#!/usr/bin/env python
# Copyright 2021-2024 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Author: Xing Zhang <zhangxing.nju@gmail.com>
#
import ctypes
import numpy as np
from pyscf import __config__
from pyscf import lib
from pyscf.lib import logger
from pyscf.gto import moleintor
from pyscf.pbc import tools
from pyscf.pbc.lib.kpts_helper import gamma_point
from pyscf.pbc.df import fft
from pyscf.pbc.df.df_jk import (
_format_dms,
_format_kpts_band,
_format_jks,
)
from pyscf.pbc.dft.multigrid.pp import (
_get_vpplocG_part1,
_get_pp_without_erf,
vpploc_part1_nuc_grad,
)
from pyscf.pbc.dft.multigrid.utils import (
_take_4d,
_take_5d,
_takebak_4d,
_takebak_5d,
)
from pyscf.pbc.dft.multigrid.multigrid import MultiGridFFTDF
NGRIDS = getattr(__config__, 'pbc_dft_multigrid_ngrids', 4)
KE_RATIO = getattr(__config__, 'pbc_dft_multigrid_ke_ratio', 3.0)
REL_CUTOFF = getattr(__config__, 'pbc_dft_multigrid_rel_cutoff', 20.0)
GGA_METHOD = getattr(__config__, 'pbc_dft_multigrid_gga_method', 'FFT')
EXTRA_PREC = getattr(__config__, 'pbc_gto_eval_gto_extra_precision', 1e-2)
RHOG_HIGH_ORDER = getattr(__config__, 'pbc_dft_multigrid_rhog_high_order', False)
PTR_EXPDROP = 16
EXPDROP = getattr(__config__, 'pbc_dft_multigrid_expdrop', 1e-12)
IMAG_TOL = 1e-9
libdft = lib.load_library('libdft')
[docs]
def gradient_gs(f_gs, Gv):
r'''Compute the G-space components of :math:`\nabla f(r)`
given :math:`f(G)` and :math:`G`,
which is equivalent to einsum('np,px->nxp', f_gs, 1j*Gv)
'''
ng, dim = Gv.shape
assert dim == 3
Gv = np.asarray(Gv, order='C', dtype=np.double)
f_gs = np.asarray(f_gs.reshape(-1,ng), order='C', dtype=np.complex128)
n = f_gs.shape[0]
out = np.empty((n,dim,ng), dtype=np.complex128)
fn = getattr(libdft, 'gradient_gs', None)
try:
fn(out.ctypes.data_as(ctypes.c_void_p),
f_gs.ctypes.data_as(ctypes.c_void_p),
Gv.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(n), ctypes.c_size_t(ng))
except Exception as e:
raise RuntimeError(f'Error in gradient_gs: {e}')
return out
[docs]
class GridLevel_Info(ctypes.Structure):
'''
Info about the grid levels.
'''
_fields_ = [("nlevels", ctypes.c_int), # number of grid levels
("rel_cutoff", ctypes.c_double),
("cutoff", ctypes.POINTER(ctypes.c_double)),
("mesh", ctypes.POINTER(ctypes.c_int))]
[docs]
class RS_Grid(ctypes.Structure):
'''
Values on real space multigrid.
'''
_fields_ = [("nlevels", ctypes.c_int),
("gridlevel_info", ctypes.POINTER(GridLevel_Info)),
("comp", ctypes.c_int),
# data is list of 1d arrays
("data", ctypes.POINTER(ctypes.POINTER(ctypes.c_double)))]
[docs]
class PGFPair(ctypes.Structure):
'''
A primitive Gaussian function pair.
'''
_fields_ = [("ish", ctypes.c_int),
("ipgf", ctypes.c_int),
("jsh", ctypes.c_int),
("jpgf", ctypes.c_int),
("iL", ctypes.c_int),
("radius", ctypes.c_double)]
[docs]
class Task(ctypes.Structure):
'''
A single task.
'''
_fields_ = [("buf_size", ctypes.c_size_t),
("ntasks", ctypes.c_size_t),
("pgfpairs", ctypes.POINTER(ctypes.POINTER(PGFPair))),
("radius", ctypes.c_double)]
[docs]
class TaskList(ctypes.Structure):
'''
A task list.
'''
_fields_ = [("nlevels", ctypes.c_int),
("hermi", ctypes.c_int),
("gridlevel_info", ctypes.POINTER(GridLevel_Info)),
("tasks", ctypes.POINTER(ctypes.POINTER(Task)))]
[docs]
def multi_grids_tasks(cell, ke_cutoff=None, hermi=0,
ngrids=NGRIDS, ke_ratio=KE_RATIO, rel_cutoff=REL_CUTOFF):
if ke_cutoff is None:
ke_cutoff = cell.ke_cutoff
if ke_cutoff is None:
raise ValueError("cell.ke_cutoff is not set.")
ke1 = ke_cutoff
cutoff = [ke1,]
for i in range(ngrids-1):
ke1 /= ke_ratio
cutoff.append(ke1)
cutoff.reverse()
a = cell.lattice_vectors()
mesh = []
for ke in cutoff:
mesh.append(tools.cutoff_to_mesh(a, ke))
logger.info(cell, 'ke_cutoff for multigrid tasks:\n%s', cutoff)
logger.info(cell, 'meshes for multigrid tasks:\n%s', mesh)
gridlevel_info = init_gridlevel_info(cutoff, rel_cutoff, mesh)
task_list = build_task_list(cell, gridlevel_info, hermi=hermi)
return task_list
def _update_task_list(mydf, hermi=0, ngrids=None, ke_ratio=None, rel_cutoff=None):
'''
Update :attr:`task_list` if necessary.
'''
cell = mydf.cell
if ngrids is None:
ngrids = mydf.ngrids
if ke_ratio is None:
ke_ratio = mydf.ke_ratio
if rel_cutoff is None:
rel_cutoff = mydf.rel_cutoff
need_update = False
task_list = getattr(mydf, 'task_list', None)
if task_list is None:
need_update = True
else:
hermi_orig = task_list.contents.hermi
nlevels = task_list.contents.nlevels
rel_cutoff_orig = task_list.contents.gridlevel_info.contents.rel_cutoff
#TODO also need to check kenetic energy cutoff change
if (hermi_orig > hermi or
nlevels != ngrids or
abs(rel_cutoff_orig-rel_cutoff) > 1e-12):
need_update = True
if need_update:
if task_list is not None:
free_task_list(task_list)
task_list = multi_grids_tasks(cell, hermi=hermi, ngrids=ngrids,
ke_ratio=ke_ratio, rel_cutoff=rel_cutoff)
mydf.task_list = task_list
return task_list
[docs]
def init_gridlevel_info(cutoff, rel_cutoff, mesh):
if cutoff[0] < 1e-15:
cutoff = cutoff[1:]
cutoff = np.asarray(cutoff, order='C', dtype=np.double)
mesh = np.asarray(np.asarray(mesh).reshape(-1,3), order='C', dtype=np.int32)
nlevels = len(cutoff)
gridlevel_info = ctypes.POINTER(GridLevel_Info)()
fn = getattr(libdft, "init_gridlevel_info", None)
try:
fn(ctypes.byref(gridlevel_info),
cutoff.ctypes.data_as(ctypes.c_void_p),
mesh.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(nlevels), ctypes.c_double(rel_cutoff))
except Exception as e:
raise RuntimeError("Failed to init grid level info. %s" % e)
return gridlevel_info
[docs]
def free_gridlevel_info(gridlevel_info):
fn = getattr(libdft, "del_gridlevel_info", None)
try:
fn(ctypes.byref(gridlevel_info))
except Exception as e:
raise RuntimeError("Failed to free grid level info. %s" % e)
[docs]
def init_rs_grid(gridlevel_info, comp):
'''
Initialize values on real space multigrid
'''
rs_grid = ctypes.POINTER(RS_Grid)()
fn = getattr(libdft, "init_rs_grid", None)
try:
fn(ctypes.byref(rs_grid),
ctypes.byref(gridlevel_info),
ctypes.c_int(comp))
except Exception as e:
raise RuntimeError("Failed to initialize real space multigrid data. %s" % e)
return rs_grid
[docs]
def free_rs_grid(rs_grid):
fn = getattr(libdft, "del_rs_grid", None)
try:
fn(ctypes.byref(rs_grid))
except Exception as e:
raise RuntimeError("Failed to free real space multigrid data. %s" % e)
[docs]
def build_task_list(cell, gridlevel_info, cell1=None, Ls=None, hermi=0, precision=None):
'''
Build the task list for multigrid DFT calculations.
