def test_selfenergy_interpolation(atoms, ntk, filename, begin, end, base, scale, direction=0):
from gpaw.transport.sparse_matrix import Banded_Sparse_HSD, CP_Sparse_HSD, Se_Sparse_Matrix
from gpaw.transport.selfenergy import LeadSelfEnergy
from gpaw.transport.contour import Contour
hl_skmm, sl_kmm = get_hs(atoms)
dl_skmm = get_lcao_density_matrix(atoms.calc)
fermi = atoms.calc.get_fermi_level()
wfs = atoms.calc.wfs
hl_spkmm, sl_pkmm, dl_spkmm, \
hl_spkcmm, sl_pkcmm, dl_spkcmm = get_pk_hsd(2, ntk,
wfs.kd.ibzk_qc,
hl_skmm, sl_kmm, dl_skmm,
None, wfs.dtype,
direction=direction)
my_npk = len(wfs.kd.ibzk_qc) / ntk
my_nspins = len(wfs.kpt_u) / ( my_npk * ntk)
lead_hsd = Banded_Sparse_HSD(wfs.dtype, my_nspins, my_npk)
lead_couple_hsd = CP_Sparse_HSD(wfs.dtype, my_nspins, my_npk)
for pk in range(my_npk):
lead_hsd.reset(0, pk, sl_pkmm[pk], 'S', init=True)
lead_couple_hsd.reset(0, pk, sl_pkcmm[pk], 'S', init=True)
for s in range(my_nspins):
lead_hsd.reset(s, pk, hl_spkmm[s, pk], 'H', init=True)
lead_hsd.reset(s, pk, dl_spkmm[s, pk], 'D', init=True)
lead_couple_hsd.reset(s, pk, hl_spkcmm[s, pk], 'H', init=True)
lead_couple_hsd.reset(s, pk, dl_spkcmm[s, pk], 'D', init=True)
lead_se = LeadSelfEnergy(lead_hsd, lead_couple_hsd)
begin += fermi
end += fermi
ee = np.linspace(begin, end, base)
cmp_ee = np.linspace(begin, end, base * scale)
se = []
cmp_se = []
from scipy import interpolate
for e in ee:
se.append(lead_se(e).recover())
se = np.array(se)
ne, ny, nz= se.shape
nie = len(cmp_ee)
data = np.zeros([nie, ny, nz], se.dtype)
for yy in range(ny):
for zz in range(nz):
ydata = se[:, yy, zz]
f = interpolate.interp1d(ee, ydata)
data[:, yy, zz] = f(cmp_ee)
inter_se_linear = data
for e in cmp_ee:
cmp_se.append(lead_se(e).recover())
fd = file(filename, 'w')
cPickle.dump((cmp_se, inter_se_linear, ee, cmp_ee), fd, 2)
fd.close()
for i,e in enumerate(cmp_ee):
print(e, np.max(abs(cmp_se[i] - inter_se_linear[i])), 'linear', np.max(abs(cmp_se[i])))
def generate_selfenergy_database(atoms,
ntk,
filename,
direction=0,
kt=0.1,
bias=[-3, 3],
depth=3,
comm=None):
from gpaw.transport.sparse_matrix import Banded_Sparse_HSD, CP_Sparse_HSD, Se_Sparse_Matrix
from gpaw.transport.selfenergy import LeadSelfEnergy
from gpaw.transport.contour import Contour
hl_skmm, sl_kmm = get_hs(atoms)
dl_skmm = get_lcao_density_matrix(atoms.calc)
fermi = atoms.calc.get_fermi_level()
wfs = atoms.calc.wfs
hl_spkmm, sl_pkmm, dl_spkmm, \
hl_spkcmm, sl_pkcmm, dl_spkcmm = get_pk_hsd(2, ntk,
wfs.ibzk_qc,
hl_skmm, sl_kmm, dl_skmm,
None, wfs.dtype,
direction=direction)
my_npk = len(wfs.ibzk_qc) / ntk
my_nspins = len(wfs.kpt_u) / (my_npk * ntk)
lead_hsd = Banded_Sparse_HSD(wfs.dtype, my_nspins, my_npk)
lead_couple_hsd = CP_Sparse_HSD(wfs.dtype, my_nspins, my_npk)
for pk in range(my_npk):
lead_hsd.reset(0, pk, sl_pkmm[pk], 'S', init=True)
lead_couple_hsd.reset(0, pk, sl_pkcmm[pk], 'S', init=True)
for s in range(my_nspins):
lead_hsd.reset(s, pk, hl_spkmm[s, pk], 'H', init=True)
lead_hsd.reset(s, pk, dl_spkmm[s, pk], 'D', init=True)
lead_couple_hsd.reset(s, pk, hl_spkcmm[s, pk], 'H', init=True)
lead_couple_hsd.reset(s, pk, dl_spkcmm[s, pk], 'D', init=True)
lead_se = LeadSelfEnergy(lead_hsd, lead_couple_hsd)
contour = Contour(kt, [fermi] * 2, bias, depth, comm=comm)
path = contour.get_plot_path(ex=True)
for nid, energy in zip(path.my_nids, path.my_energies):
for kpt in wfs.kpt_u:
if kpt.q % ntk == 0:
flag = str(kpt.k // ntk) + '_' + str(nid)
lead_se.s = kpt.s
lead_se.pk = kpt.q // ntk
data = lead_se(energy)
fd = file(flag, 'w')
cPickle.dump(data, fd, 2)
fd.close()
def initialize_selfenergy_and_green_function(self, tp):
self.selfenergies = []
if tp.use_lead:
for i in range(tp.lead_num):
self.selfenergies.append(
LeadSelfEnergy(tp.lead_hsd[i], tp.lead_couple_hsd[i]))
self.selfenergies[i].set_bias(tp.bias[i])
def generate_selfenergy_database(atoms, ntk, filename, direction=0, kt=0.1,
bias=[-3,3], depth=3, comm=None):
from gpaw.transport.sparse_matrix import Banded_Sparse_HSD, CP_Sparse_HSD, Se_Sparse_Matrix
from gpaw.transport.selfenergy import LeadSelfEnergy
from gpaw.transport.contour import Contour
hl_skmm, sl_kmm = get_hs(atoms)
dl_skmm = get_lcao_density_matrix(atoms.calc)
fermi = atoms.calc.get_fermi_level()
wfs = atoms.calc.wfs
hl_spkmm, sl_pkmm, dl_spkmm, \
hl_spkcmm, sl_pkcmm, dl_spkcmm = get_pk_hsd(2, ntk,
wfs.kd.ibzk_qc,
hl_skmm, sl_kmm, dl_skmm,
None, wfs.dtype,
direction=direction)
my_npk = len(wfs.kd.ibzk_qc) / ntk
my_nspins = len(wfs.kpt_u) / ( my_npk * ntk)
lead_hsd = Banded_Sparse_HSD(wfs.dtype, my_nspins, my_npk)
lead_couple_hsd = CP_Sparse_HSD(wfs.dtype, my_nspins, my_npk)
for pk in range(my_npk):
lead_hsd.reset(0, pk, sl_pkmm[pk], 'S', init=True)
lead_couple_hsd.reset(0, pk, sl_pkcmm[pk], 'S', init=True)
for s in range(my_nspins):
lead_hsd.reset(s, pk, hl_spkmm[s, pk], 'H', init=True)
lead_hsd.reset(s, pk, dl_spkmm[s, pk], 'D', init=True)
lead_couple_hsd.reset(s, pk, hl_spkcmm[s, pk], 'H', init=True)
lead_couple_hsd.reset(s, pk, dl_spkcmm[s, pk], 'D', init=True)
lead_se = LeadSelfEnergy(lead_hsd, lead_couple_hsd)
contour = Contour(kt, [fermi] * 2, bias, depth, comm=comm)
path = contour.get_plot_path(ex=True)
