def trans_cal():
from mpi4py import MPI
from aces.App import App
m = App().m
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
print("my rank is: %d" % rank)
if rank == 0:
print("Reading force constants from cache")
d = np.load('fcbin.npz')
fccenter, fcleft, fcright = d['fccenter'], d['fcleft'], d['fcright']
#fccenter,fcleft,fcright = comm.bcast((fccenter,fcleft,fcright) if rank == 0 else None, root=0)
print rank, len(fccenter)
total = 400.0
fmax = m.fmax
dm = fmax / total
intval = dm * size
omega = np.arange(dm * rank, fmax, intval) # THz
factor = 1e12**2 * 1e-20 * 1e-3 / 1.6e-19 / 6.23e23
energies = (omega * 2.0 * np.pi)**2 * factor
mkdir('tmp')
from ase.transport.calculators import TransportCalculator
# important trick!
# if eta is too small , there would be infinite value in transmission
# while if eta is too large, the transmission will be curve.
#
if m.eta is None:
eta = np.abs(fccenter).max() * 1e-5
eta1 = np.abs(fcleft).max() * 1e-4
eta2 = np.abs(fcright).max() * 1e-4
else:
if hasattr(m.eta, 'extend'):
eta, eta1, eta2 = m.eta
else:
eta, eta1, eta2 = m.eta, m.eta, m.eta
tcalc = TransportCalculator(
h=fccenter,
h1=fcleft,
h2=fcright,
energies=energies,
dos=True,
logfile='tmp/negf.log' +
str(rank),
eta=eta,
eta1=eta1,
eta2=eta2)
if rank == 0:
print('Calculate Transmission')
trans = tcalc.get_transmission()
if rank == 0:
print('Calculate Dos')
dos = tcalc.get_dos() * omega
# np.savez('tmp/result%s.npz'%(rank),x=omega,trans=trans,dos=dos)
to_txt(['omega', 'trans', 'dos'], np.c_[
omega, trans, dos], 'tmp/result.txt' + str(rank))
def trans_cal():
from mpi4py import MPI
from aces.App import App
m = App().m
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
print("my rank is: %d" % rank)
if rank == 0:
print("Reading force constants from cache")
d = np.load('fcbin.npz')
fccenter, fcleft, fcright = d['fccenter'], d['fcleft'], d['fcright']
#fccenter,fcleft,fcright = comm.bcast((fccenter,fcleft,fcright) if rank == 0 else None, root=0)
print rank, len(fccenter)
total = 400.0
fmax = m.fmax
dm = fmax / total
intval = dm * size
omega = np.arange(dm * rank, fmax, intval) # THz
factor = 1e12**2 * 1e-20 * 1e-3 / 1.6e-19 / 6.23e23
energies = (omega * 2.0 * np.pi)**2 * factor
mkdir('tmp')
from ase.transport.calculators import TransportCalculator
# important trick!
# if eta is too small , there would be infinite value in transmission
# while if eta is too large, the transmission will be curve.
#
if m.eta is None:
eta = np.abs(fccenter).max() * 1e-5
eta1 = np.abs(fcleft).max() * 1e-4
eta2 = np.abs(fcright).max() * 1e-4
else:
if hasattr(m.eta, 'extend'):
eta, eta1, eta2 = m.eta
else:
eta, eta1, eta2 = m.eta, m.eta, m.eta
tcalc = TransportCalculator(h=fccenter,
h1=fcleft,
h2=fcright,
energies=energies,
dos=True,
logfile='tmp/negf.log' + str(rank),
eta=eta,
eta1=eta1,
eta2=eta2)
if rank == 0:
print('Calculate Transmission')
trans = tcalc.get_transmission()
if rank == 0:
print('Calculate Dos')
dos = tcalc.get_dos() * omega
# np.savez('tmp/result%s.npz'%(rank),x=omega,trans=trans,dos=dos)
to_txt(['omega', 'trans', 'dos'], np.c_[omega, trans, dos],
'tmp/result.txt' + str(rank))
def test(self):
dm = .1