Arguments:
cell : :class:`pbc.gto.cell.Cell`
The :class:`Cell` instance for the bra basis functions.
gridlevel_info : :class:`ctypes.POINTER`
The C pointer of the :class:`GridLevel_Info` structure.
cell1 : :class:`pbc.gto.cell.Cell`, optional
The :class:`Cell` instance for the ket basis functions.
If not given, both bra and ket basis functions come from cell.
Ls : (*,3) array, optional
The cartesian coordinates of the periodic images.
Default is calculated by :func:`cell.get_lattice_Ls`.
hermi : int, optional
If :math:`hermi=1`, the task list is built only for
the upper triangle of the matrix. Default is 0.
precision : float, optional
The integral precision. Default is :attr:`cell.precision`.
Returns: :class:`ctypes.POINTER`
The C pointer of the :class:`TaskList` structure.
'''
from pyscf.pbc.gto import build_neighbor_list_for_shlpairs, free_neighbor_list
if cell1 is None:
cell1 = cell
if Ls is None:
Ls = cell.get_lattice_Ls()
if precision is None:
precision = cell.precision
if hermi == 1 and cell1 is not cell:
logger.warn(cell,
"Set hermi=0 because cell and cell1 are not the same.")
hermi = 0
ish_atm = np.asarray(cell._atm, order='C', dtype=np.int32)
ish_bas = np.asarray(cell._bas, order='C', dtype=np.int32)
ish_env = np.asarray(cell._env, order='C', dtype=float)
nish = len(ish_bas)
ish_rcut, ipgf_rcut = cell.rcut_by_shells(precision=precision,
return_pgf_radius=True)
assert nish == len(ish_rcut)
ptr_ipgf_rcut = lib.ndarray_pointer_2d(ipgf_rcut)
if cell1 is cell:
jsh_atm = ish_atm
jsh_bas = ish_bas
jsh_env = ish_env
jsh_rcut = ish_rcut
jpgf_rcut = ipgf_rcut
ptr_jpgf_rcut = ptr_ipgf_rcut
else:
jsh_atm = np.asarray(cell1._atm, order='C', dtype=np.int32)
jsh_bas = np.asarray(cell1._bas, order='C', dtype=np.int32)
jsh_env = np.asarray(cell1._env, order='C', dtype=float)
jsh_rcut, jpgf_rcut = cell1.rcut_by_shells(precision=precision,
return_pgf_radius=True)
ptr_jpgf_rcut = lib.ndarray_pointer_2d(jpgf_rcut)
njsh = len(jsh_bas)
assert njsh == len(jsh_rcut)
nl = build_neighbor_list_for_shlpairs(cell, cell1, Ls=Ls,
ish_rcut=ish_rcut, jsh_rcut=jsh_rcut,
hermi=hermi)
task_list = ctypes.POINTER(TaskList)()
func = getattr(libdft, "build_task_list", None)
try:
func(ctypes.byref(task_list),
ctypes.byref(nl), ctypes.byref(gridlevel_info),
ish_atm.ctypes.data_as(ctypes.c_void_p),
ish_bas.ctypes.data_as(ctypes.c_void_p),
ish_env.ctypes.data_as(ctypes.c_void_p),
ish_rcut.ctypes.data_as(ctypes.c_void_p),
ptr_ipgf_rcut.ctypes,
jsh_atm.ctypes.data_as(ctypes.c_void_p),
jsh_bas.ctypes.data_as(ctypes.c_void_p),
jsh_env.ctypes.data_as(ctypes.c_void_p),
jsh_rcut.ctypes.data_as(ctypes.c_void_p),
ptr_jpgf_rcut.ctypes,
ctypes.c_int(nish), ctypes.c_int(njsh),
Ls.ctypes.data_as(ctypes.c_void_p),
ctypes.c_double(precision), ctypes.c_int(hermi))
except Exception as e:
raise RuntimeError("Failed to build task list. %s" % e)
free_neighbor_list(nl)
return task_list
[docs]
def free_task_list(task_list):
'''
Note:
This will also free task_list.contents.gridlevel_info.
'''
if task_list is None:
return
func = getattr(libdft, "del_task_list", None)
try:
func(ctypes.byref(task_list))
except Exception as e:
raise RuntimeError("Failed to free task list. %s" % e)
[docs]
def eval_rho(cell, dm, task_list, shls_slice=None, hermi=0, xctype='LDA', kpts=None,
dimension=None, cell1=None, shls_slice1=None, Ls=None,
a=None, ignore_imag=False):
'''
Collocate density (opt. gradients) on the real-space grid.
The two sets of Gaussian functions can be different.
Returns:
rho: RS_Grid object
Densities on real space multigrids.