for nid, energy in zip(path.my_nids, path.my_energies):
for kpt in wfs.kpt_u:
if kpt.q % ntk == 0:
flag = str(kpt.k // ntk) + '_' + str(nid)
lead_se.s = kpt.s
lead_se.pk = kpt.q // ntk
data = lead_se(energy)
fd = file(flag, 'w')
cPickle.dump(data, fd, 2)
fd.close()
def path_selfenergy(atoms, ntk, filename, begin, end, num=257, direction=0):
from gpaw.transport.sparse_matrix import Banded_Sparse_HSD, CP_Sparse_HSD, Se_Sparse_Matrix
from gpaw.transport.selfenergy import LeadSelfEnergy
from gpaw.transport.contour import Contour
hl_skmm, sl_kmm = get_hs(atoms)
dl_skmm = get_lcao_density_matrix(atoms.calc)
fermi = atoms.calc.get_fermi_level()
wfs = atoms.calc.wfs
hl_spkmm, sl_pkmm, dl_spkmm, \
hl_spkcmm, sl_pkcmm, dl_spkcmm = get_pk_hsd(2, ntk,
wfs.ibzk_qc,
hl_skmm, sl_kmm, dl_skmm,
None, wfs.dtype,
direction=direction)
my_npk = len(wfs.ibzk_qc) / ntk
my_nspins = len(wfs.kpt_u) / (my_npk * ntk)
lead_hsd = Banded_Sparse_HSD(wfs.dtype, my_nspins, my_npk)
lead_couple_hsd = CP_Sparse_HSD(wfs.dtype, my_nspins, my_npk)
for pk in range(my_npk):
lead_hsd.reset(0, pk, sl_pkmm[pk], 'S', init=True)
lead_couple_hsd.reset(0, pk, sl_pkcmm[pk], 'S', init=True)
for s in range(my_nspins):
lead_hsd.reset(s, pk, hl_spkmm[s, pk], 'H', init=True)
lead_hsd.reset(s, pk, dl_spkmm[s, pk], 'D', init=True)
lead_couple_hsd.reset(s, pk, hl_spkcmm[s, pk], 'H', init=True)
lead_couple_hsd.reset(s, pk, dl_spkcmm[s, pk], 'D', init=True)
lead_se = LeadSelfEnergy(lead_hsd, lead_couple_hsd)
begin += fermi
end += fermi
ee = np.linspace(begin, end, num)
se = []
for e in ee:
se.append(lead_se(e).recover())
se = np.array(se)
fd = file(filename + '_' + str(world.rank), 'w')
cPickle.dump((se, ee), fd, 2)
fd.close()
def initialize(
self, srf_atoms, tip_atoms, srf_coupling_atoms, tip_coupling_atoms, srf_pl_atoms, tip_pl_atoms, energies, bias=0
):
"""
srf_atoms: the atom index of onsite surface atoms
tip_atoms: the atom index of onsite tip atoms
energies: list of energies, for which the transmission function should be evaluated
bias: will be used to precalculate the greenfunctions of surface and tip
"""
self.bias = bias
self.energies = energies
self.tip_atoms = tip_atoms
self.srf_atoms = srf_atoms
self.srf_coupling_atoms = srf_coupling_atoms
self.tip_coupling_atoms = tip_coupling_atoms
# align potentials for onsite surface, onsite tip and tip principle layers according to surface_pl
srf_alignv = self.align_v(calc1=self.srf_pl, index1=srf_pl_atoms[0], calc2=self.srf, index2=srf_atoms[0])
tip_alignv = srf_alignv + self.tip.get_fermi_level() - self.srf.get_fermi_level() # - bias
tip_pl_alignv = self.align_v(calc1=self.tip, index1=tip_atoms[-1], calc2=self.tip_pl, index2=tip_pl_atoms[-1])
tip_pl_alignv += tip_alignv
self.srf_alignv = srf_alignv
self.tip_alignv = tip_alignv
# hamiltoian and overlap matrix of onsite tip and srf
self.h1, self.s1 = Lead_HS(calc=self.tip, atom_list=tip_atoms, shift=srf_alignv - bias).get_HS()
print("shape of onsite tip", np.shape(self.h1), np.shape(self.s1))
self.h2, self.s2 = Lead_HS(calc=self.srf, atom_list=srf_atoms, shift=tip_alignv).get_HS()
print("shape of onsite srf", np.shape(self.h2), np.shape(self.s2))
# hamiltoian and overlap matrix of two tip and srf principle layers
self.h10, self.s10 = Lead_HS(
calc=self.tip_pl, atom_list=tip_pl_atoms, shift=tip_pl_alignv
).get_HS() # 2 and only 2 principle layers
print("shape of pl tip", np.shape(self.h10), np.shape(self.s10))
self.h20, self.s20 = Lead_HS(
calc=self.srf_pl, atom_list=srf_pl_atoms, shift=-bias
).get_HS() # 2 and only 2 principle layers
print("shape of pl srf", np.shape(self.h20), np.shape(self.s20))
nbf1, nbf2 = len(self.h1), len(self.h2) # No. of basis functions of onsite tip and surface atoms
pl1, pl2 = len(self.h10) / 2, len(self.h20) / 2 # No. of basis functions per principle layer of tip and srf
nenergies = len(energies)
# periodic part of the tip
hs1_dii = self.h10[:pl1, :pl1], self.s10[:pl1, :pl1]
hs1_dij = self.h10[:pl1, pl1 : 2 * pl1], self.s10[:pl1, pl1 : 2 * pl1]
# coupling betwen per. and non. per part of the tip
h1_im = np.zeros((pl1, nbf1), complex)
s1_im = np.zeros((pl1, nbf1), complex)
h1_im[:pl1, :pl1], s1_im[:pl1, :pl1] = hs1_dij
hs1_dim = [h1_im, s1_im]
# periodic part the surface
hs2_dii = self.h20[:pl2, :pl2], self.s20[:pl2, :pl2]
hs2_dij = self.h20[pl2 : 2 * pl2, :pl2], self.s20[pl2 : 2 * pl2, :pl2]
# coupling betwen per. and non. per part of the surface
h2_im = np.zeros((pl2, nbf2), complex)
s2_im = np.zeros((pl2, nbf2), complex)
h2_im[-pl2:, -pl2:], s2_im[-pl2:, -pl2:] = hs2_dij
hs2_dim = [h2_im, s2_im]
# tip and surface greenfunction
self.selfenergy1 = LeadSelfEnergy(hs1_dii, hs1_dij, hs1_dim)
self.selfenergy2 = LeadSelfEnergy(hs2_dii, hs2_dij, hs2_dim)
self.greenfunction1 = GreenFunction(self.h1, self.s1, [self.selfenergy1])
self.greenfunction2 = GreenFunction(self.h2, self.s2, [self.selfenergy2])
# print 'shape of greenfunction tip:', np.shape(self.greenfunction1)
# print 'shape of greenfunction srf:', np.shape(self.greenfunction2)
# shift the bands due to the bias
self.selfenergy1.set_bias(0.0)
self.selfenergy2.set_bias(bias)
# extend the surface potentials and basis functions
self.setup_stm()
