#!/usr/bin/env python
# Copyright 2014-2024 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: Qiming Sun <osirpt.sun@gmail.com>
#
'''Multigrid to compute DFT integrals'''
import ctypes
import numpy
import scipy.linalg
from pyscf import __config__
from pyscf import lib
from pyscf.lib import logger
from pyscf.gto import ATOM_OF, ANG_OF, NPRIM_OF, PTR_EXP, PTR_COEFF
from pyscf.dft.numint import libdft, BLKSIZE, MGGA_DENSITY_LAPL
from pyscf.pbc import tools
from pyscf.pbc import gto
from pyscf.pbc.gto import pseudo
from pyscf.pbc.gto.pseudo import pp_int
from pyscf.pbc.dft import numint, gen_grid
from pyscf.pbc.df.df_jk import (
_format_dms,
_format_kpts_band,
_format_jks,
)
from pyscf.pbc.lib.kpts_helper import gamma_point
from pyscf.pbc.df import fft, ft_ao
from pyscf.pbc.dft.multigrid.utils import (
_take_4d,
_take_5d,
_takebak_4d,
_takebak_5d,
)
#sys.stderr.write('WARN: multigrid is an experimental feature. It is still in '
# 'testing\nFeatures and APIs may be changed in the future.\n')
EXTRA_PREC = getattr(__config__, 'pbc_gto_eval_gto_extra_precision', 1e-2)
TO_EVEN_GRIDS = getattr(__config__, 'pbc_dft_multigrid_to_even', False)
RMAX_FACTOR_ORTH = getattr(__config__, 'pbc_dft_multigrid_rmax_factor_orth', 1.1)
RMAX_FACTOR_NONORTH = getattr(__config__, 'pbc_dft_multigrid_rmax_factor_nonorth', 0.5)
RMAX_RATIO = getattr(__config__, 'pbc_dft_multigrid_rmax_ratio', 0.7)
R_RATIO_SUBLOOP = getattr(__config__, 'pbc_dft_multigrid_r_ratio_subloop', 0.6)
INIT_MESH_ORTH = getattr(__config__, 'pbc_dft_multigrid_init_mesh_orth', (12,12,12))
INIT_MESH_NONORTH = getattr(__config__, 'pbc_dft_multigrid_init_mesh_nonorth', (32,32,32))
KE_RATIO = getattr(__config__, 'pbc_dft_multigrid_ke_ratio', 1.3)
TASKS_TYPE = getattr(__config__, 'pbc_dft_multigrid_tasks_type', 'ke_cut') # 'rcut'
# RHOG_HIGH_ORDER=True will compute the high order derivatives of electron
# density in real space and FT to reciprocal space. Set RHOG_HIGH_ORDER=False
# to approximate the density derivatives in reciprocal space (without
# evaluating the high order derivatives in real space).
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
[docs]
def eval_mat(cell, weights, shls_slice=None, comp=1, hermi=0,
xctype='LDA', kpts=None, mesh=None, offset=None, submesh=None):
assert (all(cell._bas[:,NPRIM_OF] == 1))
if mesh is None:
mesh = cell.mesh
vol = cell.vol
weight_penalty = numpy.prod(mesh) / vol
exp_min = numpy.hstack(cell.bas_exps()).min()
theta_ij = exp_min / 2
lattice_sum_fac = max(2*numpy.pi*cell.rcut/(vol*theta_ij), 1)
precision = cell.precision / weight_penalty / lattice_sum_fac
if xctype != 'LDA':
precision *= .1
atm, bas, env = gto.conc_env(cell._atm, cell._bas, cell._env,
cell._atm, cell._bas, cell._env)
env[PTR_EXPDROP] = min(precision*EXTRA_PREC, EXPDROP)
ao_loc = gto.moleintor.make_loc(bas, 'cart')
if shls_slice is None:
shls_slice = (0, cell.nbas, 0, cell.nbas)
i0, i1, j0, j1 = shls_slice
j0 += cell.nbas
j1 += cell.nbas
naoi = ao_loc[i1] - ao_loc[i0]
naoj = ao_loc[j1] - ao_loc[j0]
Ls = gto.eval_gto.get_lattice_Ls(cell)
nimgs = len(Ls)
weights = numpy.asarray(weights, order='C')
assert (weights.dtype == numpy.double)
xctype = xctype.upper()
n_mat = None
if xctype == 'LDA':
if weights.ndim == 1:
weights = weights.reshape(-1, numpy.prod(mesh))
else:
n_mat = weights.shape[0]
elif xctype == 'GGA':
if hermi == 1:
raise RuntimeError('hermi=1 is not supported for GGA functional')
if weights.ndim == 2:
weights = weights.reshape(-1, 4, numpy.prod(mesh))
else:
n_mat = weights.shape[0]
else:
raise NotImplementedError
a = cell.lattice_vectors()
b = numpy.linalg.inv(a.T)
if offset is None:
offset = (0, 0, 0)
if submesh is None:
submesh = mesh
# log_prec is used to estimate the gto_rcut. Add EXTRA_PREC to count
# other possible factors and coefficients in the integral.
log_prec = numpy.log(precision * EXTRA_PREC)
if abs(a-numpy.diag(a.diagonal())).max() < 1e-12:
lattice_type = '_orth'
else:
lattice_type = '_nonorth'
eval_fn = 'NUMINTeval_' + xctype.lower() + lattice_type
drv = libdft.NUMINT_fill2c
def make_mat(weights):
mat = numpy.zeros((nimgs,comp,naoj,naoi))
drv(getattr(libdft, eval_fn),
weights.ctypes.data_as(ctypes.c_void_p),
mat.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(comp), ctypes.c_int(hermi),
(ctypes.c_int*4)(i0, i1, j0, j1),
ao_loc.ctypes.data_as(ctypes.c_void_p),
ctypes.c_double(log_prec),
ctypes.c_int(cell.dimension),
ctypes.c_int(nimgs),
Ls.ctypes.data_as(ctypes.c_void_p),
a.ctypes.data_as(ctypes.c_void_p),
b.ctypes.data_as(ctypes.c_void_p),
(ctypes.c_int*3)(*offset), (ctypes.c_int*3)(*submesh),
(ctypes.c_int*3)(*mesh),
atm.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(len(atm)),
bas.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(len(bas)),
env.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(len(env)))
return mat
out = []
for wv in weights:
if cell.dimension == 0:
mat = make_mat(wv)[0].transpose(0,2,1)
if hermi == 1:
for i in range(comp):
lib.hermi_triu(mat[i], inplace=True)
if comp == 1:
mat = mat[0]
elif kpts is None or gamma_point(kpts):
mat = make_mat(wv).sum(axis=0).transpose(0,2,1)
if hermi == 1:
for i in range(comp):
lib.hermi_triu(mat[i], inplace=True)
if comp == 1:
mat = mat[0]
if getattr(kpts, 'ndim', None) == 2:
mat = mat[None,:]
else:
mat = make_mat(wv)
expkL = numpy.exp(1j*kpts.reshape(-1,3).dot(Ls.T))
mat = lib.einsum('kr,rcij->kcij', expkL, mat)
if hermi == 1:
for i in range(comp):
for k in range(len(kpts)):
lib.hermi_triu(mat[k,i], inplace=True)
mat = mat.transpose(0,1,3,2)
if comp == 1:
mat = mat[:,0]
out.append(mat)
if n_mat is None:
out = out[0]
return out
[docs]
def eval_rho(cell, dm, shls_slice=None, hermi=0, xctype='LDA', kpts=None,
mesh=None, offset=None, submesh=None, ignore_imag=False,
out=None):
'''Collocate the *real* density (opt. gradients) on the real-space grid.
Kwargs:
ignore_image :
The output density is assumed to be real if ignore_imag=True.