'''
cell0 = cell
shls_slice0 = shls_slice
if cell1 is None:
cell1 = cell0
#TODO mixture of cartesian and spherical bases
assert cell0.cart == cell1.cart
ish_atm = np.asarray(cell0._atm, order='C', dtype=np.int32)
ish_bas = np.asarray(cell0._bas, order='C', dtype=np.int32)
ish_env = np.asarray(cell0._env, order='C', dtype=np.double)
ish_env[PTR_EXPDROP] = min(cell0.precision*EXTRA_PREC, EXPDROP)
if cell1 is cell0:
jsh_atm = ish_atm
jsh_bas = ish_bas
jsh_env = ish_env
else:
jsh_atm = np.asarray(cell1._atm, order='C', dtype=np.int32)
jsh_bas = np.asarray(cell1._bas, order='C', dtype=np.int32)
jsh_env = np.asarray(cell1._env, order='C', dtype=np.double)
jsh_env[PTR_EXPDROP] = min(cell1.precision*EXTRA_PREC, EXPDROP)
if shls_slice0 is None:
shls_slice0 = (0, cell0.nbas)
i0, i1 = shls_slice0
if shls_slice1 is None:
shls_slice1 = shls_slice0
j0, j1 = shls_slice1
if hermi == 1:
assert cell1 is cell0
assert i0 == j0 and i1 == j1
key0 = 'cart' if cell0.cart else 'sph'
ao_loc0 = moleintor.make_loc(ish_bas, key0)
naoi = ao_loc0[i1] - ao_loc0[i0]
if hermi == 1:
ao_loc1 = ao_loc0
else:
key1 = 'cart' if cell1.cart else 'sph'
ao_loc1 = moleintor.make_loc(jsh_bas, key1)
naoj = ao_loc1[j1] - ao_loc1[j0]
dm = np.asarray(dm, order='C')
assert dm.shape[-2:] == (naoi, naoj)
if dimension is None:
dimension = cell0.dimension
assert dimension == getattr(cell1, "dimension", None)
if Ls is None and dimension > 0:
Ls = np.asarray(cell0.get_lattice_Ls(), order='C')
elif Ls is None and dimension == 0:
Ls = np.zeros((1,3))
if dimension == 0 or kpts is None or gamma_point(kpts):
nkpts, nimgs = 1, Ls.shape[0]
dm = dm.reshape(-1,1,naoi,naoj)
else:
expkL = np.exp(1j*kpts.reshape(-1,3).dot(Ls.T))
nkpts, nimgs = expkL.shape
dm = dm.reshape(-1,nkpts,naoi,naoj)
n_dm = dm.shape[0]
#TODO check if cell1 has the same lattice vectors
if a is None:
a = cell0.lattice_vectors()
b = np.linalg.inv(a.T)
if abs(a-np.diag(a.diagonal())).max() < 1e-12:
lattice_type = '_orth'
else:
lattice_type = '_nonorth'
xctype = xctype.upper()
if xctype == 'LDA':
comp = 1
elif xctype == 'GGA':
if hermi == 1:
raise RuntimeError('hermi=1 is not supported for GGA functional')
comp = 4
else:
raise NotImplementedError('meta-GGA')
eval_fn = 'make_rho_' + xctype.lower() + lattice_type
drv = getattr(libdft, "grid_collocate_drv", None)
def make_rho_(rs_rho, dm):
try:
drv(getattr(libdft, eval_fn, None),
ctypes.byref(rs_rho),
dm.ctypes.data_as(ctypes.c_void_p),
ctypes.byref(task_list),
ctypes.c_int(comp), ctypes.c_int(hermi),
(ctypes.c_int*4)(i0, i1, j0, j1),
ao_loc0.ctypes.data_as(ctypes.c_void_p),
ao_loc1.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(dimension),
Ls.ctypes.data_as(ctypes.c_void_p),
a.ctypes.data_as(ctypes.c_void_p),
b.ctypes.data_as(ctypes.c_void_p),
ish_atm.ctypes.data_as(ctypes.c_void_p),
ish_bas.ctypes.data_as(ctypes.c_void_p),
ish_env.ctypes.data_as(ctypes.c_void_p),
jsh_atm.ctypes.data_as(ctypes.c_void_p),
jsh_bas.ctypes.data_as(ctypes.c_void_p),
jsh_env.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(cell0.cart))
except Exception as e:
raise RuntimeError("Failed to compute rho. %s" % e)
return rs_rho
gridlevel_info = task_list.contents.gridlevel_info
rho = []
for i, dm_i in enumerate(dm):
rs_rho = init_rs_grid(gridlevel_info, comp)
if dimension == 0 or kpts is None or gamma_point(kpts):
make_rho_(rs_rho, dm_i)
else:
raise NotImplementedError
rho.append(rs_rho)
if n_dm == 1:
rho = rho[0]
return rho
def _eval_rhoG(mydf, dm_kpts, hermi=1, kpts=np.zeros((1,3)), deriv=0,
rhog_high_order=RHOG_HIGH_ORDER):
assert(deriv < 2)
cell = mydf.cell
dm_kpts = np.asarray(dm_kpts, order='C')
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
task_list = _update_task_list(mydf, hermi=hermi, ngrids=mydf.ngrids,
ke_ratio=mydf.ke_ratio, rel_cutoff=mydf.rel_cutoff)
gga_high_order = False
if deriv == 0:
xctype = 'LDA'
rhodim = 1
elif deriv == 1:
if rhog_high_order:
xctype = 'GGA'
rhodim = 4
else: # approximate high order derivatives in reciprocal space
gga_high_order = True
xctype = 'LDA'
rhodim = 1
deriv = 0
assert(hermi == 1 or gamma_point(kpts))
elif deriv == 2: # meta-GGA
raise NotImplementedError
assert(hermi == 1 or gamma_point(kpts))
ignore_imag = (hermi == 1)
rs_rho = eval_rho(cell, dms, task_list, hermi=hermi, xctype=xctype, kpts=kpts,
ignore_imag=ignore_imag)
nx, ny, nz = mydf.mesh
rhoG = np.zeros((nset*rhodim,nx,ny,nz), dtype=np.complex128)
nlevels = task_list.contents.nlevels
meshes = task_list.contents.gridlevel_info.contents.mesh
meshes = np.ctypeslib.as_array(meshes, shape=(nlevels,3))
for ilevel in range(nlevels):
mesh = meshes[ilevel]
ngrids = np.prod(mesh)
if nset > 1:
rho = []
for i in range(nset):
rho.append(np.ctypeslib.as_array(rs_rho[i].contents.data[ilevel], shape=(ngrids,)))
rho = np.asarray(rho)
else:
rho = np.ctypeslib.as_array(rs_rho.contents.data[ilevel], shape=(ngrids,))
weight = 1./nkpts * cell.vol/ngrids