# tip and surface greenfunction matrices.
nbf1_small = self.ni # XXX Change this for efficiency in the future
nbf2_small = self.nj # XXX -||-
coupling_list1 = range(nbf1 - nbf1_small, nbf1) # XXX -||-
coupling_list2 = range(0, nbf2_small) # XXX -||-
self.gft1_emm = np.zeros((nenergies, nbf1_small, nbf1_small), complex)
self.gft2_emm = np.zeros((nenergies, nbf2_small, nbf2_small), complex)
for e, energy in enumerate(self.energies):
gft1_mm = self.greenfunction1(energy)[coupling_list1]
gft1_mm = np.take(gft1_mm, coupling_list1, axis=1)
gft2_mm = self.greenfunction2(energy)[coupling_list2]
gft2_mm = np.take(gft2_mm, coupling_list2, axis=1)
self.gft1_emm[e] = gft1_mm
self.gft2_emm[e] = gft2_mm
class STM:
def __init__(self, srf, tip, srf_pl, tip_pl, eta1=1e-4, eta2=1e-4):
self.tip = tip # tip calcualtor
self.tip_pl = tip_pl # tip periodic layers calculator
self.srf = srf # surface calculator
self.srf_pl = srf_pl # surface periodic layers calculator
self.eta1 = eta1
self.eta2 = eta2
self.tgd = tip.gd
self.sgd = srf.gd
# assert tgd.h_cv == sgd.h_cv
# assert not (tgd.h_c - sgd.h_c).any()
def initialize(
self, srf_atoms, tip_atoms, srf_coupling_atoms, tip_coupling_atoms, srf_pl_atoms, tip_pl_atoms, energies, bias=0
):
"""
srf_atoms: the atom index of onsite surface atoms
tip_atoms: the atom index of onsite tip atoms
energies: list of energies, for which the transmission function should be evaluated
bias: will be used to precalculate the greenfunctions of surface and tip
"""
self.bias = bias
self.energies = energies
self.tip_atoms = tip_atoms
self.srf_atoms = srf_atoms
self.srf_coupling_atoms = srf_coupling_atoms
self.tip_coupling_atoms = tip_coupling_atoms
# align potentials for onsite surface, onsite tip and tip principle layers according to surface_pl
srf_alignv = self.align_v(calc1=self.srf_pl, index1=srf_pl_atoms[0], calc2=self.srf, index2=srf_atoms[0])
tip_alignv = srf_alignv + self.tip.get_fermi_level() - self.srf.get_fermi_level() # - bias
tip_pl_alignv = self.align_v(calc1=self.tip, index1=tip_atoms[-1], calc2=self.tip_pl, index2=tip_pl_atoms[-1])
tip_pl_alignv += tip_alignv
self.srf_alignv = srf_alignv
self.tip_alignv = tip_alignv
# hamiltoian and overlap matrix of onsite tip and srf
self.h1, self.s1 = Lead_HS(calc=self.tip, atom_list=tip_atoms, shift=srf_alignv - bias).get_HS()
print("shape of onsite tip", np.shape(self.h1), np.shape(self.s1))
self.h2, self.s2 = Lead_HS(calc=self.srf, atom_list=srf_atoms, shift=tip_alignv).get_HS()
print("shape of onsite srf", np.shape(self.h2), np.shape(self.s2))
# hamiltoian and overlap matrix of two tip and srf principle layers
self.h10, self.s10 = Lead_HS(
calc=self.tip_pl, atom_list=tip_pl_atoms, shift=tip_pl_alignv
).get_HS() # 2 and only 2 principle layers
print("shape of pl tip", np.shape(self.h10), np.shape(self.s10))
self.h20, self.s20 = Lead_HS(
calc=self.srf_pl, atom_list=srf_pl_atoms, shift=-bias
).get_HS() # 2 and only 2 principle layers
print("shape of pl srf", np.shape(self.h20), np.shape(self.s20))
nbf1, nbf2 = len(self.h1), len(self.h2) # No. of basis functions of onsite tip and surface atoms
pl1, pl2 = len(self.h10) / 2, len(self.h20) / 2 # No. of basis functions per principle layer of tip and srf
nenergies = len(energies)
# periodic part of the tip
hs1_dii = self.h10[:pl1, :pl1], self.s10[:pl1, :pl1]
hs1_dij = self.h10[:pl1, pl1 : 2 * pl1], self.s10[:pl1, pl1 : 2 * pl1]
# coupling betwen per. and non. per part of the tip
h1_im = np.zeros((pl1, nbf1), complex)
s1_im = np.zeros((pl1, nbf1), complex)
h1_im[:pl1, :pl1], s1_im[:pl1, :pl1] = hs1_dij
hs1_dim = [h1_im, s1_im]
# periodic part the surface
hs2_dii = self.h20[:pl2, :pl2], self.s20[:pl2, :pl2]
hs2_dij = self.h20[pl2 : 2 * pl2, :pl2], self.s20[pl2 : 2 * pl2, :pl2]
# coupling betwen per. and non. per part of the surface
h2_im = np.zeros((pl2, nbf2), complex)
s2_im = np.zeros((pl2, nbf2), complex)
h2_im[-pl2:, -pl2:], s2_im[-pl2:, -pl2:] = hs2_dij
hs2_dim = [h2_im, s2_im]
# tip and surface greenfunction
self.selfenergy1 = LeadSelfEnergy(hs1_dii, hs1_dij, hs1_dim)
self.selfenergy2 = LeadSelfEnergy(hs2_dii, hs2_dij, hs2_dim)
self.greenfunction1 = GreenFunction(self.h1, self.s1, [self.selfenergy1])
self.greenfunction2 = GreenFunction(self.h2, self.s2, [self.selfenergy2])
# print 'shape of greenfunction tip:', np.shape(self.greenfunction1)
# print 'shape of greenfunction srf:', np.shape(self.greenfunction2)
# shift the bands due to the bias
self.selfenergy1.set_bias(0.0)
self.selfenergy2.set_bias(bias)
# extend the surface potentials and basis functions
self.setup_stm()