omega = np.arange(dm, 60, dm) #THz
factor = 1e12**2 * 1e-20 * 1e-3 / 1.6e-19 / 6.23e23
energies = (omega * 2.0 * np.pi)**2 * factor
#energies=np.arange(0,10,.01)
h = -np.array((-2, 1, 0, 1, -2, 1, 0, 1, -2)).reshape((3, 3))
h1 = -np.array((-2, 1, 1, -2)).reshape((2, 2))
#x=1.0/np.sqrt(2)
#h1=h=-np.array((-2,x,0,0,x,-1,x,0,0,x,-2,x,0,0,x,-1)).reshape((4,4))
#energies = np.arange(-3, 3, 0.1)
calc = TransportCalculator(h=h, h1=h1, energies=energies, dos=True)
T = calc.get_transmission()
#print T
dos = calc.get_dos() * omega
plot([omega, 'Frequency (THz)'], [T, 'Transmission'],
'test_green_transmission.png')
plot([omega, 'Frequency (THz)'], [dos, 'Phonon Density of State'],
'test_green_dos.png')
def ase_et(self, scatter_hamil):
# Set up options if we want to call ASE with special cases
if self.input.dict['orthonormal_overlap'] != 'False':
scatter_hamil['s'] = None
scatter_hamil['s1'] = None
scatter_hamil['s2'] = None
# initialize the calculator
ETran = TCalc(h=scatter_hamil['h'],
h1=scatter_hamil['h1'],
h2=scatter_hamil['h2'],
s=scatter_hamil['s'],
s1=scatter_hamil['s1'],
s2=scatter_hamil['s2'])
# Disable reading in the off diagonal matrices
if (self.input.dict['exclude_coupling'] == 'False'):
ETran.set(hc1=scatter_hamil['hc1'],
hc2=scatter_hamil['hc2'],
sc1=scatter_hamil['sc1'],
sc2=scatter_hamil['sc2'])
# either calculate the electron transport at the Fermi level or a
# range of values relative to the Fermi level
if (self.input.dict['energy_levels_ET'] is None):
temp_form = [(0.0)]
ETran.set(energies=[(0.0)])
else:
temp_form = self.input.dict['energy_levels_ET']
ETran.set(
energies=np.arange(temp_form[0], temp_form[1], temp_form[2]))
# ase has some warnings that we can't do anything about,
# so suppress the warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
T = ETran.get_transmission()
# print results
if (self.input.dict['energy_levels_ET'] is not None):
j = 0
for i in np.arange(temp_form[0], temp_form[1], temp_form[2]):
printx("Energy = {0:.3f} Transmission = {1}".format(i, T[j]),
self.file)
j += 1
self.print_conductance(T, temp_form[2], temp_form[0], temp_form[1])
else:
printx("Energy = 0.0 Transmission = {0}".format(T[0]), self.file)
printx("\n", self.file)
return
def generate(self):
self.m.xp = 1
leadm = self.preLead()
self.phonopy('lead', leadm)
centerm = self.preCenter()
self.phonopy('center', centerm)
fccenter, fclead = self.collect()
dm = .5
omega = np.arange(dm, 60, dm) #THz
factor = 1e12**2 * 1e-20 * 1e-3 / 1.6e-19 / 6.23e23
energies = (omega * 2.0 * np.pi)**2 * factor
tcalc = TransportCalculator(h=fccenter,
h1=fclead,
h2=fclead,
energies=energies,
logfile='negf.log',
dos=True)
print 'Calculate Transmission'
trans = tcalc.get_transmission()
print 'Calculate Dos'
dos = tcalc.get_dos() * omega
print 'Calculate Thermal Conductance'
self.post()
def test_transport_calculator():
H_lead = np.zeros([4, 4])
# On-site energies are zero
for i in range(4):
H_lead[i, i] = 0.0
# Nearest neighbor hopping is -1.0
for i in range(3):
H_lead[i, i + 1] = -1.0
H_lead[i + 1, i] = -1.0
# Next-nearest neighbor hopping is 0.2
for i in range(2):
H_lead[i, i + 2] = 0.2
H_lead[i + 2, i] = 0.2
H_scat = np.zeros([6, 6])