'''
assert (all(cell._bas[:,NPRIM_OF] == 1))
if mesh is None:
mesh = cell.mesh
vol = cell.vol
weight_penalty = numpy.prod(mesh) / vol
exp_min = numpy.hstack(cell.bas_exps()).min()
theta_ij = exp_min / 2
lattice_sum_fac = max(2*numpy.pi*cell.rcut/(vol*theta_ij), 1)
precision = cell.precision / weight_penalty / lattice_sum_fac
if xctype != 'LDA':
precision *= .1
atm, bas, env = gto.conc_env(cell._atm, cell._bas, cell._env,
cell._atm, cell._bas, cell._env)
env[PTR_EXPDROP] = min(precision*EXTRA_PREC, EXPDROP)
ao_loc = gto.moleintor.make_loc(bas, 'cart')
if shls_slice is None:
shls_slice = (0, cell.nbas, 0, cell.nbas)
i0, i1, j0, j1 = shls_slice
if hermi == 1:
assert (i0 == j0 and i1 == j1)
j0 += cell.nbas
j1 += cell.nbas
naoi = ao_loc[i1] - ao_loc[i0]
naoj = ao_loc[j1] - ao_loc[j0]
dm = numpy.asarray(dm, order='C')
assert (dm.shape[-2:] == (naoi, naoj))
Ls = gto.eval_gto.get_lattice_Ls(cell)
if cell.dimension == 0 or kpts is None or gamma_point(kpts):
nkpts, nimgs = 1, Ls.shape[0]
dm = dm.reshape(-1,1,naoi,naoj).transpose(0,1,3,2)
else:
expkL = numpy.exp(1j*kpts.reshape(-1,3).dot(Ls.T))
nkpts, nimgs = expkL.shape
dm = dm.reshape(-1,nkpts,naoi,naoj).transpose(0,1,3,2)
n_dm = dm.shape[0]
a = cell.lattice_vectors()
b = numpy.linalg.inv(a.T)
if offset is None:
offset = (0, 0, 0)
if submesh is None:
submesh = mesh
log_prec = numpy.log(precision * EXTRA_PREC)
if abs(a-numpy.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')
if comp == 1:
shape = (numpy.prod(submesh),)
else:
shape = (comp, numpy.prod(submesh))
eval_fn = 'NUMINTrho_' + xctype.lower() + lattice_type
drv = libdft.NUMINT_rho_drv
def make_rho_(rho, dm, hermi):
drv(getattr(libdft, eval_fn),
rho.ctypes.data_as(ctypes.c_void_p),
dm.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(comp), ctypes.c_int(hermi),
(ctypes.c_int*4)(i0, i1, j0, j1),
ao_loc.ctypes.data_as(ctypes.c_void_p),
ctypes.c_double(log_prec),
ctypes.c_int(cell.dimension),
ctypes.c_int(nimgs),
Ls.ctypes.data_as(ctypes.c_void_p),
a.ctypes.data_as(ctypes.c_void_p),
b.ctypes.data_as(ctypes.c_void_p),
(ctypes.c_int*3)(*offset), (ctypes.c_int*3)(*submesh),
(ctypes.c_int*3)(*mesh),
atm.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(len(atm)),
bas.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(len(bas)),
env.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(len(env)))
return rho
rho = []
for i, dm_i in enumerate(dm):
if cell.dimension == 0:
if ignore_imag:
# basis are real. dm.imag can be dropped if ignore_imag
dm_i = dm_i.real
has_imag = dm_i.dtype == numpy.complex128
if has_imag:
dmR = numpy.asarray(dm_i.real, order='C')
dmI = numpy.asarray(dm_i.imag, order='C')
else:
# make a copy because the dm may be overwritten in the
# NUMINT_rho_drv inplace
dmR = numpy.array(dm_i, order='C', copy=True)
elif kpts is None or gamma_point(kpts):
if ignore_imag:
# basis are real. dm.imag can be dropped if ignore_imag
dm_i = dm_i.real
has_imag = dm_i.dtype == numpy.complex128
if has_imag:
dmR = numpy.repeat(dm_i.real, nimgs, axis=0)
dmI = numpy.repeat(dm_i.imag, nimgs, axis=0)
else:
dmR = numpy.repeat(dm_i, nimgs, axis=0)
else:
dm_L = lib.dot(expkL.T, dm_i.reshape(nkpts,-1)).reshape(nimgs,naoj,naoi)
dmR = numpy.asarray(dm_L.real, order='C')
if ignore_imag:
has_imag = False
else:
dmI = numpy.asarray(dm_L.imag, order='C')
has_imag = (hermi == 0 and abs(dmI).max() > 1e-8)
if (has_imag and xctype == 'LDA' and
naoi == naoj and
# For hermitian density matrices, the anti-symmetry
# character of the imaginary part of the density matrices
# can be found by rearranging the repeated images.
abs(dm_i - dm_i.conj().transpose(0,2,1)).max() < 1e-8):
has_imag = False
dm_L = None
if has_imag:
# complex density cannot be updated inplace directly by
# function NUMINT_rho_drv
if out is None:
rho_i = numpy.empty(shape, numpy.complex128)
rho_i.real = make_rho_(numpy.zeros(shape), dmR, 0)
rho_i.imag = make_rho_(numpy.zeros(shape), dmI, 0)
else:
assert out[i].dtype == numpy.complex128
rho_i = out[i].reshape(shape)
rho_i.real += make_rho_(numpy.zeros(shape), dmR, 0)
rho_i.imag += make_rho_(numpy.zeros(shape), dmI, 0)
else:
if out is None:
# rho_i needs to be initialized to 0 because rho_i is updated
# inplace in function NUMINT_rho_drv
rho_i = make_rho_(numpy.zeros(shape), dmR, hermi)
else:
assert out[i].dtype == numpy.double
rho_i = out[i].reshape(shape)
make_rho_(rho_i, dmR, hermi)
dmR = dmI = None
rho.append(rho_i)
if n_dm == 1:
rho = rho[0]
return rho
[docs]
def get_nuc(mydf, kpts=None):
kpts, is_single_kpt = fft._check_kpts(mydf, kpts)
cell = mydf.cell
mesh = mydf.mesh
charge = -cell.atom_charges()
Gv = cell.get_Gv(mesh)
SI = cell.get_SI(Gv)
rhoG = numpy.dot(charge, SI)
coulG = tools.get_coulG(cell, mesh=mesh, Gv=Gv)
vneG = rhoG * coulG
hermi = 1
vne = _get_j_pass2(mydf, vneG, hermi, kpts)[0]
if is_single_kpt:
vne = vne[0]
return numpy.asarray(vne)
[docs]
def get_pp(mydf, kpts=None, max_memory=4000):
'''Get the periodic pseudopotential nuc-el AO matrix, with G=0 removed.