# tip and surface greenfunction matrices.
nbf1_small = self.ni # XXX Change this for efficiency in the future
nbf2_small = self.nj # XXX -||-
coupling_list1 = range(nbf1 - nbf1_small, nbf1) # XXX -||-
coupling_list2 = range(0, nbf2_small) # XXX -||-
self.gft1_emm = np.zeros((nenergies, nbf1_small, nbf1_small), complex)
self.gft2_emm = np.zeros((nenergies, nbf2_small, nbf2_small), complex)
for e, energy in enumerate(self.energies):
gft1_mm = self.greenfunction1(energy)[coupling_list1]
gft1_mm = np.take(gft1_mm, coupling_list1, axis=1)
gft2_mm = self.greenfunction2(energy)[coupling_list2]
gft2_mm = np.take(gft2_mm, coupling_list2, axis=1)
self.gft1_emm[e] = gft1_mm
self.gft2_emm[e] = gft2_mm
def scan(self, height=None, zmin=None, zmax=None):
if height is not None:
self.current = np.zeros((self.srf.gd.N_c[0], self.srf.gd.N_c[1]))
h = 0
h = np.round(height / Bohr / self.srf.gd.h_c[2]).astype(int)
for px in range(self.srf.gd.N_c[0]):
for py in range(self.srf.gd.N_c[1]):
pos = np.array([px, py, h])
V_12 = self.get_Vts(pos)
I = self.get_current(V_12)
self.current[px, py] = I
print("I = ", I)
return self.current
if height is None:
zmin_c = np.round(zmin / Bohr / self.srf.gd.h_c[2]).astype(int)
zmax_c = np.round(zmax / Bohr / self.srf.gd.h_c[2]).astype(int)
nz = zmax_c - zmin_c
self.current = np.zeros((self.srf.gd.N_c[0], self.srf.gd.N_c[1], nz))
for px in range(self.srf.gd.N_c[0]):
for py in range(self.srf.gd.N_c[1]):
for pz in range(zmin_c, zmax_c):
# get coupleing between tip and surface Vts for a given tip position on original srf grids
pos = np.array([px, py, pz])
V_12 = self.get_Vts(pos)
# get current values
I = self.get_current(V_12)
self.current[px, py, pz - zmin_c] = I
print(I)
return self.current
def get_current(self, v_12):
# get transmission
T_e = self.get_transmission(v_12)
I = -np.trapz(x=self.energies, y=T_e)
return I
def get_transmission(self, v_12):
nenergies = len(self.energies)
T_e = np.empty(nenergies, float)
v_21 = dagger(v_12)
for e, energy in enumerate(self.energies):
gft1 = self.gft1_emm[e]
gft2 = self.gft2_emm[e]
a1 = gft1 - dagger(gft1)
a2 = gft2 - dagger(gft2)
v12_a2 = np.dot(v_12, a2)
v21_a1 = np.dot(v_21, a1)
T = -np.trace(np.dot(v12_a2, v21_a1))
T_e[e] = T
self.T_e = T_e
return T_e
def get_Vts(self, tip_pos):
# tip_pos is in terms of grid points, showing the tip apex position respect to the surface grid
print("tip_pos is", tip_pos)
tip_apex_c = self.tip_apex_c[0]
srf_pos_av = self.srf.atoms.get_positions() / Bohr
srf_zmax = srf_pos_av[:, 2].max()
srf_zmax_c = np.round(srf_zmax / self.srf.gd.h_c[2]).astype(int)
srf_ox = self.srf_extension[0] + tip_pos[0]
srf_oy = self.srf_extension[1] + tip_pos[1]
srf_oz = srf_zmax_c + tip_pos[2]
shift = np.array([srf_ox, srf_oy, srf_oz])
shift -= tip_apex_c
for tbox in self.tip_coupling_functions:
tbox.corner_c += shift
for tbox in self.tip_coupling_kinetics:
tbox.corner_c += shift
# get the combined effective potential on the extended surface grid
"""
This part needs to be updated for a separate xc calculation
"""
tip_ox = self.ox
tip_oy = self.oy
tip_oz = self.oz
srf_sx = srf_ox - self.nmx
srf_ex = srf_ox + self.npx
srf_sy = srf_oy - self.nmy
srf_ey = srf_oy + self.npy
srf_sz = srf_oz - self.nmz
srf_ez = srf_oz + self.npz
tip_sx = tip_ox - self.nmx
tip_ex = tip_ox + self.npx
tip_sy = tip_oy - self.nmy
tip_ey = tip_oy + self.npy
tip_sz = tip_oz - self.nmz
tip_ez = tip_oz + self.npz
V_tip = self.tip.get_effective_potential() - self.tip_alignv
V_couple = self.extvt_G.copy()
V_couple[srf_sx:srf_ex, srf_sy:srf_ey, srf_sz:srf_ez] = V_tip[tip_sx:tip_ex, tip_sy:tip_ey, tip_sz:tip_ez]
# get V_ts
V_ts = np.zeros((self.ni, self.nj))
for ns in range(len(self.srf_coupling_functions)):
sf = self.srf_coupling_functions[ns]
sk = self.srf_coupling_kinetics[ns]
j1 = sf[0].index
j2 = j1 + len(sf[0])
for t in self.tip_coupling_functions:
i1 = t.index
i2 = i1 + len(t)
test_overlap1 = []
test_overlap2 = []
for x in range(9):
overlap = t | sf[x]
if overlap is not None:
test_overlap1.append(overlap.copy())
test_overlap2.append((np.abs(overlap)).sum())
else:
test_overlap1.append(overlap)
test_overlap2.append(overlap)
k = np.argmax(test_overlap2)
if test_overlap1[k] is not None:
V_ts[i1:i2, j1:j2] += t | sk[k]
V_ts[i1:i2, j1:j2] += t | V_couple | sf[k]
# return the tbox back :)
for tbox in self.tip_coupling_functions:
tbox.corner_c -= shift
for tbox in self.tip_coupling_kinetics:
tbox.corner_c -= shift
# print 'np.shape(V_ts)',np.shape(V_ts)
return V_ts
def setup_stm(self):
tip_pos_av = self.tip.atoms.get_positions() / Bohr
srf_pos_av = self.srf.atoms.get_positions() / Bohr
# get selected tip functions on small grids and apply kinetics
self.tip_coupling_functions = []
self.tip_coupling_kinetics = []
i = 0
for a in self.tip_coupling_atoms:
setup = self.tip.wfs.setups[a]
spos_c = tip_pos_av[a] / self.tip.gd.cell_c # gives the scaled position of tip atoms in tip cell