rho_freq = tools.fft(rho.reshape(nset*rhodim, -1), mesh)
rho = None
rho_freq *= weight
gx = np.fft.fftfreq(mesh[0], 1./mesh[0]).astype(np.int32)
gy = np.fft.fftfreq(mesh[1], 1./mesh[1]).astype(np.int32)
gz = np.fft.fftfreq(mesh[2], 1./mesh[2]).astype(np.int32)
_takebak_4d(rhoG, rho_freq.reshape((-1,) + tuple(mesh)), (None, gx, gy, gz))
rho_freq = None
if nset > 1:
for i in range(nset):
free_rs_grid(rs_rho[i])
else:
free_rs_grid(rs_rho)
rs_rho = None
rhoG = rhoG.reshape(nset,rhodim,-1)
if gga_high_order:
Gv = cell.get_Gv(mydf.mesh)
#:rhoG1 = np.einsum('np,px->nxp', 1j*rhoG[:,0], Gv)
rhoG1 = gradient_gs(rhoG[:,0], Gv)
rhoG = np.concatenate([rhoG, rhoG1], axis=1)
Gv = rhoG1 = None
return rhoG
[docs]
def eval_mat(cell, weights, task_list, shls_slice=None, comp=1, hermi=0, deriv=0,
xctype='LDA', kpts=None, grid_level=None, dimension=None, mesh=None,
cell1=None, shls_slice1=None, Ls=None, a=None):
cell0 = cell
shls_slice0 = shls_slice
if cell1 is None:
cell1 = cell0
if mesh is None:
mesh = cell0.mesh
#TODO mixture of cartesian and spherical bases
assert cell0.cart == cell1.cart
ish_atm = np.asarray(cell0._atm, order='C', dtype=np.int32)
ish_bas = np.asarray(cell0._bas, order='C', dtype=np.int32)
ish_env = np.asarray(cell0._env, order='C', dtype=np.double)
ish_env[PTR_EXPDROP] = min(cell0.precision*EXTRA_PREC, EXPDROP)
if cell1 is cell0:
jsh_atm = ish_atm
jsh_bas = ish_bas
jsh_env = ish_env
else:
jsh_atm = np.asarray(cell1._atm, order='C', dtype=np.int32)
jsh_bas = np.asarray(cell1._bas, order='C', dtype=np.int32)
jsh_env = np.asarray(cell1._env, order='C', dtype=np.double)
jsh_env[PTR_EXPDROP] = min(cell1.precision*EXTRA_PREC, EXPDROP)
if shls_slice0 is None:
shls_slice0 = (0, cell0.nbas)
i0, i1 = shls_slice0
if shls_slice1 is None:
shls_slice1 = (0, cell1.nbas)
j0, j1 = shls_slice1
if hermi == 1:
assert cell1 is cell0
assert i0 == j0 and i1 == j1
key0 = 'cart' if cell0.cart else 'sph'
ao_loc0 = moleintor.make_loc(ish_bas, key0)
naoi = ao_loc0[i1] - ao_loc0[i0]
if hermi == 1:
ao_loc1 = ao_loc0
else:
key1 = 'cart' if cell1.cart else 'sph'
ao_loc1 = moleintor.make_loc(jsh_bas, key1)
naoj = ao_loc1[j1] - ao_loc1[j0]
if dimension is None:
dimension = cell0.dimension
assert dimension == getattr(cell1, "dimension", None)
if Ls is None and dimension > 0:
Ls = np.asarray(cell0.get_lattice_Ls(), order='C')
elif Ls is None and dimension == 0:
Ls = np.zeros((1,3))
if dimension == 0 or kpts is None or gamma_point(kpts):
nkpts, nimgs = 1, Ls.shape[0]
else:
expkL = np.exp(1j*kpts.reshape(-1,3).dot(Ls.T))
nkpts, nimgs = expkL.shape
#TODO check if cell1 has the same lattice vectors
if a is None:
a = cell0.lattice_vectors()
b = np.linalg.inv(a.T)
if abs(a-np.diag(a.diagonal())).max() < 1e-12:
lattice_type = '_orth'
else:
lattice_type = '_nonorth'
weights = np.asarray(weights, order='C')
assert(weights.dtype == np.double)
xctype = xctype.upper()
n_mat = None
if xctype == 'LDA':
if weights.ndim == 1:
weights = weights.reshape(-1, np.prod(mesh))
else:
n_mat = weights.shape[0]
elif xctype == 'GGA':
if weights.ndim == 2:
weights = weights.reshape(-1, 4, np.prod(mesh))
else:
n_mat = weights.shape[0]
else:
raise NotImplementedError
eval_fn = 'eval_mat_' + xctype.lower() + lattice_type
if deriv > 0:
if deriv == 1:
assert comp == 3
assert hermi == 0
eval_fn += '_ip1'
else:
raise NotImplementedError
drv = getattr(libdft, "grid_integrate_drv", None)
def make_mat(wv):
if comp == 1:
mat = np.zeros((naoi, naoj))
else:
mat = np.zeros((comp, naoi, naoj))
try:
drv(getattr(libdft, eval_fn, None),
mat.ctypes.data_as(ctypes.c_void_p),
wv.ctypes.data_as(ctypes.c_void_p),
ctypes.byref(task_list),
ctypes.c_int(comp), ctypes.c_int(hermi),
ctypes.c_int(grid_level),
(ctypes.c_int*4)(i0, i1, j0, j1),
ao_loc0.ctypes.data_as(ctypes.c_void_p),
ao_loc1.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(dimension),
Ls.ctypes.data_as(ctypes.c_void_p),
a.ctypes.data_as(ctypes.c_void_p),
b.ctypes.data_as(ctypes.c_void_p),
ish_atm.ctypes.data_as(ctypes.c_void_p),
ish_bas.ctypes.data_as(ctypes.c_void_p),
ish_env.ctypes.data_as(ctypes.c_void_p),
jsh_atm.ctypes.data_as(ctypes.c_void_p),
jsh_bas.ctypes.data_as(ctypes.c_void_p),
jsh_env.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(cell0.cart))
except Exception as e:
raise RuntimeError("Failed to compute rho. %s" % e)
return mat
out = []
for wv in weights:
if dimension == 0 or kpts is None or gamma_point(kpts):
mat = make_mat(wv)
else:
raise NotImplementedError
out.append(mat)
if n_mat is None:
out = out[0]
return out
def _get_j_pass2(mydf, vG, kpts=np.zeros((1,3)), hermi=1, verbose=None):
cell = mydf.cell
nkpts = len(kpts)
nao = cell.nao_nr()
nx, ny, nz = mydf.mesh
vG = vG.reshape(-1,nx,ny,nz)
nset = vG.shape[0]
task_list = _update_task_list(mydf, hermi=hermi, ngrids=mydf.ngrids,
ke_ratio=mydf.ke_ratio, rel_cutoff=mydf.rel_cutoff)