'''
from pyscf import gto
kpts, is_single_kpt = fft._check_kpts(mydf, kpts)
cell = mydf.cell
mesh = mydf.mesh
Gv = cell.get_Gv(mesh)
ngrids = len(Gv)
vpplocG = numpy.empty((ngrids,), dtype=numpy.complex128)
mem_avail = max(max_memory, mydf.max_memory-lib.current_memory()[0])
blksize = int(mem_avail*1e6/((cell.natm*2)*16))
blksize = min(ngrids, max(21**3, blksize))
for ig0, ig1 in lib.prange(0, ngrids, blksize):
vpplocG_batch = pp_int.get_gth_vlocG_part1(cell, Gv[ig0:ig1])
SI = cell.get_SI(Gv[ig0:ig1])
vpplocG[ig0:ig1] = -numpy.einsum('ij,ij->j', SI, vpplocG_batch)
hermi = 1
vpp = _get_j_pass2(mydf, vpplocG, hermi, kpts)[0]
vpp2 = pp_int.get_pp_loc_part2(cell, kpts)
for k, kpt in enumerate(kpts):
vpp[k] += vpp2[k]
# vppnonloc evaluated in reciprocal space
fakemol = gto.Mole()
fakemol._atm = numpy.zeros((1,gto.ATM_SLOTS), dtype=numpy.int32)
fakemol._bas = numpy.zeros((1,gto.BAS_SLOTS), dtype=numpy.int32)
ptr = gto.PTR_ENV_START
fakemol._env = numpy.zeros(ptr+10)
fakemol._bas[0,gto.NPRIM_OF ] = 1
fakemol._bas[0,gto.NCTR_OF ] = 1
fakemol._bas[0,gto.PTR_EXP ] = ptr+3
fakemol._bas[0,gto.PTR_COEFF] = ptr+4
def vppnl_by_k(kpt):
SPG_lm_aoGs = []
for ia in range(cell.natm):
symb = cell.atom_symbol(ia)
if symb not in cell._pseudo:
SPG_lm_aoGs.append(None)
continue
pp = cell._pseudo[symb]
p1 = 0
for l, proj in enumerate(pp[5:]):
rl, nl, hl = proj
if nl > 0:
p1 = p1+nl*(l*2+1)
SPG_lm_aoGs.append(numpy.zeros((p1, cell.nao), dtype=numpy.complex128))
mem_avail = max(max_memory, mydf.max_memory-lib.current_memory()[0])
blksize = int(mem_avail*1e6/((48+cell.nao+13+3)*16))
blksize = min(ngrids, max(21**3, blksize))
vppnl = 0
for ig0, ig1 in lib.prange(0, ngrids, blksize):
ng = ig1 - ig0
# buf for SPG_lmi upto l=0..3 and nl=3
buf = numpy.empty((48,ng), dtype=numpy.complex128)
Gk = Gv[ig0:ig1] + kpt
G_rad = numpy.linalg.norm(Gk, axis=1)
aokG = ft_ao.ft_ao(cell, Gv[ig0:ig1], kpt=kpt) * (ngrids/cell.vol)
for ia in range(cell.natm):
symb = cell.atom_symbol(ia)
if symb not in cell._pseudo:
continue
pp = cell._pseudo[symb]
p1 = 0
for l, proj in enumerate(pp[5:]):
rl, nl, hl = proj
if nl > 0:
fakemol._bas[0,gto.ANG_OF] = l
fakemol._env[ptr+3] = .5*rl**2
fakemol._env[ptr+4] = rl**(l+1.5)*numpy.pi**1.25
pYlm_part = fakemol.eval_gto('GTOval', Gk)
p0, p1 = p1, p1+nl*(l*2+1)
# pYlm is real, SI[ia] is complex
pYlm = numpy.ndarray((nl,l*2+1,ng), dtype=numpy.complex128, buffer=buf[p0:p1])
for k in range(nl):
qkl = pseudo.pp._qli(G_rad*rl, l, k)
pYlm[k] = pYlm_part.T * qkl
#:SPG_lmi = numpy.einsum('g,nmg->nmg', SI[ia].conj(), pYlm)
#:SPG_lm_aoG = numpy.einsum('nmg,gp->nmp', SPG_lmi, aokG)
#:tmp = numpy.einsum('ij,jmp->imp', hl, SPG_lm_aoG)
#:vppnl += numpy.einsum('imp,imq->pq', SPG_lm_aoG.conj(), tmp)
if p1 > 0:
SPG_lmi = buf[:p1]
SPG_lmi *= cell.get_SI(Gv[ig0:ig1], atmlst=[ia,]).conj()
SPG_lm_aoGs[ia] += lib.zdot(SPG_lmi, aokG)
buf = None
for ia in range(cell.natm):
symb = cell.atom_symbol(ia)
if symb not in cell._pseudo:
continue
pp = cell._pseudo[symb]
p1 = 0
for l, proj in enumerate(pp[5:]):
rl, nl, hl = proj
if nl > 0:
p0, p1 = p1, p1+nl*(l*2+1)
hl = numpy.asarray(hl)
SPG_lm_aoG = SPG_lm_aoGs[ia][p0:p1].reshape(nl,l*2+1,-1)
tmp = numpy.einsum('ij,jmp->imp', hl, SPG_lm_aoG)
vppnl += numpy.einsum('imp,imq->pq', SPG_lm_aoG.conj(), tmp)
SPG_lm_aoGs=None
return vppnl * (1./ngrids**2)
for k, kpt in enumerate(kpts):
vppnl = vppnl_by_k(kpt)
if gamma_point(kpt):
vpp[k] = vpp[k].real + vppnl.real
else:
vpp[k] += vppnl
if is_single_kpt:
vpp = vpp[0]
return numpy.asarray(vpp)
[docs]
def get_j_kpts(mydf, dm_kpts, hermi=1, kpts=numpy.zeros((1,3)), kpts_band=None):
'''Get the Coulomb (J) AO matrix at sampled k-points.
Args:
dm_kpts : (nkpts, nao, nao) ndarray or a list of (nkpts,nao,nao) ndarray
Density matrix at each k-point. If a list of k-point DMs, eg,
UHF alpha and beta DM, the alpha and beta DMs are contracted
separately.
kpts : (nkpts, 3) ndarray
Kwargs:
kpts_band : (3,) ndarray or (*,3) ndarray
A list of arbitrary "band" k-points at which to evalute the matrix.
Returns:
vj : (nkpts, nao, nao) ndarray
or list of vj if the input dm_kpts is a list of DMs
'''
cell = mydf.cell
dm_kpts = numpy.asarray(dm_kpts)
rhoG = _eval_rhoG(mydf, dm_kpts, hermi, kpts, deriv=0)
coulG = tools.get_coulG(cell, mesh=cell.mesh)
#:vG = numpy.einsum('ng,g->ng', rhoG[:,0], coulG)
vG = rhoG[:,0]
vG *= coulG
kpts_band, input_band = _format_kpts_band(kpts_band, kpts), kpts_band
vj_kpts = _get_j_pass2(mydf, vG, hermi, kpts_band)
return _format_jks(vj_kpts, dm_kpts, input_band, kpts)
def _eval_rhoG(mydf, dm_kpts, hermi=1, kpts=numpy.zeros((1,3)), deriv=0,
rhog_high_order=RHOG_HIGH_ORDER):
log = logger.Logger(mydf.stdout, mydf.verbose)
cell = mydf.cell
dm_kpts = lib.asarray(dm_kpts, order='C')
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
tasks = getattr(mydf, 'tasks', None)
if tasks is None:
mydf.tasks = tasks = multi_grids_tasks(cell, mydf.mesh, log)
log.debug('Multigrid ntasks %s', len(tasks))
assert (deriv < 2)
#hermi = hermi and abs(dms - dms.transpose(0,1,3,2).conj()).max() < 1e-9
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
#if hermi != 1 and not gamma_point(kpts):
# raise NotImplementedError
elif deriv == 2: # meta-GGA
raise NotImplementedError
ignore_imag = (hermi == 1)
nx, ny, nz = mydf.mesh
rhoG = numpy.zeros((nset*rhodim,nx,ny,nz), dtype=numpy.complex128)
for grids_dense, grids_sparse in tasks:
h_cell = grids_dense.cell
mesh = tuple(grids_dense.mesh)
ngrids = numpy.prod(mesh)
log.debug('mesh %s rcut %g', mesh, h_cell.rcut)
if grids_sparse is None:
# The first pass handles all diffused functions using the regular
# matrix multiplication code.
rho = numpy.zeros((nset,rhodim,ngrids), dtype=numpy.complex128)
idx_h = grids_dense.ao_idx
dms_hh = numpy.asarray(dms[:,:,idx_h[:,None],idx_h], order='C')
for ao_h_etc, p0, p1 in mydf.aoR_loop(grids_dense, kpts, deriv):
ao_h, mask = ao_h_etc[0], ao_h_etc[2]
for k in range(nkpts):
for i in range(nset):
if xctype == 'LDA':
ao_dm = lib.dot(ao_h[k], dms_hh[i,k])
rho_sub = numpy.einsum('xi,xi->x', ao_dm, ao_h[k].conj())
else:
rho_sub = numint.eval_rho(h_cell, ao_h[k], dms_hh[i,k],
mask, xctype, hermi)
rho[i,:,p0:p1] += rho_sub
ao_h = ao_h_etc = ao_dm = None
if ignore_imag:
rho = rho.real
else:
idx_h = grids_dense.ao_idx
idx_l = grids_sparse.ao_idx
idx_t = numpy.append(idx_h, idx_l)
dms_ht = numpy.asarray(dms[:,:,idx_h[:,None],idx_t], order='C')
dms_lh = numpy.asarray(dms[:,:,idx_l[:,None],idx_h], order='C')
l_cell = grids_sparse.cell
h_pcell, h_coeff = h_cell.decontract_basis(to_cart=True, aggregate=True)
l_pcell, l_coeff = l_cell.decontract_basis(to_cart=True, aggregate=True)
t_cell = h_pcell + l_pcell
t_coeff = scipy.linalg.block_diag(h_coeff, l_coeff)
nshells_h = _pgto_shells(h_cell)
nshells_t = _pgto_shells(t_cell)
if deriv == 0:
if hermi == 1:
naol, naoh = dms_lh.shape[2:]
dms_ht[:,:,:,naoh:] += dms_lh.transpose(0,1,3,2)
pgto_dms = lib.einsum('nkij,pi,qj->nkpq', dms_ht, h_coeff, t_coeff)
shls_slice = (0, nshells_h, 0, nshells_t)
#:rho = eval_rho(t_cell, pgto_dms, shls_slice, 0, 'LDA', kpts,
#: offset=None, submesh=None, ignore_imag=True)
rho = _eval_rho_bra(t_cell, pgto_dms, shls_slice, 0,
'LDA', kpts, grids_dense, True, log)
else:
pgto_dms = lib.einsum('nkij,pi,qj->nkpq', dms_ht, h_coeff, t_coeff)
shls_slice = (0, nshells_h, 0, nshells_t)
#:rho = eval_rho(t_cell, pgto_dms, shls_slice, 0, 'LDA', kpts,
#: offset=None, submesh=None)
rho = _eval_rho_bra(t_cell, pgto_dms, shls_slice, 0,
'LDA', kpts, grids_dense, ignore_imag, log)
pgto_dms = lib.einsum('nkij,pi,qj->nkpq', dms_lh, l_coeff, h_coeff)
shls_slice = (nshells_h, nshells_t, 0, nshells_h)
#:rho += eval_rho(t_cell, pgto_dms, shls_slice, 0, 'LDA', kpts,
#: offset=None, submesh=None)
rho += _eval_rho_ket(t_cell, pgto_dms, shls_slice, 0,
'LDA', kpts, grids_dense, ignore_imag, log)
elif deriv == 1:
pgto_dms = lib.einsum('nkij,pi,qj->nkpq', dms_ht, h_coeff, t_coeff)
shls_slice = (0, nshells_h, 0, nshells_t)
#:rho = eval_rho(t_cell, pgto_dms, shls_slice, 0, 'GGA', kpts,
#: ignore_imag=ignore_imag)
rho = _eval_rho_bra(t_cell, pgto_dms, shls_slice, 0, 'GGA',
kpts, grids_dense, ignore_imag, log)
pgto_dms = lib.einsum('nkij,pi,qj->nkpq', dms_lh, l_coeff, h_coeff)
shls_slice = (nshells_h, nshells_t, 0, nshells_h)
#:rho += eval_rho(t_cell, pgto_dms, shls_slice, 0, 'GGA', kpts,
#: ignore_imag=ignore_imag)
rho += _eval_rho_ket(t_cell, pgto_dms, shls_slice, 0, 'GGA',
kpts, grids_dense, ignore_imag, log)
if hermi == 1:
# \nabla \chi_i DM(i,j) \chi_j was computed above.
# *2 for \chi_i DM(i,j) \nabla \chi_j
rho[:,1:4] *= 2
else:
raise NotImplementedError
weight = 1./nkpts * cell.vol/ngrids
rho_freq = tools.fft(rho.reshape(nset*rhodim, -1), mesh)
rho_freq *= weight
gx = numpy.fft.fftfreq(mesh[0], 1./mesh[0]).astype(numpy.int32)
gy = numpy.fft.fftfreq(mesh[1], 1./mesh[1]).astype(numpy.int32)
gz = numpy.fft.fftfreq(mesh[2], 1./mesh[2]).astype(numpy.int32)
#:rhoG[:,gx[:,None,None],gy[:,None],gz] += rho_freq.reshape((-1,)+mesh)
_takebak_4d(rhoG, rho_freq.reshape((-1,) + mesh), (None, gx, gy, gz))
rhoG = rhoG.reshape(nset,rhodim,-1)
if gga_high_order:
Gv = cell.get_Gv(mydf.mesh)
rhoG1 = numpy.einsum('np,px->nxp', 1j*rhoG[:,0], Gv)
rhoG = numpy.concatenate([rhoG, rhoG1], axis=1)
return rhoG
def _eval_rho_bra(cell, dms, shls_slice, hermi, xctype, kpts, grids,
ignore_imag, log):
a = cell.lattice_vectors()
rmax = a.max()
mesh = numpy.asarray(grids.mesh)
rcut = grids.cell.rcut
nset = dms.shape[0]
if xctype == 'LDA':
rhodim = 1
else:
rhodim = 4
if rcut > rmax * R_RATIO_SUBLOOP:
rho = eval_rho(cell, dms, shls_slice, hermi, xctype, kpts,
mesh, ignore_imag=ignore_imag)
return numpy.reshape(rho, (nset, rhodim, numpy.prod(mesh)))
if hermi == 1 or ignore_imag:
rho = numpy.zeros((nset, rhodim) + tuple(mesh))
else:
rho = numpy.zeros((nset, rhodim) + tuple(mesh), dtype=numpy.complex128)
b = numpy.linalg.inv(a.T)
ish0, ish1, jsh0, jsh1 = shls_slice
nshells_j = jsh1 - jsh0
pcell = cell.copy(deep=False)
rest_dms = []
rest_bas = []
i1 = 0
for atm_id in set(cell._bas[ish0:ish1,ATOM_OF]):
atm_bas_idx = numpy.where(cell._bas[ish0:ish1,ATOM_OF] == atm_id)[0]
_bas_i = cell._bas[atm_bas_idx]
l = _bas_i[:,ANG_OF]
i0, i1 = i1, i1 + sum((l+1)*(l+2)//2)
sub_dms = dms[:,:,i0:i1]
atom_position = cell.atom_coord(atm_id)
frac_edge0 = b.dot(atom_position - rcut)
frac_edge1 = b.dot(atom_position + rcut)
if (numpy.all(0 < frac_edge0) and numpy.all(frac_edge1 < 1)):
pcell._bas = numpy.vstack((_bas_i, cell._bas[jsh0:jsh1]))
nshells_i = len(atm_bas_idx)
sub_slice = (0, nshells_i, nshells_i, nshells_i+nshells_j)
offset = (frac_edge0 * mesh).astype(int)
mesh1 = numpy.ceil(frac_edge1 * mesh).astype(int)
submesh = mesh1 - offset
log.debug1('atm %d rcut %f offset %s submesh %s',
atm_id, rcut, offset, submesh)
rho1 = eval_rho(pcell, sub_dms, sub_slice, hermi, xctype, kpts,
mesh, offset, submesh, ignore_imag=ignore_imag)
#:rho[:,:,offset[0]:mesh1[0],offset[1]:mesh1[1],offset[2]:mesh1[2]] += \
#: numpy.reshape(rho1, (nset, rhodim) + tuple(submesh))