for phit in setup.phit_j:
f = AtomCenteredFunctions(self.tip.gd, [phit], spos_c, i)
f_kinetic = f.apply_t()
self.tip_coupling_functions.append(f)
self.tip_coupling_kinetics.append(f_kinetic)
i += len(f.f_iG)
self.ni = i
# print self.tip_coupling_functions[1].size_c
# print self.tip_coupling_functions[1].corner_c
# print np.shape(self.tip_coupling_functions[1].f_iG)
# get tip cutoff and surface extension
tip_corner_c = []
temp_min = self.tip_coupling_kinetics[0].corner_c
temp_max = self.tip_coupling_kinetics[0].corner_c
for f in self.tip_coupling_kinetics:
start_c = np.minimum(f.corner_c, temp_min)
end_c = np.maximum(f.corner_c + f.size_c, temp_max)
temp_min = start_c
temp_max = end_c
# print "start_c",start_c # in terms of grid points
# print "end_c",end_c # in terms of grid points
self.tip_zmin = tip_pos_av[:, 2].min()
self.tip_apex_index = np.where(tip_pos_av[:, 2] < self.tip_zmin + 0.01)
self.tip_apex_c = np.round(tip_pos_av[self.tip_apex_index] / self.tgd.h_c).astype(int)
self.ox, self.oy, self.oz = self.tip_apex_c[0]
self.npx, self.npy, self.npz = np.round(end_c).astype(int) - self.tip_apex_c[0]
self.nmx, self.nmy, self.nmz = self.tip_apex_c[0] - np.round(start_c).astype(int)
# get the extension of srf basis functions and effective potentials
srf_extension = np.array([self.srf.gd.N_c[0], self.srf.gd.N_c[1], 0])
self.srf_extension = srf_extension
print("extension of surface in terms of grids:", srf_extension)
# get surface fucntions on small grid with periodic conditions with entension
self.srf_coupling_functions = []
self.srf_coupling_kinetics = []
j = 0
for a in self.srf_coupling_atoms:
setup = self.srf.wfs.setups[a]
spos_c = srf_pos_av[a] / self.srf.gd.cell_c
for phit in setup.phit_j:
f = AtomCenteredFunctions(self.srf.gd, [phit], spos_c, j)
f.corner_c += srf_extension
f_kinetic = f.apply_t()
self.srf_coupling_functions.append(f.periodic())
self.srf_coupling_kinetics.append(f_kinetic.periodic())
j += len(f.f_iG)
self.nj = j
# print np.shape(self.srf_coupling_functions[0][0].f_iG), self.srf_coupling_functions[0][0].corner_c
# print np.shape(self.srf_coupling_functions[0][8].f_iG), self.srf_coupling_functions[0][8].corner_c
# Extend the surface grid and surface effective potential
svt_G = self.srf.get_effective_potential() - self.srf_alignv + self.bias
ex, ey, ez = srf_extension
newsize_c = 2 * srf_extension + self.sgd.N_c
extvt_G = np.zeros(newsize_c)
extvt_G[ex:-ex, ey:-ey, :] = svt_G
extvt_G[:ex, ey:-ey, :] = svt_G[-ex:, :, :]
extvt_G[-ex:, ey:-ey, :] = svt_G[:ex, :, :]
extvt_G[:, :ey, :] = extvt_G[:, -2 * ey : -ey, :]
extvt_G[:, -ey:, :] = extvt_G[:, ey : 2 * ey, :]
self.extvt_G = extvt_G
extsgd = GridDescriptor(N_c=newsize_c, cell_cv=self.sgd.h_c * (newsize_c), pbc_c=False, comm=mpi.serial_comm)
## Transfer the coner_c of surface basis-functions and srf_kinetics to the extended surface cell
# for sbox in self.srf_coupling_functions:
# sbox.corner_c += srf_extension
# for sbox in self.srf_coupling_kinetics:
# sbox.corner_c += srf_extension
def align_v(self, calc1, index1, calc2, index2):
pos1 = calc1.atoms.positions[index1]
pos1_c = np.round(pos1 / Bohr / calc1.wfs.gd.h_c).astype(int)
pos1_v = calc1.get_effective_potential()[pos1_c[0], pos1_c[1], pos1_c[2]]
pos2 = calc2.atoms.positions[index2]
pos2_c = np.round(pos2 / Bohr / calc2.wfs.gd.h_c).astype(int)
pos2_v = calc2.get_effective_potential()[pos2_c[0], pos2_c[1], pos2_c[2]]
return pos2_v - pos1_v
def initialize(self, srf_atoms, tip_atoms, srf_coupling_atoms, tip_coupling_atoms,
srf_pl_atoms, tip_pl_atoms, energies, bias=0):
"""
srf_atoms: the atom index of onsite surface atoms
tip_atoms: the atom index of onsite tip atoms
energies: list of energies, for which the transmission function should be evaluated
bias: will be used to precalculate the greenfunctions of surface and tip
"""
self.bias = bias
self.energies = energies
self.tip_atoms = tip_atoms
self.srf_atoms = srf_atoms
self.srf_coupling_atoms = srf_coupling_atoms
self.tip_coupling_atoms = tip_coupling_atoms
#align potentials for onsite surface, onsite tip and tip principle layers according to surface_pl
srf_alignv = self.align_v(calc1 = self.srf_pl, index1=srf_pl_atoms[0], calc2=self.srf, index2=srf_atoms[0])
tip_alignv = srf_alignv + self.tip.get_fermi_level() - self.srf.get_fermi_level() #- bias
tip_pl_alignv = self.align_v(calc1=self.tip, index1=tip_atoms[-1], calc2=self.tip_pl, index2=tip_pl_atoms[-1])
tip_pl_alignv += tip_alignv
self.srf_alignv = srf_alignv
self.tip_alignv = tip_alignv
#hamiltoian and overlap matrix of onsite tip and srf
self.h1, self.s1 = Lead_HS(calc = self.tip, atom_list = tip_atoms, shift = srf_alignv-bias).get_HS()
print 'shape of onsite tip', np.shape(self.h1), np.shape(self.s1)
self.h2, self.s2 = Lead_HS(calc = self.srf, atom_list = srf_atoms, shift = tip_alignv).get_HS()
print 'shape of onsite srf', np.shape(self.h2), np.shape(self.s2)