at_gamma_point = gamma_point(kpts)
if at_gamma_point:
vj_kpts = np.zeros((nset,nkpts,nao,nao))
else:
vj_kpts = np.zeros((nset,nkpts,nao,nao), dtype=np.complex128)
nlevels = task_list.contents.nlevels
meshes = task_list.contents.gridlevel_info.contents.mesh
meshes = np.ctypeslib.as_array(meshes, shape=(nlevels,3))
for ilevel in range(nlevels):
mesh = meshes[ilevel]
ngrids = np.prod(mesh)
gx = np.fft.fftfreq(mesh[0], 1./mesh[0]).astype(np.int32)
gy = np.fft.fftfreq(mesh[1], 1./mesh[1]).astype(np.int32)
gz = np.fft.fftfreq(mesh[2], 1./mesh[2]).astype(np.int32)
sub_vG = _take_4d(vG, (None, gx, gy, gz)).reshape(nset,ngrids)
v_rs = tools.ifft(sub_vG, mesh).reshape(nset,ngrids)
vR = np.asarray(v_rs.real, order='C')
vI = np.asarray(v_rs.imag, order='C')
if at_gamma_point:
v_rs = vR
mat = eval_mat(cell, vR, task_list, comp=1, hermi=hermi,
xctype='LDA', kpts=kpts, grid_level=ilevel, mesh=mesh)
vj_kpts += np.asarray(mat).reshape(nset,-1,nao,nao)
if not at_gamma_point and abs(vI).max() > IMAG_TOL:
raise NotImplementedError
if nset == 1:
vj_kpts = vj_kpts[0]
return vj_kpts
def _get_j_pass2_ip1(mydf, vG, kpts=np.zeros((1,3)), hermi=0, deriv=1, verbose=None):
if deriv == 1:
comp = 3
assert hermi == 0
else:
raise NotImplementedError
cell = mydf.cell
nkpts = len(kpts)
nao = cell.nao_nr()
nx, ny, nz = mydf.mesh
vG = vG.reshape(-1,nx,ny,nz)
nset = vG.shape[0]
task_list = _update_task_list(mydf, hermi=hermi, ngrids=mydf.ngrids,
ke_ratio=mydf.ke_ratio, rel_cutoff=mydf.rel_cutoff)
at_gamma_point = gamma_point(kpts)
if at_gamma_point:
vj_kpts = np.zeros((nset,nkpts,comp,nao,nao))
else:
vj_kpts = np.zeros((nset,nkpts,comp,nao,nao), dtype=np.complex128)
nlevels = task_list.contents.nlevels
meshes = task_list.contents.gridlevel_info.contents.mesh
meshes = np.ctypeslib.as_array(meshes, shape=(nlevels,3))
for ilevel in range(nlevels):
mesh = meshes[ilevel]
ngrids = np.prod(mesh)
gx = np.fft.fftfreq(mesh[0], 1./mesh[0]).astype(np.int32)
gy = np.fft.fftfreq(mesh[1], 1./mesh[1]).astype(np.int32)
gz = np.fft.fftfreq(mesh[2], 1./mesh[2]).astype(np.int32)
sub_vG = _take_4d(vG, (None, gx, gy, gz)).reshape(nset,ngrids)
v_rs = tools.ifft(sub_vG, mesh).reshape(nset,ngrids)
if at_gamma_point:
vR = np.asarray(v_rs.real, order='C', dtype=float)
#vI = None
else:
raise NotImplementedError
mat = eval_mat(cell, vR, task_list, comp=comp, hermi=hermi, deriv=deriv,
xctype='LDA', kpts=kpts, grid_level=ilevel, mesh=mesh)
mat = np.asarray(mat).reshape(nset,-1,comp,nao,nao)
vj_kpts = np.add(vj_kpts, mat, out=vj_kpts)
if nset == 1:
vj_kpts = vj_kpts[0]
return vj_kpts
def _get_gga_pass2(mydf, vG, kpts=np.zeros((1,3)), hermi=1, verbose=None):
cell = mydf.cell
nkpts = len(kpts)
nao = cell.nao_nr()
nx, ny, nz = mydf.mesh
vG = vG.reshape(-1,4,nx,ny,nz)
nset = vG.shape[0]
task_list = _update_task_list(mydf, hermi=hermi, ngrids=mydf.ngrids,
ke_ratio=mydf.ke_ratio, rel_cutoff=mydf.rel_cutoff)
if gamma_point(kpts):
veff = np.zeros((nset,nkpts,nao,nao))
else:
veff = np.zeros((nset,nkpts,nao,nao), dtype=np.complex128)
nlevels = task_list.contents.nlevels
meshes = task_list.contents.gridlevel_info.contents.mesh
meshes = np.ctypeslib.as_array(meshes, shape=(nlevels,3))
for ilevel in range(nlevels):
mesh = meshes[ilevel]
ngrids = np.prod(mesh)
gx = np.fft.fftfreq(mesh[0], 1./mesh[0]).astype(np.int32)
gy = np.fft.fftfreq(mesh[1], 1./mesh[1]).astype(np.int32)
gz = np.fft.fftfreq(mesh[2], 1./mesh[2]).astype(np.int32)
sub_vG = _take_5d(vG, (None, None, gx, gy, gz)).reshape(-1,ngrids)
wv = tools.ifft(sub_vG, mesh).real.reshape(nset,4,ngrids)
wv = np.asarray(wv, order='C')
mat = eval_mat(cell, wv, task_list, comp=1, hermi=hermi,
xctype='GGA', kpts=kpts, grid_level=ilevel, mesh=mesh)
mat = np.asarray(mat).reshape(nset,-1,nao,nao)
veff = np.add(veff, mat, out=veff)
if not gamma_point(kpts):
raise NotImplementedError
if nset == 1:
veff = veff[0]
return veff
def _get_gga_pass2_ip1(mydf, vG, kpts=np.zeros((1,3)), hermi=0, deriv=1, verbose=None):
if deriv == 1:
comp = 3
assert hermi == 0
else:
raise NotImplementedError
cell = mydf.cell
nkpts = len(kpts)
nao = cell.nao_nr()
nx, ny, nz = mydf.mesh
vG = vG.reshape(-1,4,nx,ny,nz)
nset = vG.shape[0]
task_list = _update_task_list(mydf, hermi=hermi, ngrids=mydf.ngrids,
ke_ratio=mydf.ke_ratio, rel_cutoff=mydf.rel_cutoff)
at_gamma_point = gamma_point(kpts)
if at_gamma_point:
vj_kpts = np.zeros((nset,nkpts,comp,nao,nao))
else:
vj_kpts = np.zeros((nset,nkpts,comp,nao,nao), dtype=np.complex128)
nlevels = task_list.contents.nlevels
meshes = task_list.contents.gridlevel_info.contents.mesh
meshes = np.ctypeslib.as_array(meshes, shape=(nlevels,3))
for ilevel in range(nlevels):
mesh = meshes[ilevel]
ngrids = np.prod(mesh)
gx = np.fft.fftfreq(mesh[0], 1./mesh[0]).astype(np.int32)
gy = np.fft.fftfreq(mesh[1], 1./mesh[1]).astype(np.int32)
gz = np.fft.fftfreq(mesh[2], 1./mesh[2]).astype(np.int32)
