eV = self.mu_r / units.eV - self.efermi
self.nelect = BztInterpolator.data.nelect
self.volume = BztInterpolator.data.get_volume()
# Compute the Onsager coefficients from those Fermi integrals
self.Conductivity_mu, self.Seebeck_mu, self.Kappa_mu, Hall_mu = BL.calc_Onsager_coefficients(L0, L1, L2,
self.mu_r, temp_r,
self.volume,
Lm11=Lm11)
# Common properties rescaling
self.Conductivity_mu *= CRTA # S / m
self.Seebeck_mu *= 1e6 # microvolt / K
self.Kappa_mu *= CRTA # W / (m K)
self.Hall_carrier_conc_trace_mu = units.Coulomb * 1e-6 / (np.abs(Hall_mu[:, :, 0, 1, 2] +
Hall_mu[:, :, 2, 0, 1] +
Hall_mu[:, :, 1, 2, 0]) / 3)
self.Carrier_conc_mu = (N + self.nelect) / (self.volume / (units.Meter / 100.) ** 3)
# Derived properties
cond_eff_mass = np.zeros((len(self.temp_r), len(self.mu_r), 3, 3))
for t in range(len(self.temp_r)):
for i in range(len(self.mu_r)):
try:
cond_eff_mass[t, i] = np.linalg.inv(self.Conductivity_mu[t, i]) * self.Carrier_conc_mu[
t, i] * units.qe_SI ** 2 / units.me_SI * 1e6
except np.linalg.LinAlgError:
pass
self.Effective_mass_mu = cond_eff_mass * CRTA
self.Power_Factor_mu = (self.Seebeck_mu @ self.Seebeck_mu) @ self.Conductivity_mu
self.Power_Factor_mu *= 1e-9 # milliWatt / m / K**2
def get_dos(self,
partial_dos=False,
npts_mu=10000,
T=None,
progress=False):
"""
Return a Dos object interpolating bands
Args:
partial_dos: if True, projections will be interpolated as well
and partial doses will be return. Projections must be available
in the loader.
npts_mu: number of energy points of the Dos
T: parameter used to smooth the Dos
progress: Default False, If True a progress bar is shown when
partial dos are computed.
"""
dos_dict = {}
enr = (self.eband.min(), self.eband.max())
if self.data.is_spin_polarized:
h = sum(np.array_split(self.accepted, 2)[0])
eband_ud = np.array_split(self.eband, [h], axis=0)
vvband_ud = np.array_split(self.vvband, [h], axis=0)
spins = [Spin.up, Spin.down]
else:
eband_ud = [self.eband]
vvband_ud = [self.vvband]
spins = [Spin.up]
for spin, eb, vvb in zip(spins, eband_ud, vvband_ud):
energies, densities, vvdos, cdos = BL.BTPDOS(eb,
vvb,
npts=npts_mu,
erange=enr)
if T:
densities = BL.smoothen_DOS(energies, densities, T)
dos_dict.setdefault(spin, densities)
tdos = Dos(self.efermi / units.eV, energies / units.eV, dos_dict)
if partial_dos:
tdos = self.get_partial_doses(tdos, eband_ud, spins, enr, npts_mu,
T, progress)
return tdos
def __init__(
self,
BztInterpolator,
temp_r=np.arange(100, 1400, 100),
doping=None,
npts_mu=4000,
CRTA=1e-14,
margin=None,
save_bztTranspProps=False,
load_bztTranspProps=False,
fname="bztTranspProps.json.gz",
):
"""
Args:
BztInterpolator: a BztInterpolator previously generated
temp_r: numpy array of temperatures at which to calculate transport properties
doping: doping levels at which to calculate transport properties. If provided,
transport properties w.r.t. these doping levels are also computed. See
compute_properties_doping() method for details.
npts_mu: number of energy points at which to calculate transport properties
CRTA: constant value of the relaxation time
save_bztTranspProps: Default False. If True all computed transport properties
will be stored in fname file.
load_bztTranspProps: Default False. If True all computed transport properties
will be loaded from fname file.
fname: File path where to save/load transport properties.
Upon creation, it contains properties tensors w.r.t. the chemical potential
of size (len(temp_r),npts_mu,3,3):
Conductivity_mu (S/m), Seebeck_mu (microV/K), Kappa_mu (W/(m*K)),
Power_Factor_mu (milliW/K m);
cond_Effective_mass_mu (m_e) calculated as Ref.
