def __init__(self, cell, kpts=numpy.zeros((1, 3))):
self.cell = cell
self.stdout = cell.stdout
self.verbose = cell.verbose
self.max_memory = cell.max_memory
self.kpts = kpts # default is gamma point
self.kpts_band = None
self.auxbasis = None
if cell.dimension == 0:
self.eta = 0.2
self.gs = cell.gs
else:
ke_cutoff = tools.gs_to_cutoff(cell.lattice_vectors(), cell.gs)
ke_cutoff = ke_cutoff[:cell.dimension].min()
self.eta = min(
aft.estimate_eta_for_ke_cutoff(cell, ke_cutoff,
cell.precision),
estimate_eta(cell, cell.precision))
ke_cutoff = aft.estimate_ke_cutoff_for_eta(cell, self.eta,
cell.precision)
self.gs = tools.cutoff_to_gs(cell.lattice_vectors(), ke_cutoff)
self.gs[cell.dimension:] = cell.gs[cell.dimension:]
# Not input options
self.exxdiv = None # to mimic KRHF/KUHF object in function get_coulG
self.auxcell = None
self.blockdim = 240
self.linear_dep_threshold = LINEAR_DEP_THR
self._j_only = False
# If _cderi_to_save is specified, the 3C-integral tensor will be saved in this file.
self._cderi_to_save = tempfile.NamedTemporaryFile(dir=lib.param.TMPDIR)
# If _cderi is specified, the 3C-integral tensor will be read from this file
self._cderi = None
self._keys = set(self.__dict__.keys())
def __init__(self, cell, kpts=numpy.zeros((1, 3))):
self.cell = cell
self.stdout = cell.stdout
self.verbose = cell.verbose
self.max_memory = cell.max_memory
self.kpts = kpts # default is gamma point
self.kpts_band = None
self._auxbasis = None
# Search for optimized eta and mesh here.
if cell.dimension == 0:
self.eta = 0.2
self.mesh = cell.mesh
else:
ke_cutoff = tools.mesh_to_cutoff(cell.lattice_vectors(), cell.mesh)
ke_cutoff = ke_cutoff[:cell.dimension].min()
eta_cell = aft.estimate_eta_for_ke_cutoff(cell, ke_cutoff,
cell.precision)
eta_guess = estimate_eta(cell, cell.precision)
if eta_cell < eta_guess:
self.eta = eta_cell
# TODO? Round off mesh to the nearest odd numbers.
# Odd number of grids is preferred because even number of
# grids may break the conjugation symmetry between the
# k-points k and -k.
#?self.mesh = [(n//2)*2+1 for n in cell.mesh]
self.mesh = cell.mesh
else:
self.eta = eta_guess
ke_cutoff = aft.estimate_ke_cutoff_for_eta(
cell, self.eta, cell.precision)
self.mesh = tools.cutoff_to_mesh(cell.lattice_vectors(),
ke_cutoff)
if cell.dimension < 2 or cell.low_dim_ft_type == 'inf_vacuum':
self.mesh[cell.dimension:] = cell.mesh[cell.dimension:]
# exp_to_discard to remove diffused fitting functions. The diffused
# fitting functions may cause linear dependency in DF metric. Removing
# the fitting functions whose exponents are smaller than exp_to_discard
# can improve the linear dependency issue. However, this parameter
# affects the quality of the auxiliary basis. The default value of
# this parameter was set to 0.2 in v1.5.1 or older and was changed to
# 0 since v1.5.2.
self.exp_to_discard = cell.exp_to_discard
# The following attributes are not input options.
self.exxdiv = None # to mimic KRHF/KUHF object in function get_coulG
self.auxcell = None
self.blockdim = getattr(__config__, 'pbc_df_df_DF_blockdim', 240)
self.linear_dep_threshold = LINEAR_DEP_THR
self._j_only = False
# If _cderi_to_save is specified, the 3C-integral tensor will be saved in this file.
self._cderi_to_save = tempfile.NamedTemporaryFile(dir=lib.param.TMPDIR)
# If _cderi is specified, the 3C-integral tensor will be read from this file
self._cderi = None
self._keys = set(self.__dict__.keys())
def __init__(self, cell, kpts=numpy.zeros((1,3))):
self.cell = cell
self.stdout = cell.stdout
self.verbose = cell.verbose
self.max_memory = cell.max_memory
self.kpts = kpts # default is gamma point
self.kpts_band = None
self._auxbasis = None
# Search for optimized eta and mesh here.
if cell.dimension == 0:
self.eta = 0.2
self.mesh = cell.mesh
else:
ke_cutoff = tools.mesh_to_cutoff(cell.lattice_vectors(), cell.mesh)
ke_cutoff = ke_cutoff[:cell.dimension].min()
eta_cell = aft.estimate_eta_for_ke_cutoff(cell, ke_cutoff, cell.precision)
eta_guess = estimate_eta(cell, cell.precision)
if eta_cell < eta_guess:
self.eta = eta_cell
# TODO? Round off mesh to the nearest odd numbers.
# Odd number of grids is preferred because even number of
# grids may break the conjugation symmetry between the
# k-points k and -k.
#?self.mesh = [(n//2)*2+1 for n in cell.mesh]
self.mesh = cell.mesh
else:
self.eta = eta_guess
ke_cutoff = aft.estimate_ke_cutoff_for_eta(cell, self.eta, cell.precision)
self.mesh = tools.cutoff_to_mesh(cell.lattice_vectors(), ke_cutoff)
if cell.dimension < 2 or cell.low_dim_ft_type == 'inf_vacuum':
self.mesh[cell.dimension:] = cell.mesh[cell.dimension:]
# exp_to_discard to remove diffused fitting functions. The diffused
# fitting functions may cause linear dependency in DF metric. Removing
# the fitting functions whose exponents are smaller than exp_to_discard
# can improve the linear dependency issue. However, this parameter
# affects the quality of the auxiliary basis. The default value of
# this parameter was set to 0.2 in v1.5.1 or older and was changed to
# 0 since v1.5.2.
self.exp_to_discard = cell.exp_to_discard
# The following attributes are not input options.
self.exxdiv = None # to mimic KRHF/KUHF object in function get_coulG
self.auxcell = None
self.blockdim = getattr(__config__, 'pbc_df_df_DF_blockdim', 240)
self.linear_dep_threshold = LINEAR_DEP_THR
self._j_only = False
# If _cderi_to_save is specified, the 3C-integral tensor will be saved in this file.
self._cderi_to_save = tempfile.NamedTemporaryFile(dir=lib.param.TMPDIR)
# If _cderi is specified, the 3C-integral tensor will be read from this file
self._cderi = None
self._keys = set(self.__dict__.keys())
def get_pbc_pvxp(cell, kpts=None):
import numpy
import copy
import time
from pyscf import lib
from pyscf.lib import logger
from pyscf.pbc import tools
from pyscf.gto import mole
from pyscf.pbc.df import ft_ao
from pyscf.pbc.df import aft_jk
from pyscf.pbc.df import aft
if kpts is None:
kpts_lst = numpy.zeros((1,3))
else:
kpts_lst = numpy.reshape(kpts, (-1,3))
log = logger.Logger(cell.stdout, cell.verbose)
t1 = t0 = (time.clock(), time.time())
mydf = aft.AFTDF(cell, kpts)
mydf.eta = 0.2
ke_guess = aft.estimate_ke_cutoff_for_eta(cell, mydf.eta, cell.precision)
mydf.mesh = tools.cutoff_to_mesh(cell.lattice_vectors(), ke_guess)
log.debug('mydf.mesh %s', mydf.mesh)
nkpts = len(kpts_lst)
nao = cell.nao_nr()
nao_pair = nao * (nao+1) // 2
Gv, Gvbase, kws = cell.get_Gv_weights(mydf.mesh)
charge = -cell.atom_charges() # Apply Koseki effective charge?
kpt_allow = numpy.zeros(3)
coulG = tools.get_coulG(cell, kpt_allow, mesh=mydf.mesh, Gv=Gv)
coulG *= kws
if mydf.eta == 0:
soc_mat = numpy.zeros((nkpts,3,nao*nao), dtype=numpy.complex128)
SI = cell.get_SI(Gv)
vG = numpy.einsum('i,ix->x', charge, SI) * coulG
else:
nuccell = copy.copy(cell)
half_sph_norm = .5/numpy.sqrt(numpy.pi)
norm = half_sph_norm/mole.gaussian_int(2, mydf.eta)
chg_env = [mydf.eta, norm]
ptr_eta = cell._env.size
ptr_norm = ptr_eta + 1
chg_bas = [[ia, 0, 1, 1, 0, ptr_eta, ptr_norm, 0] for ia in range(cell.natm)]
nuccell._atm = cell._atm
nuccell._bas = numpy.asarray(chg_bas, dtype=numpy.int32)
nuccell._env = numpy.hstack((cell._env, chg_env))
soc_mat = mydf._int_nuc_vloc(nuccell, kpts_lst, 'int3c2e_pvxp1_sph',
aosym='s1', comp=3)
soc_mat = numpy.asarray(soc_mat).reshape(nkpts,3,nao**2)
t1 = log.timer_debug1('pnucp pass1: analytic int', *t1)
aoaux = ft_ao.ft_ao(nuccell, Gv)
vG = numpy.einsum('i,xi->x', charge, aoaux) * coulG
max_memory = max(2000, mydf.max_memory-lib.current_memory()[0])
for aoaoks, p0, p1 in mydf.ft_loop(mydf.mesh, kpt_allow, kpts_lst,
max_memory=max_memory, aosym='s1',
intor='GTO_ft_pxp_sph', comp=3):
for k, aoao in enumerate(aoaoks):
aoao = aoao.reshape(3,-1,nao**2)
if aft_jk.gamma_point(kpts_lst[k]):
soc_mat[k] += numpy.einsum('k,ckx->cx', vG[p0:p1].real, aoao.real)
soc_mat[k] += numpy.einsum('k,ckx->cx', vG[p0:p1].imag, aoao.imag)
else:
soc_mat[k] += numpy.einsum('k,ckx->cx', vG[p0:p1].conj(), aoao)
t1 = log.timer_debug1('contracting pnucp', *t1)
soc_mat_kpts = []
for k, kpt in enumerate(kpts_lst):
if aft_jk.gamma_point(kpt):
soc_mat_kpts.append(soc_mat[k].real.reshape(3,nao,nao))
else:
soc_mat_kpts.append(soc_mat[k].reshape(3,nao,nao))
if kpts is None or numpy.shape(kpts) == (3,):
soc_mat_kpts = soc_mat_kpts[0]
return numpy.asarray(soc_mat_kpts)