# Principal layers on either side of S
H_scat[:2, :2] = H_lead[:2, :2]
H_scat[-2:, -2:] = H_lead[:2, :2]
# Scattering region
H_scat[2, 2] = 0.0
H_scat[3, 3] = 0.0
H_scat[2, 3] = -0.8
H_scat[3, 2] = -0.8
# External coupling
H_scat[1, 2] = 0.2
H_scat[2, 1] = 0.2
H_scat[3, 4] = 0.2
H_scat[4, 3] = 0.2
energies = np.arange(-3, 3, 0.02)
tcalc = TransportCalculator(h=H_scat,
h1=H_lead,
eta=0.02,
energies=energies)
T = tcalc.get_transmission()
tcalc.set(pdos=[2, 3])
pdos = tcalc.get_pdos()
tcalc.set(dos=True)
dos = tcalc.get_dos()
write('T.dat', tcalc.energies, T)
write('pdos0.dat', tcalc.energies, pdos[0])
write('pdos1.dat', tcalc.energies, pdos[1])
#subdiagonalize
h_rot, s_rot, eps, u = tcalc.subdiagonalize_bfs([2, 3], apply=True)
T_rot = tcalc.get_transmission()
dos_rot = tcalc.get_dos()
pdos_rot = tcalc.get_pdos()
write('T_rot.dat', tcalc.energies, T_rot)
write('pdos0_rot.dat', tcalc.energies, pdos_rot[0])
write('pdos1_rot.dat', tcalc.energies, pdos_rot[1])
print('Subspace eigenvalues:', eps)
assert sum(abs(eps - (-0.8, 0.8))) < 2.0e-15, 'Subdiagonalization. error'
print('Max deviation of T after the rotation:', np.abs(T - T_rot).max())
assert max(abs(T - T_rot)) < 2.0e-15, 'Subdiagonalization. error'
#remove coupling
h_cut, s_cut = tcalc.cutcoupling_bfs([2], apply=True)
T_cut = tcalc.get_transmission()
dos_cut = tcalc.get_dos()
pdos_cut = tcalc.get_pdos()
write('T_cut.dat', tcalc.energies, T_cut)
write('pdos0_cut.dat', tcalc.energies, pdos_cut[0])
write('pdos1_cut.dat', tcalc.energies, pdos_cut[1])
stm = reload(stm) stm_calc = stm.STM(h1, s1, h2, s2, h10, s10, h20, s20, eta1, eta2) energies = np.arange(-3.0, 3.0, 0.01) stm_calc.initialize(energies) T_stm = stm_calc.get_transmission(V_ts) #Perform the full calculation and compare from ase.transport.calculators import TransportCalculator as TC h = np.zeros([4, 4]) h[:2, :2] = h1 h[-2:, -2:] = h2 h[:2, -2:] = V_ts h[-2:, :2] = V_ts.T tc = TC(energies=energies, h=h, h1=h10, h2=h20, eta=eta1, eta1=eta1, eta2=eta2) T_full = tc.get_transmission() pylab.plot(stm_calc.energies, T_stm, 'b') pylab.plot(tc.energies, T_full, 'r--') pylab.show() #bias stuff biass = np.arange(-2.0, 2.0, 0.2) Is = [stm_calc.get_current(bias, V_ts) for bias in biass] pylab.plot(biass, Is, '+') pylab.show()
[0.2, -1., 0., -1.], [0., 0.2, -1., 0.]])
H_scat = np.zeros((6, 6))
# Principal layers on either side of S
H_scat[:2, :2] = H_scat[-2:, -2:] = H_lead[:2, :2]
# Scattering region (hydrogen molecule) - onsite 0.0 and hopping -0.8
H_scat[2:4, 2:4] = [[0.0, -0.8], [-0.8, 0.0]]
# coupling to the leads - nearest neighbor only
H_scat[1, 2] = H_scat[2, 1] = H_scat[3, 4] = H_scat[4, 3] = 0.2
tcalc = TransportCalculator(
h=H_scat, # Scattering Hamiltonian
h1=H_lead, # Lead 1 (left)
h2=H_lead, # Lead 2 (right)
energies=np.arange(-3, 3, 0.02))
T_e = tcalc.get_transmission()
pylab.plot(tcalc.energies, T_e)
pylab.title('Transmission function')
pylab.show()
tcalc.set(pdos=[2, 3])
pdos_ne = tcalc.get_pdos()
pylab.plot(tcalc.energies, pdos_ne[0], ':')
pylab.plot(tcalc.energies, pdos_ne[1], '--')
pylab.title('Projected density of states')
pylab.show()
H_scat[-2:, -2:] = H_lead[:2, :2]