Also:
Carrier_conc_mu: carrier concentration of size (len(temp_r),npts_mu)
Hall_carrier_conc_trace_mu: trace of Hall carrier concentration of size
(len(temp_r),npts_mu)
mu_r_eV: array of energies in eV and with E_fermi at 0.0
where all the properties are calculated.
Example:
bztTransp = BztTransportProperties(bztInterp,temp_r = np.arange(100,1400,100))
"""
self.dosweight = BztInterpolator.data.dosweight
self.volume = BztInterpolator.data.get_volume()
self.nelect = BztInterpolator.data.nelect
self.efermi = BztInterpolator.data.fermi / units.eV
if margin is None:
margin = 9.0 * units.BOLTZMANN * temp_r.max()
if load_bztTranspProps:
self.load(fname)
else:
self.CRTA = CRTA
self.temp_r = temp_r
self.doping = doping
self.epsilon, self.dos, self.vvdos, self.cdos = BL.BTPDOS(
BztInterpolator.eband,
BztInterpolator.vvband,
npts=npts_mu,
cband=BztInterpolator.cband,
)
mur_indices = np.logical_and(
self.epsilon > self.epsilon.min() + margin,
self.epsilon < self.epsilon.max() - margin,
)
self.mu_r = self.epsilon[mur_indices]
self.mu_r_eV = self.mu_r / units.eV - self.efermi
N, L0, L1, L2, Lm11 = BL.fermiintegrals(
self.epsilon,
self.dos,
self.vvdos,
mur=self.mu_r,
Tr=temp_r,
dosweight=self.dosweight,
cdos=self.cdos,
)
# Compute the Onsager coefficients from those Fermi integrals
(
self.Conductivity_mu,
self.Seebeck_mu,
self.Kappa_mu,
Hall_mu,
) = BL.calc_Onsager_coefficients(L0,
L1,
L2,
self.mu_r,
temp_r,
self.volume,
Lm11=Lm11)
# Common properties rescaling
self.Conductivity_mu *= CRTA # S / m
self.Seebeck_mu *= 1e6 # microvolt / K
self.Kappa_mu *= CRTA # W / (m K)
self.Hall_carrier_conc_trace_mu = (
units.Coulomb * 1e-6 /
(np.abs(Hall_mu[:, :, 0, 1, 2] + Hall_mu[:, :, 2, 0, 1] +
Hall_mu[:, :, 1, 2, 0]) / 3))
self.Carrier_conc_mu = (N +
self.nelect) / (self.volume /
(units.Meter / 100.0)**3)
# Derived properties
cond_eff_mass = np.zeros((len(self.temp_r), len(self.mu_r), 3, 3))
for t in range(len(self.temp_r)):
for i in range(len(self.mu_r)):
try:
cond_eff_mass[t, i] = (
np.linalg.inv(self.Conductivity_mu[t, i]) *
self.Carrier_conc_mu[t, i] * units.qe_SI**2 /
units.me_SI * 1e6)
except np.linalg.LinAlgError:
pass
self.Effective_mass_mu = cond_eff_mass * CRTA
self.Power_Factor_mu = (
self.Seebeck_mu @ self.Seebeck_mu) @ self.Conductivity_mu
self.Power_Factor_mu *= 1e-9 # milliWatt / m / K**2
# self.props_as_dict()
self.contain_props_doping = False
if isinstance(doping, np.ndarray):
self.compute_properties_doping(doping, temp_r)
if save_bztTranspProps:
self.save(fname)
def boltztrap(dirname, bt2file, title, T):
print(("\n\nWorking in %s for %s at %i K" % (dirname, title, T)))
# If a ready-made file with the interpolation results is available, use it
# Otherwise, create the file.
if not os.path.exists(bt2file):
# Load the input
data = BTP.DFTData(dirname)
# Select the interesting bands
nemin, nemax = data.bandana(emin=data.fermi - .2, emax=data.fermi + .2)
# Set up a k point grid with roughly five times the density of the input
equivalences = sphere.get_equivalences(data.atoms,
len(data.kpoints) * 5)
# Perform the interpolation
coeffs = fite.fitde3D(data, equivalences)
# Save the result
serialization.save_calculation(
bt2file, data, equivalences, coeffs,
serialization.gen_bt2_metadata(data, data.mommat is not None))
# Load the interpolation results
print("Load the interpolation results")
data, equivalences, coeffs, metadata = serialization.load_calculation(
bt2file)
# Reconstruct the bands
print("Reconstruct the bands")
lattvec = data.get_lattvec()
eband, vvband, cband = fite.getBTPbands(equivalences, coeffs, lattvec)