#hamiltoian and overlap matrix of two tip and srf principle layers
self.h10, self.s10 = Lead_HS(calc = self.tip_pl, atom_list = tip_pl_atoms, shift = tip_pl_alignv).get_HS() #2 and only 2 principle layers
print 'shape of pl tip', np.shape(self.h10), np.shape(self.s10)
self.h20, self.s20 = Lead_HS(calc = self.srf_pl, atom_list = srf_pl_atoms, shift = -bias).get_HS() #2 and only 2 principle layers
print 'shape of pl srf', np.shape(self.h20), np.shape(self.s20)
nbf1, nbf2 = len(self.h1), len(self.h2) #No. of basis functions of onsite tip and surface atoms
pl1, pl2 = len(self.h10)/2, len(self.h20)/2 #No. of basis functions per principle layer of tip and srf
nenergies = len(energies)
#periodic part of the tip
hs1_dii = self.h10[:pl1, :pl1], self.s10[:pl1, :pl1]
hs1_dij = self.h10[:pl1, pl1:2*pl1], self.s10[:pl1, pl1:2*pl1]
#coupling betwen per. and non. per part of the tip
h1_im = np.zeros((pl1, nbf1), complex)
s1_im = np.zeros((pl1, nbf1), complex)
h1_im[:pl1, :pl1], s1_im[:pl1, :pl1] = hs1_dij
hs1_dim = [h1_im, s1_im]
#periodic part the surface
hs2_dii = self.h20[:pl2, :pl2], self.s20[:pl2, :pl2]
hs2_dij = self.h20[pl2:2*pl2, :pl2], self.s20[pl2:2*pl2, :pl2]
#coupling betwen per. and non. per part of the surface
h2_im = np.zeros((pl2, nbf2), complex)
s2_im = np.zeros((pl2, nbf2), complex)
h2_im[-pl2:, -pl2:], s2_im[-pl2:, -pl2:] = hs2_dij
hs2_dim = [h2_im, s2_im]
#tip and surface greenfunction
self.selfenergy1 = LeadSelfEnergy(hs1_dii, hs1_dij, hs1_dim)
self.selfenergy2 = LeadSelfEnergy(hs2_dii, hs2_dij, hs2_dim)
self.greenfunction1 = GreenFunction(self.h1, self.s1,[self.selfenergy1])
self.greenfunction2 = GreenFunction(self.h2, self.s2,[self.selfenergy2])
#print 'shape of greenfunction tip:', np.shape(self.greenfunction1)
#print 'shape of greenfunction srf:', np.shape(self.greenfunction2)
#shift the bands due to the bias
self.selfenergy1.set_bias(0.0)
self.selfenergy2.set_bias(bias)
# extend the surface potentials and basis functions
self.setup_stm()
#tip and surface greenfunction matrices.
nbf1_small = self.ni #XXX Change this for efficiency in the future
nbf2_small = self.nj #XXX -||-
coupling_list1 = range(nbf1-nbf1_small,nbf1)# XXX -||-
coupling_list2 = range(0,nbf2_small)# XXX -||-
self.gft1_emm = np.zeros((nenergies, nbf1_small, nbf1_small), complex)
self.gft2_emm = np.zeros((nenergies, nbf2_small, nbf2_small), complex)
for e, energy in enumerate(self.energies):
gft1_mm = self.greenfunction1(energy)[coupling_list1]
gft1_mm = np.take(gft1_mm, coupling_list1, axis=1)
gft2_mm = self.greenfunction2(energy)[coupling_list2]
gft2_mm = np.take(gft2_mm, coupling_list2, axis=1)
self.gft1_emm[e] = gft1_mm
self.gft2_emm[e] = gft2_mm
class STM:
def __init__(self, srf, tip, srf_pl, tip_pl, eta1=1e-4, eta2=1e-4):
self.tip = tip # tip calcualtor
self.tip_pl = tip_pl # tip periodic layers calculator
self.srf = srf # surface calculator
self.srf_pl = srf_pl # surface periodic layers calculator
self.eta1 = eta1
self.eta2 = eta2
self.tgd = tip.gd
self.sgd = srf.gd
#assert tgd.h_cv == sgd.h_cv
#assert not (tgd.h_c - sgd.h_c).any()
def initialize(self, srf_atoms, tip_atoms, srf_coupling_atoms, tip_coupling_atoms,
srf_pl_atoms, tip_pl_atoms, energies, bias=0):
"""
srf_atoms: the atom index of onsite surface atoms
tip_atoms: the atom index of onsite tip atoms
energies: list of energies, for which the transmission function should be evaluated
bias: will be used to precalculate the greenfunctions of surface and tip
"""
self.bias = bias
self.energies = energies
self.tip_atoms = tip_atoms
self.srf_atoms = srf_atoms
self.srf_coupling_atoms = srf_coupling_atoms
self.tip_coupling_atoms = tip_coupling_atoms
#align potentials for onsite surface, onsite tip and tip principle layers according to surface_pl
srf_alignv = self.align_v(calc1 = self.srf_pl, index1=srf_pl_atoms[0], calc2=self.srf, index2=srf_atoms[0])
tip_alignv = srf_alignv + self.tip.get_fermi_level() - self.srf.get_fermi_level() #- bias
tip_pl_alignv = self.align_v(calc1=self.tip, index1=tip_atoms[-1], calc2=self.tip_pl, index2=tip_pl_atoms[-1])
tip_pl_alignv += tip_alignv
self.srf_alignv = srf_alignv
self.tip_alignv = tip_alignv
#hamiltoian and overlap matrix of onsite tip and srf
self.h1, self.s1 = Lead_HS(calc = self.tip, atom_list = tip_atoms, shift = srf_alignv-bias).get_HS()
print 'shape of onsite tip', np.shape(self.h1), np.shape(self.s1)
self.h2, self.s2 = Lead_HS(calc = self.srf, atom_list = srf_atoms, shift = tip_alignv).get_HS()
print 'shape of onsite srf', np.shape(self.h2), np.shape(self.s2)
#hamiltoian and overlap matrix of two tip and srf principle layers
self.h10, self.s10 = Lead_HS(calc = self.tip_pl, atom_list = tip_pl_atoms, shift = tip_pl_alignv).get_HS() #2 and only 2 principle layers
print 'shape of pl tip', np.shape(self.h10), np.shape(self.s10)
self.h20, self.s20 = Lead_HS(calc = self.srf_pl, atom_list = srf_pl_atoms, shift = -bias).get_HS() #2 and only 2 principle layers