# Scattering region
H_scat[2, 2] = 0.0
H_scat[3, 3] = 0.0
H_scat[2, 3] = -0.8
H_scat[3, 2] = -0.8
# External coupling
H_scat[1, 2] = 0.2
H_scat[2, 1] = 0.2
H_scat[3, 4] = 0.2
H_scat[4, 3] = 0.2
energies = np.arange(-3, 3, 0.02)
tcalc = TransportCalculator(h=H_scat, h1=H_lead, eta=0.02, energies=energies)
T = tcalc.get_transmission()
tcalc.set(pdos=[2, 3])
pdos = tcalc.get_pdos()
tcalc.set(dos=True)
dos = tcalc.get_dos()
write('T.dat', tcalc.energies, T)
write('pdos0.dat', tcalc.energies, pdos[0])
write('pdos1.dat', tcalc.energies, pdos[1])
#subdiagonalize
h_rot, s_rot, eps, u = tcalc.subdiagonalize_bfs([2, 3], apply=True)
T_rot = tcalc.get_transmission()
stm_calc.initialize(energies) T_stm = stm_calc.get_transmission(V_ts) # Perform the full calculation and compare from ase.transport.calculators import TransportCalculator as TC h = np.zeros([4, 4]) h[:2, :2] = h1 h[-2:, -2:] = h2 h[:2, -2:] = V_ts h[-2:, :2] = V_ts.T tc = TC(energies=energies, h=h, h1=h10, h2=h20, eta=eta1, eta1=eta1, eta2=eta2) T_full = tc.get_transmission() pylab.plot(stm_calc.energies, T_stm, "b") pylab.plot(tc.energies, T_full, "r--") pylab.show() # bias stuff biass = np.arange(-2.0, 2.0, 0.2) Is = [stm_calc.get_current(bias, V_ts) for bias in biass] pylab.plot(biass, Is, "+") pylab.show()
# Principal layer size
# Uncomment this line if going back to gpawtransport again
# pl = 4 * 9 # 9 is the number of bf per Pt atom (basis=szp), see below
# Read in the hamiltonians
h, s = pickle.load(open('scat_hs.pickle', 'rb'))
# Uncomment this line if going back to gpawtransport again
# h, s = h[pl:-pl, pl:-pl], s[pl:-pl, pl:-pl]
h1, s1 = pickle.load(open('lead1_hs.pickle', 'rb'))
h2, s2 = pickle.load(open('lead2_hs.pickle', 'rb'))
tcalc = TransportCalculator(
h=h,
h1=h1,
h2=h2, # hamiltonian matrices
s=s,
s1=s1,
s2=s2, # overlap matrices
align_bf=1) # align the Fermi levels
# Calculate the conductance (the energy zero corresponds to the Fermi level)
tcalc.set(energies=[0.0])
G = tcalc.get_transmission()[0]
print('Conductance: %.2f 2e^2/h' % G)
# Determine the basis functions of the two Hydrogen atoms and subdiagonalize
Pt_N = 5 # Number of Pt atoms on each side in the scattering region
Pt_nbf = 15 # number of bf per Pt atom (basis=szp)
H_nbf = 4 # number of bf per H atom (basis=szp)
bf_H1 = Pt_nbf * Pt_N
bfs = range(bf_H1, bf_H1 + 2 * H_nbf)
calc.set(kpts=kpts, usesymm=usesymm)
atoms.get_potential_energy()
efermi = calc.get_fermi_level()
ibzk2d_k, weight2d_k, h_skmm, s_kmm\
= get_lead_lcao_hamiltonian(calc, direction=direction)
h_kmm = h_skmm[0] - efermi * s_kmm
T_kpts = np.zeros_like(energies)
i = 0
for h_mm, s_mm, weight in zip(h_kmm, s_kmm, weight2d_k):
print i
tc = TC(energies=energies,
h=h_mm, s=s_mm,
h1=h_mm, s1=s_mm,
h2=h_mm, s2=s_mm,
align_bf=0)
tc.initialize()
T_kpts += tc.get_transmission() * weight
i += 1
# repeat the system in the transverse directions
repeat = np.ones((3,))
repeat[transverse_dirs] = np.asarray(kpts)[transverse_dirs].astype(int)
atoms2 = atoms.repeat(repeat)
kpts2 = np.ones((3,))
kpts2[dir] = kpts[dir]
calc.set(kpts=tuple(kpts2.astype(int)), usesymm=usesymm)
atoms2.set_calculator(calc)
[0.2, -1., 0., -1.],
[0., 0.2, -1., 0.]])