# Obtain the Fermi integrals for different chemical potentials at
# room temperature.
TEMP = np.array([T])
epsilon, dos, vvdos, cdos = BL.BTPDOS(eband, vvband, npts=4000)
margin = 9. * units.BOLTZMANN * TEMP.max()
mur_indices = np.logical_and(epsilon > epsilon.min() + margin,
epsilon < epsilon.max() - margin)
mur = epsilon[mur_indices]
N, L0, L1, L2, Lm11 = BL.fermiintegrals(epsilon,
dos,
vvdos,
mur=mur,
Tr=TEMP,
dosweight=data.dosweight)
# Compute the Onsager coefficients from those Fermi integrals
print("Compute the Onsager coefficients")
UCvol = data.get_volume()
sigma, seebeck, kappa, Hall = BL.calc_Onsager_coefficients(
L0, L1, L2, mur, TEMP, UCvol)
fermi = BL.solve_for_mu(epsilon, dos, data.nelect, T, data.dosweight)
savedata[title + '-%s' % T] = {
"sigma": sigma,
"seebeck": seebeck,
"kappa": kappa,
"Hall": Hall,
"mu": (mur - fermi) / BL.eV,
"temp": T,
"n": N[0] + data.nelect
}
ecut, efcut, deltae, tmax, deltat, lpfac = 1.0 * RYDBERG, 0.3 * RYDBERG, 0.0005 * RYDBERG, 1200.0, 10.0, 5
# Load the input
data = dft.DFTData(dirname)
# Select the interesting bands
nemin, nemax = data.bandana(emin=data.fermi - ecut, emax=data.fermi + ecut)
# Set up a k point grid with roughly five times the density of the input
equivalences = sphere.get_equivalences(data.atoms, len(data.kpoints) * lpfac)
# Perform the interpolation
coeffs = fite.fitde3D(data, equivalences)
lattvec = data.get_lattvec()
eband, vvband, cband = fite.getBTPbands(equivalences, coeffs, lattvec)
epsilon, dos, vvdos, cdos = BL.BTPDOS(
eband,
vvband,
erange=[data.fermi - ecut, data.fermi + ecut],
npts=round(2 * ecut / deltae),
scattering_model='uniform_tau')
# Define the temperatures and chemical potentials we are interested in
#Tr = np.arange(deltat, tmax + deltat / 2, deltat)
#mur_indices = np.logical_and(epsilon > data.fermi - efcut, epsilon < data.fermi + efcut)
#mur = epsilon[mur_indices]
Tr = np.arange(deltat, tmax + deltat / 2, deltat)
mur = np.empty_like(Tr)
_nelect = data.nelect
for iT, T in enumerate(Tr):
mur[iT] = BL.solve_for_mu(epsilon,
dos,
_nelect - doping_level,
def __init__(self,
BztInterpolator,
temp_r=np.arange(100, 1400, 100),
doping=10.**np.arange(16, 23),
npts_mu=4000,
CRTA=1e-14,
margin=None):
"""
Args:
BztInterpolator: a BztInterpolator previously generated
temp_r: numpy array of temperatures at which to calculate trasport properties
doping: doping levels at which to calculate trasport properties
npts_mu: number of energy points at which to calculate trasport properties
CRTA: constant value of the relaxation time
Upon creation, it contains properties tensors w.r.t. the chemical potential
of size (len(temp_r),npts_mu,3,3):
Conductivity_mu (S/m), Seebeck_mu (microV/K), Kappa_mu (W/(m*K)),
Power_Factor_mu (milliW/K);
cond_Effective_mass_mu (m_e) calculated as Ref.