gx = numpy.arange(offset[0], mesh1[0], dtype=numpy.int32)
gy = numpy.arange(offset[1], mesh1[1], dtype=numpy.int32)
gz = numpy.arange(offset[2], mesh1[2], dtype=numpy.int32)
_takebak_5d(rho, numpy.reshape(rho1, (nset,rhodim)+tuple(submesh)),
(None, None, gx, gy, gz))
else:
log.debug1('atm %d rcut %f over 2 images', atm_id, rcut)
#:rho1 = eval_rho(pcell, sub_dms, sub_slice, hermi, xctype, kpts,
#: mesh, ignore_imag=ignore_imag)
#:rho += numpy.reshape(rho1, rho.shape)
# or
#:eval_rho(pcell, sub_dms, sub_slice, hermi, xctype, kpts,
#: mesh, ignore_imag=ignore_imag, out=rho)
rest_bas.append(_bas_i)
rest_dms.append(sub_dms)
if rest_bas:
pcell._bas = numpy.vstack(rest_bas + [cell._bas[jsh0:jsh1]])
nshells_i = sum(len(x) for x in rest_bas)
sub_slice = (0, nshells_i, nshells_i, nshells_i+nshells_j)
sub_dms = numpy.concatenate(rest_dms, axis=2)
# Update rho inplace
eval_rho(pcell, sub_dms, sub_slice, hermi, xctype, kpts,
mesh, ignore_imag=ignore_imag, out=rho)
return rho.reshape((nset, rhodim, numpy.prod(mesh)))
def _eval_rho_ket(cell, dms, shls_slice, hermi, xctype, kpts, grids,
ignore_imag, log):
a = cell.lattice_vectors()
rmax = a.max()
mesh = numpy.asarray(grids.mesh)
rcut = grids.cell.rcut
nset = dms.shape[0]
if xctype == 'LDA':
rhodim = 1
else:
rhodim = 4
if rcut > rmax * R_RATIO_SUBLOOP:
rho = eval_rho(cell, dms, shls_slice, hermi, xctype, kpts,
mesh, ignore_imag=ignore_imag)
return numpy.reshape(rho, (nset, rhodim, numpy.prod(mesh)))
if hermi == 1 or ignore_imag:
rho = numpy.zeros((nset, rhodim) + tuple(mesh))
else:
rho = numpy.zeros((nset, rhodim) + tuple(mesh), dtype=numpy.complex128)
b = numpy.linalg.inv(a.T)
ish0, ish1, jsh0, jsh1 = shls_slice
nshells_i = ish1 - ish0
pcell = cell.copy(deep=False)
rest_dms = []
rest_bas = []
j1 = 0
for atm_id in set(cell._bas[jsh0:jsh1,ATOM_OF]):
atm_bas_idx = numpy.where(cell._bas[jsh0:jsh1,ATOM_OF] == atm_id)[0]
_bas_j = cell._bas[atm_bas_idx]
l = _bas_j[:,ANG_OF]
j0, j1 = j1, j1 + sum((l+1)*(l+2)//2)
sub_dms = dms[:,:,:,j0:j1]
atom_position = cell.atom_coord(atm_id)
frac_edge0 = b.dot(atom_position - rcut)
frac_edge1 = b.dot(atom_position + rcut)
if (numpy.all(0 < frac_edge0) and numpy.all(frac_edge1 < 1)):
pcell._bas = numpy.vstack((cell._bas[ish0:ish1], _bas_j))
nshells_j = len(atm_bas_idx)
sub_slice = (0, nshells_i, nshells_i, nshells_i+nshells_j)
offset = (frac_edge0 * mesh).astype(int)
mesh1 = numpy.ceil(frac_edge1 * mesh).astype(int)
submesh = mesh1 - offset
log.debug1('atm %d rcut %f offset %s submesh %s',
atm_id, rcut, offset, submesh)
rho1 = eval_rho(pcell, sub_dms, sub_slice, hermi, xctype, kpts,
mesh, offset, submesh, ignore_imag=ignore_imag)
#:rho[:,:,offset[0]:mesh1[0],offset[1]:mesh1[1],offset[2]:mesh1[2]] += \
#: numpy.reshape(rho1, (nset, rhodim) + tuple(submesh))
gx = numpy.arange(offset[0], mesh1[0], dtype=numpy.int32)
gy = numpy.arange(offset[1], mesh1[1], dtype=numpy.int32)
gz = numpy.arange(offset[2], mesh1[2], dtype=numpy.int32)
_takebak_5d(rho, numpy.reshape(rho1, (nset,rhodim)+tuple(submesh)),
(None, None, gx, gy, gz))
else:
log.debug1('atm %d rcut %f over 2 images', atm_id, rcut)
#:rho1 = eval_rho(pcell, sub_dms, sub_slice, hermi, xctype, kpts,
#: mesh, ignore_imag=ignore_imag)
#:rho += numpy.reshape(rho1, rho.shape)
#:eval_rho(pcell, sub_dms, sub_slice, hermi, xctype, kpts,
#: mesh, ignore_imag=ignore_imag, out=rho)
rest_bas.append(_bas_j)
rest_dms.append(sub_dms)
if rest_bas:
pcell._bas = numpy.vstack([cell._bas[ish0:ish1]] + rest_bas)
nshells_j = sum(len(x) for x in rest_bas)
sub_slice = (0, nshells_i, nshells_i, nshells_i+nshells_j)
sub_dms = numpy.concatenate(rest_dms, axis=3)
# Update rho inplace
eval_rho(pcell, sub_dms, sub_slice, hermi, xctype, kpts,
mesh, ignore_imag=ignore_imag, out=rho)
return rho.reshape((nset, rhodim, numpy.prod(mesh)))
def _get_j_pass2(mydf, vG, hermi=1, kpts=numpy.zeros((1,3)), verbose=None):
log = logger.new_logger(mydf, verbose)
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]
tasks = getattr(mydf, 'tasks', None)
if tasks is None:
mydf.tasks = tasks = multi_grids_tasks(cell, mydf.mesh, log)
log.debug('Multigrid ntasks %s', len(tasks))
at_gamma_point = gamma_point(kpts)
if at_gamma_point:
vj_kpts = numpy.zeros((nset,nkpts,nao,nao))
else:
vj_kpts = numpy.zeros((nset,nkpts,nao,nao), dtype=numpy.complex128)
for grids_dense, grids_sparse in tasks:
mesh = grids_dense.mesh
ngrids = numpy.prod(mesh)
log.debug('mesh %s', mesh)
gx = numpy.fft.fftfreq(mesh[0], 1./mesh[0]).astype(numpy.int32)
gy = numpy.fft.fftfreq(mesh[1], 1./mesh[1]).astype(numpy.int32)
gz = numpy.fft.fftfreq(mesh[2], 1./mesh[2]).astype(numpy.int32)
#:sub_vG = vG[:,gx[:,None,None],gy[:,None],gz].reshape(nset,ngrids)
sub_vG = _take_4d(vG, (None, gx, gy, gz)).reshape(nset,ngrids)
v_rs = tools.ifft(sub_vG, mesh).reshape(nset,ngrids)
vR = numpy.asarray(v_rs.real, order='C')
vI = numpy.asarray(v_rs.imag, order='C')
ignore_vG_imag = hermi == 1 or abs(vI.sum()) < IMAG_TOL
if ignore_vG_imag:
v_rs = vR
elif vj_kpts.dtype == numpy.double:
# ensure result complex array if tddft amplitudes are complex while
# at gamma point
vj_kpts = vj_kpts.astype(numpy.complex128)
idx_h = grids_dense.ao_idx
if grids_sparse is None:
for ao_h_etc, p0, p1 in mydf.aoR_loop(grids_dense, kpts):
ao_h = ao_h_etc[0]
for k in range(nkpts):
for i in range(nset):
aow = numint._scale_ao(ao_h[k], v_rs[i,p0:p1])
vj_sub = lib.dot(ao_h[k].conj().T, aow)