print 'shape of pl srf', np.shape(self.h20), np.shape(self.s20)
nbf1, nbf2 = len(self.h1), len(self.h2) #No. of basis functions of onsite tip and surface atoms
pl1, pl2 = len(self.h10)/2, len(self.h20)/2 #No. of basis functions per principle layer of tip and srf
nenergies = len(energies)
#periodic part of the tip
hs1_dii = self.h10[:pl1, :pl1], self.s10[:pl1, :pl1]
hs1_dij = self.h10[:pl1, pl1:2*pl1], self.s10[:pl1, pl1:2*pl1]
#coupling betwen per. and non. per part of the tip
h1_im = np.zeros((pl1, nbf1), complex)
s1_im = np.zeros((pl1, nbf1), complex)
h1_im[:pl1, :pl1], s1_im[:pl1, :pl1] = hs1_dij
hs1_dim = [h1_im, s1_im]
#periodic part the surface
hs2_dii = self.h20[:pl2, :pl2], self.s20[:pl2, :pl2]
hs2_dij = self.h20[pl2:2*pl2, :pl2], self.s20[pl2:2*pl2, :pl2]
#coupling betwen per. and non. per part of the surface
h2_im = np.zeros((pl2, nbf2), complex)
s2_im = np.zeros((pl2, nbf2), complex)
h2_im[-pl2:, -pl2:], s2_im[-pl2:, -pl2:] = hs2_dij
hs2_dim = [h2_im, s2_im]
#tip and surface greenfunction
self.selfenergy1 = LeadSelfEnergy(hs1_dii, hs1_dij, hs1_dim)
self.selfenergy2 = LeadSelfEnergy(hs2_dii, hs2_dij, hs2_dim)
self.greenfunction1 = GreenFunction(self.h1, self.s1,[self.selfenergy1])
self.greenfunction2 = GreenFunction(self.h2, self.s2,[self.selfenergy2])
#print 'shape of greenfunction tip:', np.shape(self.greenfunction1)
#print 'shape of greenfunction srf:', np.shape(self.greenfunction2)
#shift the bands due to the bias
self.selfenergy1.set_bias(0.0)
self.selfenergy2.set_bias(bias)
# extend the surface potentials and basis functions
self.setup_stm()
#tip and surface greenfunction matrices.
nbf1_small = self.ni #XXX Change this for efficiency in the future
nbf2_small = self.nj #XXX -||-
coupling_list1 = range(nbf1-nbf1_small,nbf1)# XXX -||-
coupling_list2 = range(0,nbf2_small)# XXX -||-
self.gft1_emm = np.zeros((nenergies, nbf1_small, nbf1_small), complex)
self.gft2_emm = np.zeros((nenergies, nbf2_small, nbf2_small), complex)
for e, energy in enumerate(self.energies):
gft1_mm = self.greenfunction1(energy)[coupling_list1]
gft1_mm = np.take(gft1_mm, coupling_list1, axis=1)
gft2_mm = self.greenfunction2(energy)[coupling_list2]
gft2_mm = np.take(gft2_mm, coupling_list2, axis=1)
self.gft1_emm[e] = gft1_mm
self.gft2_emm[e] = gft2_mm
def scan(self,height=None,zmin=None,zmax=None):
if height is not None:
self.current = np.zeros((self.srf.gd.N_c[0],self.srf.gd.N_c[1]))
h = 0
h = np.round(height/Bohr/self.srf.gd.h_c[2]).astype(int)
for px in range(self.srf.gd.N_c[0]):
for py in range(self.srf.gd.N_c[1]):
pos = np.array([px,py,h])
V_12 = self.get_Vts(pos)
I = self.get_current(V_12)
self.current[px,py] = I
print 'I = ', I
return self.current
if height is None:
zmin_c = np.round(zmin/Bohr/self.srf.gd.h_c[2]).astype(int)
zmax_c = np.round(zmax/Bohr/self.srf.gd.h_c[2]).astype(int)
nz = zmax_c - zmin_c
self.current = np.zeros((self.srf.gd.N_c[0],self.srf.gd.N_c[1],nz))
for px in range(self.srf.gd.N_c[0]):
for py in range(self.srf.gd.N_c[1]):
for pz in range(zmin_c,zmax_c):
# get coupleing between tip and surface Vts for a given tip position on original srf grids
pos = np.array([px,py,pz])
V_12 = self.get_Vts(pos)
# get current values
I = self.get_current(V_12)
self.current[px,py,pz-zmin_c] = I
print I
return self.current
def get_current(self, v_12):
# get transmission
T_e = self.get_transmission(v_12)
I = -np.trapz(x = self.energies, y = T_e)
return I
def get_transmission(self, v_12):
nenergies = len(self.energies)
T_e = np.empty(nenergies,float)
v_21 = dagger(v_12)
for e, energy in enumerate(self.energies):
gft1 = self.gft1_emm[e]
gft2 = self.gft2_emm[e]
a1 = (gft1 - dagger(gft1))
a2 = (gft2 - dagger(gft2))
v12_a2 = np.dot(v_12, a2)
v21_a1 = np.dot(v_21, a1)
T = -np.trace(np.dot(v12_a2, v21_a1))
T_e[e] = T
self.T_e = T_e
return T_e
def get_Vts(self, tip_pos):
# tip_pos is in terms of grid points, showing the tip apex position respect to the surface grid
print 'tip_pos is', tip_pos
tip_apex_c = self.tip_apex_c[0]
srf_pos_av = self.srf.atoms.get_positions() / Bohr
srf_zmax = srf_pos_av[:,2].max()
srf_zmax_c = np.round(srf_zmax / self.srf.gd.h_c[2]).astype(int)
srf_ox = self.srf_extension[0] + tip_pos[0]
srf_oy = self.srf_extension[1] + tip_pos[1]
srf_oz = srf_zmax_c + tip_pos[2]
shift = np.array([srf_ox, srf_oy, srf_oz])
shift -= tip_apex_c
for tbox in self.tip_coupling_functions:
tbox.corner_c += shift
for tbox in self.tip_coupling_kinetics:
tbox.corner_c += shift
# get the combined effective potential on the extended surface grid
"""
This part needs to be updated for a separate xc calculation
"""
tip_ox = self.ox
tip_oy = self.oy
tip_oz = self.oz
srf_sx = srf_ox - self.nmx
srf_ex = srf_ox + self.npx
srf_sy = srf_oy - self.nmy
srf_ey = srf_oy + self.npy
srf_sz = srf_oz - self.nmz
srf_ez = srf_oz + self.npz
tip_sx = tip_ox - self.nmx
tip_ex = tip_ox + self.npx
tip_sy = tip_oy - self.nmy
tip_ey = tip_oy + self.npy