sub_vG = _take_5d(vG, (None, None, gx, gy, gz)).reshape(-1,ngrids)
v_rs = tools.ifft(sub_vG, mesh).reshape(nset,4,ngrids)
vR = np.asarray(v_rs.real, order='C')
vI = np.asarray(v_rs.imag, order='C')
if at_gamma_point:
v_rs = vR
mat = eval_mat(cell, vR, task_list, comp=comp, hermi=hermi, deriv=deriv,
xctype='GGA', kpts=kpts, grid_level=ilevel, mesh=mesh)
vj_kpts += np.asarray(mat).reshape(nset,-1,comp,nao,nao)
if not at_gamma_point and abs(vI).max() > IMAG_TOL:
raise NotImplementedError
if nset == 1:
vj_kpts = vj_kpts[0]
return vj_kpts
def _rks_gga_wv0(rho, vxc, weight):
vrho, vgamma = vxc[:2]
ngrid = vrho.size
wv = np.empty((4,ngrid))
wv[0] = np.multiply(weight, vrho, out=wv[0])
for i in range(1, 4):
wv[i] = np.multiply(weight * 2, np.multiply(vgamma, rho[i], out=wv[i]), out=wv[i])
return wv
def _uks_gga_wv0(rho, vxc, weight):
rhoa, rhob = rho
vrho, vsigma = vxc[:2]
ngrids = vrho.shape[0]
wv = np.empty((2, 4, ngrids))
wv[0,0] = np.multiply(weight, vrho[:,0], out=wv[0,0])
for i in range(1,4):
wv[0,i] = np.multiply(2., np.multiply(rhoa[i], vsigma[:,0], out=wv[0,i]), out=wv[0,i])
wv[0,i] = np.add(wv[0,i], np.multiply(rhob[i], vsigma[:,1]), out=wv[0,i])
wv[0,i] = np.multiply(weight, wv[0,i], out=wv[0,i])
wv[1,0] = np.multiply(weight, vrho[:,1], out=wv[1,0])
for i in range(1,4):
wv[1,i] = np.multiply(2., np.multiply(rhob[i], vsigma[:,2], out=wv[1,i]), out=wv[1,i])
wv[1,i] = np.add(wv[1,i], np.multiply(rhoa[i], vsigma[:,1]), out=wv[1,i])
wv[1,i] = np.multiply(weight, wv[1,i], out=wv[1,i])
return wv
def _rks_gga_wv0_pw(cell, rho, vxc, weight, mesh):
vrho, vgamma = vxc[:2]
ngrid = vrho.size
buf = np.empty((3,ngrid))
for i in range(1, 4):
buf[i-1] = np.multiply(vgamma, rho[i], out=buf[i-1])
vrho_freq = tools.fft(vrho, mesh).reshape((1,ngrid))
buf_freq = tools.fft(buf, mesh).reshape((3,ngrid))
Gv = cell.get_Gv(mesh)
#out = vrho_freq - 2j * np.einsum('px,xp->p', Gv, buf_freq)
#out *= weight
out = np.empty((ngrid,), order="C", dtype=np.complex128)
func = getattr(libdft, 'get_gga_vrho_gs', None)
func(out.ctypes.data_as(ctypes.c_void_p),
vrho_freq.ctypes.data_as(ctypes.c_void_p),
buf_freq.ctypes.data_as(ctypes.c_void_p),
Gv.ctypes.data_as(ctypes.c_void_p),
ctypes.c_double(weight), ctypes.c_int(ngrid))
return out
def _uks_gga_wv0_pw(cell, rho, vxc, weight, mesh):
rhoa, rhob = rho
vrho, vgamma = vxc[:2]
ngrid = vrho.shape[0]
buf = np.empty((2,3,ngrid))
for i in range(1, 4):
buf[0,i-1] = np.multiply(vgamma[:,0], rhoa[i], out=buf[0,i-1])
tmp = np.multiply(vgamma[:,1], rhob[i])
tmp = np.multiply(.5, tmp, out=tmp)
buf[0,i-1] = np.add(buf[0,i-1], tmp, out=buf[0,i-1])
buf[1,i-1] = np.multiply(vgamma[:,2], rhob[i], out=buf[1,i-1])
tmp = np.multiply(vgamma[:,1], rhoa[i])
tmp = np.multiply(.5, tmp, out=tmp)
buf[1,i-1] = np.add(buf[1,i-1], tmp, out=buf[1,i-1])
vrho_freq = tools.fft(vrho.T, mesh).reshape((2,ngrid))
buf_freq = tools.fft(buf.reshape(-1,ngrid), mesh).reshape((2,3,ngrid))
Gv = cell.get_Gv(mesh)
#out = vrho_freq - 2j * np.einsum('px,xp->p', Gv, buf_freq)
#out *= weight
out = np.empty((2,ngrid), order="C", dtype=np.complex128)
func = getattr(libdft, 'get_gga_vrho_gs')
for s in range(2):
func(out[s].ctypes.data_as(ctypes.c_void_p),
vrho_freq[s].ctypes.data_as(ctypes.c_void_p),
buf_freq[s].ctypes.data_as(ctypes.c_void_p),
Gv.ctypes.data_as(ctypes.c_void_p),
ctypes.c_double(weight), ctypes.c_int(ngrid))
return out
[docs]
def nr_rks(mydf, xc_code, dm_kpts, hermi=1, kpts=None,
kpts_band=None, with_j=False, return_j=False, verbose=None):
'''
Same as multigrid.nr_rks, but considers Hermitian symmetry also for GGA
'''
if kpts is None: kpts = mydf.kpts
log = logger.new_logger(mydf, verbose)
cell = mydf.cell
dm_kpts = np.asarray(dm_kpts, order='C')
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
kpts_band, input_band = _format_kpts_band(kpts_band, kpts), kpts_band
ni = mydf._numint
xctype = ni._xc_type(xc_code)
if xctype == 'LDA':
deriv = 0
elif xctype == 'GGA':
deriv = 1
rhoG = _eval_rhoG(mydf, dm_kpts, hermi, kpts, deriv)
mesh = mydf.mesh
ngrids = np.prod(mesh)
coulG = tools.get_coulG(cell, mesh=mesh)
#vG = np.einsum('ng,g->ng', rhoG[:,0], coulG)
vG = np.empty_like(rhoG[:,0], dtype=np.result_type(rhoG[:,0], coulG))
for i, rhoG_i in enumerate(rhoG[:,0]):
vG[i] = np.multiply(rhoG_i, coulG, out=vG[i])
coulG = None
if mydf.vpplocG_part1 is not None:
for i in range(nset):
#vG[i] += mydf.vpplocG_part1 * 2
vG[i] = np.add(vG[i], np.multiply(2., mydf.vpplocG_part1), out=vG[i])
#ecoul = .5 * np.einsum('ng,ng->n', rhoG[:,0].real, vG.real)
#ecoul+= .5 * np.einsum('ng,ng->n', rhoG[:,0].imag, vG.imag)
ecoul = np.zeros((rhoG.shape[0],))
for i in range(rhoG.shape[0]):
ecoul[i] = .5 * np.vdot(rhoG[i,0], vG[i]).real
ecoul /= cell.vol
log.debug('Multigrid Coulomb energy %s', ecoul)
if mydf.vpplocG_part1 is not None:
for i in range(nset):
#vG[i] -= mydf.vpplocG_part1
vG[i] = np.subtract(vG[i], mydf.vpplocG_part1, out=vG[i])