H_scat = np.zeros((6, 6))
# Principal layers on either side of S
H_scat[:2, :2] = H_scat[-2:, -2:] = H_lead[:2, :2]
# Scattering region (hydrogen molecule) - onsite 0.0 and hopping -0.8
H_scat[2:4, 2:4] = [[0.0, -0.8], [-0.8, 0.0]]
# coupling to the leads - nearest neighbor only
H_scat[1, 2] = H_scat[2, 1] = H_scat[3, 4] = H_scat[4, 3] = 0.2
tcalc = TransportCalculator(h=H_scat, # Scattering Hamiltonian
h1=H_lead, # Lead 1 (left)
h2=H_lead, # Lead 2 (right)
energies=np.arange(-3, 3, 0.02))
T_e = tcalc.get_transmission()
pylab.plot(tcalc.energies, T_e)
pylab.title('Transmission function')
pylab.show()
tcalc.set(pdos=[2, 3])
pdos_ne = tcalc.get_pdos()
pylab.plot(tcalc.energies, pdos_ne[0], ':')
pylab.plot(tcalc.energies, pdos_ne[1], '--')
pylab.title('Projected density of states')
pylab.show()
h_rot, s_rot, eps_n, vec_nn = tcalc.subdiagonalize_bfs([2, 3])
# Scattering region
H_scat[2,2] = 0.0
H_scat[3,3] = 0.0
H_scat[2,3] = -0.8
H_scat[3,2] = -0.8
# External coupling
H_scat[1,2] = 0.2
H_scat[2,1] = 0.2
H_scat[3,4] = 0.2
H_scat[4,3] = 0.2
energies = np.arange(-3,3,0.02)
tcalc = TransportCalculator(h=H_scat,
h1=H_lead,
h2=H_lead,
energies=energies)
T = tcalc.get_transmission()
tcalc.set(pdos=[2, 3])
pdos = tcalc.get_pdos()
tcalc.set(dos=True)
dos = tcalc.get_dos()
write('T.dat',tcalc.energies,T)
write('pdos0.dat', tcalc.energies,pdos[0])
write('pdos1.dat', tcalc.energies,pdos[1])
#subdiagonalize
h_rot, s_rot, eps, u = tcalc.subdiagonalize_bfs([2, 3])
atoms.get_potential_energy()
efermi = calc.get_fermi_level()
ibzk2d_k, weight2d_k, h_skmm, s_kmm\
= get_lead_lcao_hamiltonian(calc, direction=direction)
h_kmm = h_skmm[0] - efermi * s_kmm
T_kpts = np.zeros_like(energies)
i = 0
for h_mm, s_mm, weight in zip(h_kmm, s_kmm, weight2d_k):
print i
tc = TC(energies=energies,
h=h_mm,
s=s_mm,
h1=h_mm,
s1=s_mm,
h2=h_mm,
s2=s_mm,
align_bf=0)
tc.initialize()
T_kpts += tc.get_transmission() * weight
i += 1
# repeat the system in the transverse directions
repeat = np.ones((3, ))
repeat[transverse_dirs] = np.asarray(kpts)[transverse_dirs].astype(int)
atoms2 = atoms.repeat(repeat)
kpts2 = np.ones((3, ))
kpts2[dir] = kpts[dir]
calc.set(kpts=tuple(kpts2.astype(int)), usesymm=usesymm)
import pickle
import pylab
# Principal layer size
# Uncomment this line if going back to gpawtransport again
# pl = 4 * 9 # 9 is the number of bf per Pt atom (basis=szp), see below
# Read in the hamiltonians
h, s = pickle.load(file('scat_hs.pickle'))
# Uncomment this line if going back to gpawtransport again
# h, s = h[pl:-pl, pl:-pl], s[pl:-pl, pl:-pl]
h1, s1 = pickle.load(file('lead1_hs.pickle'))
h2, s2 = pickle.load(file('lead2_hs.pickle'))
tcalc = TransportCalculator(h=h, h1=h1, h2=h2, # hamiltonian matrices
s=s, s1=s1, s2=s2, # overlap matrices
align_bf=1) # align the Fermi levels
# Calculate the conductance (the energy zero corresponds to the Fermi level)
tcalc.set(energies=[0.0])
G = tcalc.get_transmission()[0]
print('Conductance: %.2f 2e^2/h' % G)
# Determine the basis functions of the two Hydrogen atoms and subdiagonalize
Pt_N = 5 # Number of Pt atoms on each side in the scattering region
Pt_nbf = 9 # number of bf per Pt atom (basis=szp)
H_nbf = 4 # number of bf per H atom (basis=szp)
bf_H1 = Pt_nbf * Pt_N
bfs = range(bf_H1, bf_H1 + 2 * H_nbf)
h_rot, s_rot, eps_n, vec_jn = tcalc.subdiagonalize_bfs(bfs)
for n in range(len(eps_n)):