tip_sz = tip_oz - self.nmz
tip_ez = tip_oz + self.npz
V_tip = self.tip.get_effective_potential() - self.tip_alignv
V_couple = self.extvt_G.copy()
V_couple[srf_sx:srf_ex, srf_sy:srf_ey, srf_sz:srf_ez] = V_tip[tip_sx:tip_ex, tip_sy:tip_ey, tip_sz:tip_ez]
#get V_ts
V_ts = np.zeros((self.ni,self.nj))
for ns in range(len(self.srf_coupling_functions)):
sf = self.srf_coupling_functions[ns]
sk = self.srf_coupling_kinetics[ns]
j1 = sf[0].index
j2 = j1 + len(sf[0])
for t in self.tip_coupling_functions:
i1 = t.index
i2 = i1 + len(t)
test_overlap1 = []
test_overlap2 = []
for x in range(9):
overlap = (t | sf[x])
if overlap is not None:
test_overlap1.append(overlap.copy())
test_overlap2.append((np.abs(overlap)).sum())
else:
test_overlap1.append(overlap)
test_overlap2.append(overlap)
k = np.argmax(test_overlap2)
if test_overlap1[k] != None:
V_ts[i1:i2, j1:j2] += (t|sk[k])
V_ts[i1:i2, j1:j2] += (t|V_couple|sf[k])
# return the tbox back :)
for tbox in self.tip_coupling_functions:
tbox.corner_c -= shift
for tbox in self.tip_coupling_kinetics:
tbox.corner_c -= shift
#print 'np.shape(V_ts)',np.shape(V_ts)
return V_ts
def setup_stm(self):
tip_pos_av = self.tip.atoms.get_positions() / Bohr
srf_pos_av = self.srf.atoms.get_positions() / Bohr
# get selected tip functions on small grids and apply kinetics
self.tip_coupling_functions = []
self.tip_coupling_kinetics = []
i = 0
for a in self.tip_coupling_atoms:
setup = self.tip.wfs.setups[a]
spos_c = tip_pos_av[a] / self.tip.gd.cell_c #gives the scaled position of tip atoms in tip cell
for phit in setup.phit_j:
f = AtomCenteredFunctions(self.tip.gd, [phit], spos_c, i)
f_kinetic = f.apply_t()
self.tip_coupling_functions.append(f)
self.tip_coupling_kinetics.append(f_kinetic)
i += len(f.f_iG)
self.ni = i
#print self.tip_coupling_functions[1].size_c
#print self.tip_coupling_functions[1].corner_c
#print np.shape(self.tip_coupling_functions[1].f_iG)
# get tip cutoff and surface extension
tip_corner_c = []
temp_min = self.tip_coupling_kinetics[0].corner_c
temp_max = self.tip_coupling_kinetics[0].corner_c
for f in self.tip_coupling_kinetics:
start_c = np.minimum(f.corner_c, temp_min)
end_c = np.maximum(f.corner_c+f.size_c, temp_max)
temp_min = start_c
temp_max = end_c
#print "start_c",start_c # in terms of grid points
#print "end_c",end_c # in terms of grid points
self.tip_zmin = tip_pos_av[:,2].min()
self.tip_apex_index = np.where(tip_pos_av[:,2] < self.tip_zmin + 0.01)
self.tip_apex_c = np.round(tip_pos_av[self.tip_apex_index] / self.tgd.h_c).astype(int)
self.ox, self.oy, self.oz = self.tip_apex_c[0]
self.npx, self.npy, self.npz = np.round(end_c).astype(int) - self.tip_apex_c[0]
self.nmx, self.nmy, self.nmz = self.tip_apex_c[0] - np.round(start_c).astype(int)
# get the extension of srf basis functions and effective potentials
srf_extension = np.array([self.srf.gd.N_c[0], self.srf.gd.N_c[1], 0])
self.srf_extension = srf_extension
print 'extension of surface in terms of grids:', srf_extension
# get surface fucntions on small grid with periodic conditions with entension
self.srf_coupling_functions = []
self.srf_coupling_kinetics = []
j = 0
for a in self.srf_coupling_atoms:
setup = self.srf.wfs.setups[a]
spos_c = srf_pos_av[a] / self.srf.gd.cell_c
for phit in setup.phit_j:
f = AtomCenteredFunctions(self.srf.gd, [phit], spos_c, j)
f.corner_c += srf_extension
f_kinetic = f.apply_t()
self.srf_coupling_functions.append(f.periodic())
self.srf_coupling_kinetics.append(f_kinetic.periodic())
j += len(f.f_iG)
self.nj = j
#print np.shape(self.srf_coupling_functions[0][0].f_iG), self.srf_coupling_functions[0][0].corner_c
#print np.shape(self.srf_coupling_functions[0][8].f_iG), self.srf_coupling_functions[0][8].corner_c
# Extend the surface grid and surface effective potential
svt_G = self.srf.get_effective_potential() - self.srf_alignv + self.bias
ex,ey,ez = srf_extension
newsize_c = 2 * srf_extension + self.sgd.N_c
extvt_G = np.zeros(newsize_c)
extvt_G[ex:-ex,ey:-ey,:] = svt_G
extvt_G[:ex,ey:-ey,:] = svt_G[-ex:,:,:]
extvt_G[-ex:,ey:-ey,:] = svt_G[:ex,:,:]
extvt_G[:,:ey,:] = extvt_G[:,-2*ey:-ey,:]
extvt_G[:,-ey:,:] = extvt_G[:,ey:2*ey,:]
self.extvt_G = extvt_G
extsgd = GridDescriptor(N_c=newsize_c,
cell_cv=self.sgd.h_c*(newsize_c),
pbc_c=False,
comm=mpi.serial_comm)
## Transfer the coner_c of surface basis-functions and srf_kinetics to the extended surface cell
#for sbox in self.srf_coupling_functions:
# sbox.corner_c += srf_extension
#for sbox in self.srf_coupling_kinetics:
# sbox.corner_c += srf_extension
def align_v(self,calc1,index1,calc2,index2):
pos1 = calc1.atoms.positions[index1]
pos1_c = np.round(pos1/Bohr/calc1.wfs.gd.h_c).astype(int)
pos1_v = calc1.get_effective_potential()[pos1_c[0],pos1_c[1],pos1_c[2]]
pos2 = calc2.atoms.positions[index2]
pos2_c = np.round(pos2/Bohr/calc2.wfs.gd.h_c).astype(int)
pos2_v = calc2.get_effective_potential()[pos2_c[0],pos2_c[1],pos2_c[2]]
return pos2_v - pos1_v