vj_kpts[i,k,idx_h[:,None],idx_h] += vj_sub
ao_h = ao_h_etc = None
else:
idx_h = grids_dense.ao_idx
idx_l = grids_sparse.ao_idx
# idx_t = numpy.append(idx_h, idx_l)
naoh = len(idx_h)
h_cell = grids_dense.cell
l_cell = grids_sparse.cell
h_pcell, h_coeff = h_cell.decontract_basis(to_cart=True, aggregate=True)
l_pcell, l_coeff = l_cell.decontract_basis(to_cart=True, aggregate=True)
t_cell = h_pcell + l_pcell
t_coeff = scipy.linalg.block_diag(h_coeff, l_coeff)
nshells_h = _pgto_shells(h_cell)
nshells_t = _pgto_shells(t_cell)
shls_slice = (0, nshells_h, 0, nshells_t)
vp = eval_mat(t_cell, vR, shls_slice, 1, 0, 'LDA', kpts)
# Imaginary part may contribute
if not ignore_vG_imag:
vpI = eval_mat(t_cell, vI, shls_slice, 1, 0, 'LDA', kpts)
vp = numpy.asarray(vp) + numpy.asarray(vpI) * 1j
vpI = None
vp = lib.einsum('nkpq,pi,qj->nkij', vp, h_coeff, t_coeff)
vj_kpts[:,:,idx_h[:,None],idx_h] += vp[:,:,:,:naoh]
vj_kpts[:,:,idx_h[:,None],idx_l] += vp[:,:,:,naoh:]
if hermi == 1:
vj_kpts[:,:,idx_l[:,None],idx_h] += \
vp[:,:,:,naoh:].transpose(0,1,3,2).conj()
else:
shls_slice = (nshells_h, nshells_t, 0, nshells_h)
vp = eval_mat(t_cell, vR, shls_slice, 1, 0, 'LDA', kpts)
# Imaginary part may contribute
if not ignore_vG_imag:
vpI = eval_mat(t_cell, vI, shls_slice, 1, 0, 'LDA', kpts)
vp = numpy.asarray(vp) + numpy.asarray(vpI) * 1j
vpI = None
vp = lib.einsum('nkpq,pi,qj->nkij', vp, l_coeff, h_coeff)
vj_kpts[:,:,idx_l[:,None],idx_h] += vp
return vj_kpts
def _get_gga_pass2(mydf, vG, hermi=1, kpts=numpy.zeros((1,3)), verbose=None):
#if hermi != 1:
# raise NotImplementedError('_get_gga_pass2 assumes hermi=1')
log = logger.new_logger(mydf, verbose)
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]
at_gamma_point = gamma_point(kpts)
if at_gamma_point:
veff = numpy.zeros((nset,nkpts,nao,nao))
else:
veff = numpy.zeros((nset,nkpts,nao,nao), dtype=numpy.complex128)
for grids_dense, grids_sparse in mydf.tasks:
mesh = grids_dense.mesh
ngrids = numpy.prod(mesh)
log.debug('mesh %s', mesh)
gx = numpy.fft.fftfreq(mesh[0], 1./mesh[0]).astype(numpy.int32)
gy = numpy.fft.fftfreq(mesh[1], 1./mesh[1]).astype(numpy.int32)
gz = numpy.fft.fftfreq(mesh[2], 1./mesh[2]).astype(numpy.int32)
#:sub_vG = vG[:,:,gx[:,None,None],gy[:,None],gz].reshape(-1,ngrids)
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 = numpy.asarray(v_rs.real, order='C')
vI = numpy.asarray(v_rs.imag, order='C')
ignore_vG_imag = hermi == 1 or abs(vI.sum()) < IMAG_TOL
if ignore_vG_imag:
v_rs = vR
elif veff.dtype == numpy.double:
# ensure result complex array if tddft amplitudes are complex while
# at gamma point
veff = veff.astype(numpy.complex128)
if grids_sparse is None:
idx_h = grids_dense.ao_idx
naoh = len(idx_h)
for ao_h_etc, p0, p1 in mydf.aoR_loop(grids_dense, kpts, deriv=1):
ao_h = ao_h_etc[0]
for k in range(nkpts):
for i in range(nset):
aow = numint._scale_ao(ao_h[k], v_rs[i])
v = lib.dot(ao_h[k][0].conj().T, aow)
veff[i,k,idx_h[:,None],idx_h] += v
if hermi == 1:
veff[i,k,idx_h[:,None],idx_h] += v.conj().T
else:
aow = numint._scale_ao(ao_h[k], v_rs[i].conj())
v = lib.dot(aow.conj().T, ao_h[k][0])
veff[i,k,idx_h[:,None],idx_h] += v
ao_h = ao_h_etc = None
else:
idx_h = grids_dense.ao_idx
idx_l = grids_sparse.ao_idx
# idx_t = numpy.append(idx_h, idx_l)
naoh = len(idx_h)
h_cell = grids_dense.cell
l_cell = grids_sparse.cell
h_pcell, h_coeff = h_cell.decontract_basis(to_cart=True, aggregate=True)
l_pcell, l_coeff = l_cell.decontract_basis(to_cart=True, aggregate=True)
t_cell = h_pcell + l_pcell
t_coeff = scipy.linalg.block_diag(h_coeff, l_coeff)
nshells_h = _pgto_shells(h_cell)
nshells_t = _pgto_shells(t_cell)
shls_slice = (0, nshells_h, 0, nshells_t)
vpR = eval_mat(t_cell, vR, shls_slice, 1, 0, 'GGA', kpts)
vp = vpR = lib.einsum('nkpq,pi,qj->nkij', vpR, h_coeff, t_coeff)
if not ignore_vG_imag:
vpI = eval_mat(t_cell, vI, shls_slice, 1, 0, 'GGA', kpts)
vpI = lib.einsum('nkpq,pi,qj->nkij', vpI, h_coeff, t_coeff)
vp = numpy.asarray(vpR) + numpy.asarray(vpI) * 1j
veff[:,:,idx_h[:,None],idx_h] += vp[:,:,:,:naoh]
veff[:,:,idx_h[:,None],idx_l] += vp[:,:,:,naoh:]
if hermi == 1:
veff[:,:,idx_h[:,None],idx_h] += vp[:,:,:,:naoh].conj().transpose(0,1,3,2)
veff[:,:,idx_l[:,None],idx_h] += vp[:,:,:,naoh:].conj().transpose(0,1,3,2)
else:
if not ignore_vG_imag:
# eval_mat only supports <nabla i|v|j>. Evaluate <i|v|nabla j>
# by conj(<nabla j|conj(v)|>)
vp = numpy.asarray(vpR) - numpy.asarray(vpI) * 1j
veff[:,:,idx_h[:,None],idx_h] += vp[:,:,:,:naoh].conj().transpose(0,1,3,2)
veff[:,:,idx_l[:,None],idx_h] += vp[:,:,:,naoh:].conj().transpose(0,1,3,2)
shls_slice = (nshells_h, nshells_t, 0, nshells_h)
vpR = eval_mat(t_cell, vR, shls_slice, 1, 0, 'GGA', kpts)
vp = vpR = lib.einsum('nkpq,pi,qj->nkij', vpR, l_coeff, h_coeff)
if not ignore_vG_imag:
vpI = eval_mat(t_cell, vI, shls_slice, 1, 0, 'GGA', kpts)
vpI = lib.einsum('nkpq,pi,qj->nkij', vpI, l_coeff, h_coeff)
vp = numpy.asarray(vpR) + numpy.asarray(vpI) * 1j
veff[:,:,idx_l[:,None],idx_h] += vp
if hermi == 1:
veff[:,:,idx_h[:,None],idx_l] += vp.conj().transpose(0,1,3,2)
else:
if not ignore_vG_imag:
vp = numpy.asarray(vpR) - numpy.asarray(vpI) * 1j
veff[:,:,idx_h[:,None],idx_l] += vp.conj().transpose(0,1,3,2)
return veff
[docs]
def nr_rks(mydf, xc_code, dm_kpts, hermi=1, kpts=None,
kpts_band=None, with_j=False, return_j=False, verbose=None):
'''Compute the XC energy and RKS XC matrix at sampled k-points.
multigrid version of function pbc.dft.numint.nr_rks.