weight = cell.vol / ngrids
# *(1./weight) because rhoR is scaled by weight in _eval_rhoG. When
# computing rhoR with IFFT, the weight factor is not needed.
rhoR = tools.ifft(rhoG.reshape(-1,ngrids), mesh).real * (1./weight)
rhoR = rhoR.reshape(nset,-1,ngrids)
wv_freq = []
nelec = np.zeros(nset)
excsum = np.zeros(nset)
for i in range(nset):
exc, vxc = ni.eval_xc(xc_code, rhoR[i], spin=0, deriv=1)[:2]
if xctype == 'LDA':
wv = np.multiply(weight, vxc[0])
wv_freq.append(tools.fft(wv, mesh))
wv = None
elif xctype == 'GGA':
if GGA_METHOD.upper() == 'FFT':
wv_freq.append(_rks_gga_wv0_pw(cell, rhoR[i], vxc, weight, mesh).reshape(1,ngrids))
else:
wv = _rks_gga_wv0(rhoR[i], vxc, weight)
wv_freq.append(tools.fft(wv, mesh))
wv = None
else:
raise NotImplementedError
nelec[i] += np.sum(rhoR[i,0]) * weight
excsum[i] += np.sum(np.multiply(rhoR[i,0], exc)) * weight
exc = vxc = None
rhoR = rhoG = None
if len(wv_freq) == 1:
wv_freq = wv_freq[0].reshape(nset,-1,*mesh)
else:
wv_freq = np.asarray(wv_freq).reshape(nset,-1,*mesh)
if nset == 1:
ecoul = ecoul[0]
nelec = nelec[0]
excsum = excsum[0]
log.debug('Multigrid exc %s nelec %s', excsum, nelec)
kpts_band, input_band = _format_kpts_band(kpts_band, kpts), kpts_band
if xctype == 'LDA':
if with_j:
wv_freq[:,0] += vG.reshape(nset,*mesh)
veff = _get_j_pass2(mydf, wv_freq, kpts_band, verbose=log)
elif xctype == 'GGA':
if with_j:
#wv_freq[:,0] += vG.reshape(nset,*mesh)
wv_freq[:,0] = np.add(wv_freq[:,0], vG.reshape(nset,*mesh), out=wv_freq[:,0])
if GGA_METHOD.upper() == 'FFT':
veff = _get_j_pass2(mydf, wv_freq, kpts_band, verbose=log)
else:
veff = _get_gga_pass2(mydf, wv_freq, kpts_band, hermi=hermi, verbose=log)
wv_freq = None
veff = _format_jks(veff, dm_kpts, input_band, kpts)
if return_j:
vj = _get_j_pass2(mydf, vG, kpts_band, verbose=log)
vj = _format_jks(veff, dm_kpts, input_band, kpts)
else:
vj = None
vG = None
veff = lib.tag_array(veff, ecoul=ecoul, exc=excsum, vj=vj, vk=None)
return nelec, excsum, veff
[docs]
def nr_uks(mydf, xc_code, dm_kpts, hermi=1, kpts=None,
kpts_band=None, with_j=False, return_j=False, verbose=None):
if kpts is None: kpts = mydf.kpts
log = logger.new_logger(mydf, verbose)
cell = mydf.cell
dm_kpts = np.asarray(dm_kpts, order='C')
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
nset //= 2
kpts_band, input_band = _format_kpts_band(kpts_band, kpts), kpts_band
mesh = mydf.mesh
ngrids = np.prod(mesh)
ni = mydf._numint
xctype = ni._xc_type(xc_code)
if xctype == 'LDA':
deriv = 0
elif xctype == 'GGA':
deriv = 1
rhoG = _eval_rhoG(mydf, dm_kpts, hermi, kpts, deriv)
rhoG = rhoG.reshape(nset,2,-1,ngrids)
coulG = tools.get_coulG(cell, mesh=mesh)
#vG = np.einsum('nsg,g->ng', rhoG[:,:,0], coulG)
vG = np.empty((nset,ngrids), dtype=np.result_type(rhoG[:,:,0], coulG))
for i, rhoG_i in enumerate(rhoG[:,:,0]):
vG[i] = np.multiply(np.add(rhoG_i[0], rhoG_i[1]), coulG, out=vG[i])
coulG = None
if mydf.vpplocG_part1 is not None:
for i in range(nset):
#vG[i] += mydf.vpplocG_part1 * 2
vG[i] = np.add(vG[i], np.multiply(2., mydf.vpplocG_part1), out=vG[i])
ecoul = np.zeros(nset)
for i in range(nset):
ecoul[i] = .5 * np.vdot(np.add(rhoG[i,0,0], rhoG[i,1,0]), vG[i]).real
ecoul /= cell.vol
log.debug('Multigrid Coulomb energy %s', ecoul)
if mydf.vpplocG_part1 is not None:
for i in range(nset):
#vG[i] -= mydf.vpplocG_part1
vG[i] = np.subtract(vG[i], mydf.vpplocG_part1, out=vG[i])
weight = cell.vol / ngrids
# *(1./weight) because rhoR is scaled by weight in _eval_rhoG. When
# computing rhoR with IFFT, the weight factor is not needed.
rhoR = tools.ifft(rhoG.reshape(-1,ngrids), mesh).real * (1./weight)
rhoR = rhoR.reshape(nset,2,-1,ngrids)
wv_freq = []
nelec = np.zeros(nset)
excsum = np.zeros(nset)
for i in range(nset):
exc, vxc = ni.eval_xc(xc_code, rhoR[i], spin=1, deriv=1)[:2]
if xctype == 'LDA':
wv = np.multiply(weight, vxc[0].T)
wv_freq.append(tools.fft(wv, mesh))
wv = None
elif xctype == 'GGA':
if GGA_METHOD.upper() == 'FFT':
wv_freq.append(_uks_gga_wv0_pw(cell, rhoR[i], vxc, weight, mesh))
else:
wv = _uks_gga_wv0(rhoR[i], vxc, weight)
wv_freq.append(tools.fft(wv.reshape(-1,*mesh), mesh))
wv = None
else:
raise NotImplementedError
nelec[i] += np.sum(rhoR[i,:,0]).sum() * weight
excsum[i] += np.sum(np.multiply(np.add(rhoR[i,0,0],rhoR[i,1,0]), exc)) * weight
exc = vxc = None
rhoR = rhoG = None
if len(wv_freq) == 1:
wv_freq = wv_freq[0].reshape(nset,2,-1,*mesh)
else:
wv_freq = np.asarray(wv_freq).reshape(nset,2,-1,*mesh)
if nset == 1:
ecoul = ecoul[0]
nelec = nelec[0]
excsum = excsum[0]
log.debug('Multigrid exc %s nelec %s', excsum, nelec)
kpts_band, input_band = _format_kpts_band(kpts_band, kpts), kpts_band
if xctype == 'LDA':
if with_j:
for s in range(2):
wv_freq[:,s,0] += vG.reshape(nset,*mesh)
veff = _get_j_pass2(mydf, wv_freq, kpts_band, verbose=log)
elif xctype == 'GGA':
if with_j:
#wv_freq[:,:,0] += vG.reshape(nset,*mesh)
for s in range(2):
wv_freq[:,s,0] = np.add(wv_freq[:,s,0], vG.reshape(nset,*mesh), out=wv_freq[:,s,0])
if GGA_METHOD.upper() == 'FFT':
veff = _get_j_pass2(mydf, wv_freq, kpts_band, verbose=log)
else:
veff = _get_gga_pass2(mydf, wv_freq, kpts_band, hermi=hermi, verbose=log)
wv_freq = None
veff = _format_jks(veff, dm_kpts, input_band, kpts)
if return_j:
vj = _get_j_pass2(mydf, vG, kpts_band, verbose=log)
vj = _format_jks(veff, dm_kpts, input_band, kpts)
else:
vj = None
vG = None
veff = lib.tag_array(veff, ecoul=ecoul, exc=excsum, vj=vj, vk=None)
return nelec, excsum, veff
[docs]
def get_veff_ip1(mydf, dm_kpts, xc_code=None, kpts=np.zeros((1,3)), kpts_band=None, spin=0):
cell = mydf.cell
dm_kpts = np.asarray(dm_kpts, order='C')
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
kpts_band = _format_kpts_band(kpts_band, kpts)
if spin == 1:
nset //= 2
mesh = mydf.mesh
ngrids = np.prod(mesh)
ni = mydf._numint
xctype = ni._xc_type(xc_code)
if xctype == 'LDA':
deriv = 0
elif xctype == 'GGA':
deriv = 1
rhoG = _eval_rhoG(mydf, dm_kpts, hermi=1, kpts=kpts_band, deriv=deriv)
if spin == 1:
rhoG = rhoG.reshape(nset,2,-1,ngrids)
# cache rhoG for core density gradients
mydf.rhoG = rhoG
coulG = tools.get_coulG(cell, mesh=mesh)
vG = np.empty((nset,ngrids), dtype=np.result_type(rhoG, coulG))
for i in range(nset):
if spin == 0:
vG[i] = np.multiply(rhoG[i,0], coulG, out=vG[i])
elif spin == 1:
tmp = np.add(rhoG[i,0,0], rhoG[i,1,0])
vG[i] = np.multiply(tmp, coulG, out=vG[i])
if mydf.vpplocG_part1 is not None:
for i in range(nset):
vG[i] = np.add(vG[i], mydf.vpplocG_part1, out=vG[i])