Also:
Carrier_conc_mu: carrier concentration of size (len(temp_r),npts_mu)
Hall_carrier_conc_trace_mu: trace of Hall carrier concentration of size
(len(temp_r),npts_mu)
mu_r_eV: array of energies in eV and with E_fermi at 0.0
where all the properties are calculated.
Example:
bztTransp = BztTransportProperties(bztInterp,temp_r = np.arange(100,1400,100))
"""
self.CRTA = CRTA
self.temp_r = temp_r
self.doping = doping
self.dosweight = BztInterpolator.data.dosweight
self.epsilon, self.dos, self.vvdos, self.cdos = BL.BTPDOS(
BztInterpolator.eband,
BztInterpolator.vvband,
npts=npts_mu,
cband=BztInterpolator.cband)
if margin is None:
margin = 9. * units.BOLTZMANN * temp_r.max()
mur_indices = np.logical_and(
self.epsilon > self.epsilon.min() + margin,
self.epsilon < self.epsilon.max() - margin)
self.mu_r = self.epsilon[mur_indices]
N, L0, L1, L2, Lm11 = BL.fermiintegrals(self.epsilon,
self.dos,
self.vvdos,
mur=self.mu_r,
Tr=temp_r,
dosweight=self.dosweight,
cdos=self.cdos)
self.efermi = BztInterpolator.data.fermi / units.eV
self.mu_r_eV = self.mu_r / units.eV - self.efermi
self.nelect = BztInterpolator.data.nelect
self.volume = BztInterpolator.data.get_volume()
# Compute the Onsager coefficients from those Fermi integrals
self.Conductivity_mu, self.Seebeck_mu, self.Kappa_mu, Hall_mu = BL.calc_Onsager_coefficients(
L0, L1, L2, self.mu_r, temp_r, self.volume, Lm11=Lm11)
# Common properties rescaling
self.Conductivity_mu *= CRTA # S / m
self.Seebeck_mu *= 1e6 # microvolt / K
self.Kappa_mu *= CRTA # W / (m K)
self.Hall_carrier_conc_trace_mu = units.Coulomb * 1e-6 / (
np.abs(Hall_mu[:, :, 0, 1, 2] + Hall_mu[:, :, 2, 0, 1] +
Hall_mu[:, :, 1, 2, 0]) / 3)
self.Carrier_conc_mu = (N + self.nelect) / (self.volume /
(units.Meter / 100.)**3)
# Derived properties
cond_eff_mass = np.zeros((len(self.temp_r), len(self.mu_r), 3, 3))
for t in range(len(self.temp_r)):
for i in range(len(self.mu_r)):
try:
cond_eff_mass[t, i] = np.linalg.inv(
self.Conductivity_mu[t, i]) * self.Carrier_conc_mu[
t, i] * units.qe_SI**2 / units.me_SI * 1e6
except np.linalg.LinAlgError:
pass
self.Effective_mass_mu = cond_eff_mass * CRTA
self.Power_Factor_mu = (
self.Seebeck_mu @ self.Seebeck_mu) @ self.Conductivity_mu
self.Power_Factor_mu *= 1e-9 # milliWatt / m / K**2
# Save the result
serialization.save_calculation(
bt2file, data, equivalences, coeffs,
serialization.gen_bt2_metadata(data, data.mommat is not None))
# Load the interpolation results
data, equivalences, coeffs, metadata = serialization.load_calculation(bt2file)
# Reconstruct the bands
lattvec = data.get_lattvec()
eband, vvband, cband = fite.getBTPbands(equivalences, coeffs, lattvec)
# Obtain the Fermi integrals for different chemical potentials at
# room temperature.
TEMP = np.array([300.])
epsilon, dos, vvdos, cdos = BL.BTPDOS(eband, vvband, npts=4000)
margin = 9. * units.BOLTZMANN * TEMP.max()
mur_indices = np.logical_and(epsilon > epsilon.min() + margin,
epsilon < epsilon.max() - margin)
mur = epsilon[mur_indices]
N, L0, L1, L2, Lm11 = BL.fermiintegrals(epsilon,
dos,
vvdos,
mur=mur,
Tr=TEMP,
dosweight=data.dosweight)
# Compute the Onsager coefficients from those Fermi integrals
UCvol = data.get_volume()
sigma, seebeck, kappa, Hall = BL.calc_Onsager_coefficients(
L0, L1, L2, mur, TEMP, UCvol)