Args:
dm_kpts : (nkpts, nao, nao) ndarray or a list of (nkpts,nao,nao) ndarray
Density matrix at each k-point.
kpts : (nkpts, 3) ndarray
Kwargs:
kpts_band : (3,) ndarray or (*,3) ndarray
A list of arbitrary "band" k-points at which to evalute the matrix.
Returns:
exc : XC energy
nelec : number of electrons obtained from the numerical integration
veff : (nkpts, nao, nao) ndarray
or list of veff if the input dm_kpts is a list of DMs
vj : (nkpts, nao, nao) ndarray
or list of vj if the input dm_kpts is a list of DMs
'''
if kpts is None: kpts = mydf.kpts
log = logger.new_logger(mydf, verbose)
cell = mydf.cell
dm_kpts = lib.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
elif xctype == 'MGGA':
deriv = 2 if MGGA_DENSITY_LAPL else 1
raise NotImplementedError
rhoG = _eval_rhoG(mydf, dm_kpts, hermi, kpts, deriv)
mesh = mydf.mesh
ngrids = numpy.prod(mesh)
coulG = tools.get_coulG(cell, mesh=mesh)
vG = numpy.einsum('ng,g->ng', rhoG[:,0], coulG)
ecoul = .5 * numpy.einsum('ng,ng->n', rhoG[:,0].real, vG.real)
ecoul+= .5 * numpy.einsum('ng,ng->n', rhoG[:,0].imag, vG.imag)
ecoul /= cell.vol
log.debug('Multigrid Coulomb energy %s', ecoul)
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)
nelec = rhoR[:,0].sum(axis=1) * weight
wv_freq = []
excsum = numpy.zeros(nset)
for i in range(nset):
if xctype == 'LDA':
exc, vxc = ni.eval_xc_eff(xc_code, rhoR[i,0], deriv=1, xctype=xctype)[:2]
else:
exc, vxc = ni.eval_xc_eff(xc_code, rhoR[i], deriv=1, xctype=xctype)[:2]
excsum[i] += (rhoR[i,0]*exc).sum() * weight
wv = weight * vxc
wv_freq.append(tools.fft(wv, mesh))
wv_freq = numpy.asarray(wv_freq).reshape(nset,-1,*mesh)
rhoR = rhoG = None
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, hermi, kpts_band, verbose=log)
elif xctype == 'GGA':
if with_j:
wv_freq[:,0] += vG.reshape(nset,*mesh)
# *.5 because v+v.T is always called in _get_gga_pass2
wv_freq[:,0] *= .5
veff = _get_gga_pass2(mydf, wv_freq, hermi, kpts_band, verbose=log)
veff = _format_jks(veff, dm_kpts, input_band, kpts)
if return_j:
vj = _get_j_pass2(mydf, vG, hermi, kpts_band, verbose=log)
vj = _format_jks(veff, dm_kpts, input_band, kpts)
else:
vj = None
shape = list(dm_kpts.shape)
if len(shape) == 3 and shape[0] != kpts_band.shape[0]:
shape[0] = kpts_band.shape[0]
veff = veff.reshape(shape)
veff = lib.tag_array(veff, ecoul=ecoul, exc=excsum, vj=vj, vk=None)
return nelec, excsum, veff
# Note nr_uks handles only one set of KUKS density matrices (alpha, beta) in
# each call (nr_rks supports multiple sets of KRKS density matrices)
[docs]
def nr_uks(mydf, xc_code, dm_kpts, hermi=1, kpts=None,
kpts_band=None, with_j=False, return_j=False, verbose=None):
'''Compute the XC energy and UKS XC matrix at sampled k-points.
multigrid version of function pbc.dft.numint.nr_uks
Args:
dm_kpts : (nkpts, nao, nao) ndarray or a list of (nkpts,nao,nao) ndarray
Density matrix at each k-point.
kpts : (nkpts, 3) ndarray
Kwargs:
kpts_band : (3,) ndarray or (*,3) ndarray
A list of arbitrary "band" k-points at which to evalute the matrix.
Returns:
exc : XC energy
nelec : number of electrons obtained from the numerical integration
veff : (2, nkpts, nao, nao) ndarray
or list of veff if the input dm_kpts is a list of DMs
vj : (nkpts, nao, nao) ndarray
or list of vj if the input dm_kpts is a list of DMs
'''
if kpts is None:
kpts = mydf.kpts
log = logger.new_logger(mydf, verbose)
cell = mydf.cell
dm_kpts = lib.asarray(dm_kpts, order='C')
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
nset //= 2
# Do not support gks
assert nset == 1
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
elif xctype == 'MGGA':
raise NotImplementedError
mesh = mydf.mesh
ngrids = numpy.prod(mesh)
rhoG = _eval_rhoG(mydf, dm_kpts, hermi, kpts, deriv)
rhoG = rhoG.reshape(nset,2,-1,ngrids)
coulG = tools.get_coulG(cell, mesh=mesh)
vG = numpy.einsum('nsg,g->ng', rhoG[:,:,0], coulG)
ecoul = .5 * numpy.einsum('nsg,ng->n', rhoG[:,:,0].real, vG.real)
ecoul+= .5 * numpy.einsum('nsg,ng->n', rhoG[:,:,0].imag, vG.imag)
ecoul /= cell.vol
log.debug('Multigrid Coulomb energy %s', ecoul)
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)
nelec = numpy.einsum('nsg->n', rhoR[:,:,0]) * weight
wv_freq = []
excsum = numpy.zeros(nset)
for i in range(nset):
exc, vxc = ni.eval_xc_eff(xc_code, rhoR[i], deriv=1, xctype=xctype)[:2]
excsum[i] = (rhoR[i,:,0]*exc).sum() * weight
log.debug('Multigrid exc %g nelec %s', excsum, nelec[i])
wv = weight * vxc
wv_freq.append(tools.fft(wv, mesh))
wv_freq = numpy.asarray(wv_freq).reshape(nset,2,-1,*mesh)
rhoR = rhoG = None
if with_j:
wv_freq[:,:,0] += vG.reshape(*mesh)
if xctype == 'LDA':
veff = _get_j_pass2(mydf, wv_freq, hermi, kpts_band, verbose=log)
elif xctype == 'GGA':
# *.5 because v+v.T is always called in _get_gga_pass2
wv_freq[:,0] *= .5
veff = _get_gga_pass2(mydf, wv_freq, hermi, kpts_band, verbose=log)
veff = _format_jks(veff, dm_kpts, input_band, kpts)
veff = veff.reshape(nset, 2, len(kpts_band), nao, nao)
if nset == 1:
veff = veff[0]
ecoul = ecoul[0]
nelec = nelec[0]
excsum = excsum[0]
if return_j:
vj = _get_j_pass2(mydf, vG, hermi, kpts_band, verbose=log)
vj = _format_jks(veff, dm_kpts, input_band, kpts)
if nset == 1:
vj = vj[0]
else:
vj = None
shape = list(dm_kpts.shape)
if len(shape) == 4 and shape[1] != kpts_band.shape[0]:
shape[1] = kpts_band.shape[0]
veff = veff.reshape(shape)
veff = lib.tag_array(veff, ecoul=ecoul, exc=excsum, vj=vj, vk=None)
return nelec, excsum, veff
[docs]