weight = cell.vol / ngrids
# *(1./weight) because rhoR is scaled by weight in _eval_rhoG. When
# computing rhoR with IFFT, the weight factor is not needed.
rhoR = tools.ifft(rhoG.reshape(-1,ngrids), mesh).real * (1./weight)
if spin == 0:
rhoR = rhoR.reshape(nset,-1,ngrids)
elif spin == 1:
rhoR = rhoR.reshape(nset,2,-1,ngrids)
wv_freq = []
for i in range(nset):
exc, vxc = ni.eval_xc(xc_code, rhoR[i], spin=spin, deriv=1)[:2]
if spin == 0:
if xctype == 'LDA':
wv = np.multiply(weight, vxc[0])
wv_freq.append(tools.fft(wv, mesh))
wv = None
elif xctype == 'GGA':
if GGA_METHOD.upper() == 'FFT':
wv_freq.append(_rks_gga_wv0_pw(cell, rhoR[i], vxc, weight, mesh).reshape(1,ngrids))
else:
wv = _rks_gga_wv0(rhoR[i], vxc, weight)
wv_freq.append(tools.fft(wv, mesh))
else:
raise NotImplementedError
elif spin == 1:
if xctype == 'LDA':
wv = np.multiply(weight, vxc[0].T)
wv_freq.append(tools.fft(wv, mesh))
wv = None
elif xctype == 'GGA':
if GGA_METHOD.upper() == 'FFT':
wv_freq.append(_uks_gga_wv0_pw(cell, rhoR[i], vxc, weight, mesh))
else:
wv = _uks_gga_wv0(rhoR[i], vxc, weight)
wv_freq.append(tools.fft(wv.reshape(-1,*mesh), mesh))
wv = None
else:
raise NotImplementedError
rhoR = rhoG = None
if spin == 0:
if len(wv_freq) == 1:
wv_freq = wv_freq[0].reshape(nset,-1,*mesh)
else:
wv_freq = np.asarray(wv_freq).reshape(nset,-1,*mesh)
elif spin == 1:
if len(wv_freq) == 1:
wv_freq = wv_freq[0].reshape(nset,2,-1,*mesh)
else:
wv_freq = np.asarray(wv_freq).reshape(nset,2,-1,*mesh)
for i in range(nset):
if spin == 0:
wv_freq[i,0] = np.add(wv_freq[i,0], vG[i].reshape(*mesh), out=wv_freq[i,0])
elif spin == 1:
for s in range(2):
wv_freq[i,s,0] = np.add(wv_freq[i,s,0], vG[i].reshape(*mesh), out=wv_freq[i,s,0])
if xctype == 'LDA':
vj_kpts = _get_j_pass2_ip1(mydf, wv_freq, kpts_band, hermi=0, deriv=1)
elif xctype == 'GGA':
if GGA_METHOD.upper() == 'FFT':
vj_kpts = _get_j_pass2_ip1(mydf, wv_freq, kpts_band, hermi=0, deriv=1)
else:
vj_kpts = _get_gga_pass2_ip1(mydf, wv_freq, kpts_band, hermi=0, deriv=1)
else:
raise NotImplementedError
comp = 3
nao = cell.nao
if spin == 0:
vj_kpts = vj_kpts.reshape(nset,nkpts,comp,nao,nao)
elif spin == 1:
vj_kpts = vj_kpts.reshape(nset,2,nkpts,comp,nao,nao)
vj_kpts = np.moveaxis(vj_kpts, -3, -4)
if nkpts == 1:
vj_kpts = vj_kpts[...,0,:,:]
if nset == 1:
vj_kpts = vj_kpts[0]
return vj_kpts
[docs]
class MultiGridFFTDF2(MultiGridFFTDF):
'''
Base class for multigrid DFT (version 2).
Attributes:
task_list : TaskList instance
Task list recording which primitive basis function pairs
need to be considered.
vpplocG_part1 : arrary
Short-range part of the local pseudopotential represented
in the reciprocal space. It is cached to reduce cost.
rhoG : array
Electronic density represented in the reciprocal space.
It is cached in nuclear gradient calculations to reduce cost.
'''
ngrids = getattr(__config__, 'pbc_dft_multigrid_ngrids', 4)
ke_ratio = getattr(__config__, 'pbc_dft_multigrid_ke_ratio', 3.0)
rel_cutoff = getattr(__config__, 'pbc_dft_multigrid_rel_cutoff', 20.0)
_keys = {'ngrids', 'ke_ratio', 'rel_cutoff',
'task_list', 'vpplocG_part1', 'rhoG'}
def __init__(self, cell, kpts=np.zeros((1,3))):
fft.FFTDF.__init__(self, cell, kpts)
self.task_list = None
self.vpplocG_part1 = None
self.rhoG = None
if not gamma_point(kpts):
raise NotImplementedError('MultiGridFFTDF2 only supports Gamma-point calculations.')
a = cell.lattice_vectors()
if abs(a-np.diag(a.diagonal())).max() > 1e-12:
raise NotImplementedError('MultiGridFFTDF2 only supports orthorhombic lattices.')
[docs]
def reset(self, cell=None):
self.vpplocG_part1 = None
self.rhoG = None
if self.task_list is not None:
free_task_list(self.task_list)
self.task_list = None
fft.FFTDF.reset(self, cell=cell)
def __del__(self):
self.reset()
[docs]
def get_veff_ip1(self, dm, xc_code=None, kpts=None, kpts_band=None, spin=0):
if kpts is None:
if self.kpts is None:
kpts = np.zeros(1,3)
else:
kpts = self.kpts
kpts = kpts.reshape(-1,3)
vj = get_veff_ip1(self, dm, xc_code=xc_code,
kpts=kpts, kpts_band=kpts_band, spin=spin)
return vj
[docs]
def get_pp(self, kpts=None):
'''Compute the GTH pseudopotential matrix, which includes
the second part of the local potential and the non-local potential.
The first part of the local potential is cached as `vpplocG_part1`,
which is the reciprocal space representation, to be added to the electron
density for computing the Coulomb matrix.
In order to get the full PP matrix, the potential due to `vpplocG_part1`
needs to be added.
'''
self.vpplocG_part1 = _get_vpplocG_part1(self, with_rho_core=True)
return _get_pp_without_erf(self, kpts)
vpploc_part1_nuc_grad = vpploc_part1_nuc_grad