def nr_rks_fxc(mydf, xc_code, dm0, dms, hermi=0, with_j=False,
rho0=None, vxc=None, fxc=None, kpts=None, verbose=None):
'''multigrid version of function pbc.dft.numint.nr_rks_fxc
'''
if kpts is None:
kpts = numpy.zeros((1,3))
log = logger.new_logger(mydf, verbose)
cell = mydf.cell
mesh = mydf.mesh
ngrids = numpy.prod(mesh)
dm_kpts = lib.asarray(dms, order='C')
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
ni = mydf._numint
xctype = ni._xc_type(xc_code)
if xctype == 'LDA':
deriv = 0
elif xctype == 'GGA':
deriv = 1
elif xctype == 'MGGA':
deriv = 2 if MGGA_DENSITY_LAPL else 1
raise NotImplementedError
weight = cell.vol / ngrids
if rho0 is None:
rhoG = _eval_rhoG(mydf, dm0, hermi, kpts, deriv)
rho0 = tools.ifft(rhoG.reshape(-1,ngrids), mesh).real * (1./weight)
if xctype == 'LDA':
rho0 = rho0.reshape(ngrids)
if fxc is None:
fxc = ni.eval_xc_eff(xc_code, rho0, deriv=2, xctype=xctype)[2]
rhoG = _eval_rhoG(mydf, dms, hermi, kpts, deriv)
rho1 = tools.ifft(rhoG.reshape(-1,ngrids), mesh)
if hermi == 1:
rho1 = rho1.real
rho1 *= (1./weight)
rho1 = rho1.reshape(nset,-1,ngrids)
wv = numpy.einsum('nxg,xyg->nyg', rho1, fxc)
wv *= weight
wv = tools.fft(wv.reshape(-1,ngrids), mesh).reshape(nset,-1,*mesh)
if with_j:
coulG = tools.get_coulG(cell, mesh=mesh)
vG = rhoG[:,0] * coulG
vG = vG.reshape(nset, *mesh)
wv[:,0] += vG
if xctype == 'LDA':
veff = _get_j_pass2(mydf, wv, hermi, kpts, verbose=log)
elif xctype == 'GGA':
# *.5 because v+v.T is always called in _get_gga_pass2
wv[:,0] *= .5
veff = _get_gga_pass2(mydf, wv, hermi, kpts, verbose=log)
return veff.reshape(dm_kpts.shape)
[docs]
def nr_rks_fxc_st(mydf, xc_code, dm0, dms_alpha, singlet=True,
rho0=None, vxc=None, fxc=None, kpts=None, verbose=None):
'''multigrid version of function pbc.dft.numint.nr_rks_fxc_st
'''
if kpts is None:
kpts = numpy.zeros((1,3))
log = logger.new_logger(mydf, verbose)
cell = mydf.cell
mesh = mydf.mesh
ngrids = numpy.prod(mesh)
dm_kpts = lib.asarray(dms_alpha, order='C')
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
ni = mydf._numint
xctype = ni._xc_type(xc_code)
if xctype == 'LDA':
deriv = 0
elif xctype == 'GGA':
deriv = 1
elif xctype == 'MGGA':
deriv = 2 if MGGA_DENSITY_LAPL else 1
raise NotImplementedError
weight = cell.vol / ngrids
if rho0 is None:
rhoG = _eval_rhoG(mydf, dm0, 1, kpts, deriv)
# *.5 to get alpha density
rho0 = tools.ifft(rhoG.reshape(-1,ngrids), mesh).real * (.5/weight)
if xctype == 'LDA':
rho0 = rho0.reshape(ngrids)
rho0 = numpy.stack((rho0, rho0))
if gamma_point(kpts):
# implies real orbitals and real matrix, thus K_{ia,bj} = K_{ia,jb}
# The output matrix v = K*x_{ia} is symmetric
hermi = 1
else:
hermi = 0
if fxc is None:
fxc = ni.eval_xc_eff(xc_code, rho0, deriv=2, xctype=xctype)[2]
if singlet:
fxc = fxc[0,:,0] + fxc[0,:,1]
else:
fxc = fxc[0,:,0] - fxc[0,:,1]
rhoG = _eval_rhoG(mydf, dms, hermi, kpts, deriv)
rho1 = tools.ifft(rhoG.reshape(-1,ngrids), mesh)
if hermi == 1:
rho1 = rho1.real
rho1 *= (1./weight)
rho1 = rho1.reshape(nset,-1,ngrids)
wv = numpy.einsum('nxg,xyg->nyg', rho1, fxc)
wv *= weight
wv = tools.fft(wv.reshape(-1,ngrids), mesh).reshape(nset,-1,*mesh)
if xctype == 'LDA':
veff = _get_j_pass2(mydf, wv, hermi, kpts, verbose=log)
elif xctype == 'GGA':
# *.5 because v+v.T is always called in _get_gga_pass2
wv[:,0] *= .5
veff = _get_gga_pass2(mydf, wv, hermi, kpts, verbose=log)
return veff.reshape(dm_kpts.shape)
[docs]
def nr_uks_fxc(mydf, xc_code, dm0, dms, hermi=0, with_j=False,
rho0=None, vxc=None, fxc=None, kpts=None, verbose=None):
'''multigrid version of function pbc.dft.numint.nr_uks_fxc
'''
if kpts is None:
kpts = numpy.zeros((1,3))
log = logger.new_logger(mydf, verbose)
cell = mydf.cell
mesh = mydf.mesh
ngrids = numpy.prod(mesh)
dm_kpts = lib.asarray(dms, order='C')
dms = _format_dms(dm_kpts, kpts)
nset, nkpts, nao = dms.shape[:3]
nstates = nset // 2
ni = mydf._numint
xctype = ni._xc_type(xc_code)
if xctype == 'LDA':
deriv = 0
elif xctype == 'GGA':
deriv = 1
elif xctype == 'MGGA':
deriv = 2 if MGGA_DENSITY_LAPL else 1
raise NotImplementedError
weight = cell.vol / ngrids
if rho0 is None:
rhoG = _eval_rhoG(mydf, dm0, hermi, kpts, deriv)
rho0 = tools.ifft(rhoG.reshape(-1,ngrids), mesh).real * (1./weight)
if xctype == 'LDA':
rho0 = rho0.reshape(2,ngrids)
else:
rho0 = rho0.reshape(2,-1,ngrids)
if fxc is None:
fxc = ni.eval_xc_eff(xc_code, rho0, deriv=2, xctype=xctype)[2]
rhoG = _eval_rhoG(mydf, dms, hermi, kpts, deriv)
rho1 = tools.ifft(rhoG.reshape(-1,ngrids), mesh)
if hermi == 1:
rho1 = rho1.real
rho1 *= (1./weight)
# rho1 = (rho1a, rho1b); rho1.shape = (2, nstates, nvar, ngrids)
rho1 = rho1.reshape(2,nstates,-1,ngrids)
wv = numpy.einsum('anxg,axbyg->nbyg', rho1, fxc)
wv *= weight
wv = tools.fft(wv.reshape(-1,ngrids), mesh).reshape(nset,-1,*mesh)
if with_j:
coulG = tools.get_coulG(cell, mesh=mesh)
vG = (rhoG[0,0] + rhoG[1,0]) * coulG
vG = vG.reshape(mesh)
wv[:,0] += vG
if xctype == 'LDA':
veff = _get_j_pass2(mydf, wv, hermi, kpts, verbose=log)
elif xctype == 'GGA':
# *.5 because v+v.T is always called in _get_gga_pass2
wv[:,0] *= .5
veff = _get_gga_pass2(mydf, wv, hermi, kpts, verbose=log)
return veff.reshape(dm_kpts.shape)
[docs]
def cache_xc_kernel(mydf, xc_code, mo_coeff, mo_occ, spin=0, kpts=None):
raise NotImplementedError
[docs]
def cache_xc_kernel1(mydf, xc_code, dm, spin=0, kpts=None):
'''Compute the 0th order density, Vxc and fxc. They can be used in TDDFT,
DFT hessian module etc.
'''
if kpts is None:
kpts = numpy.zeros((1,3))
cell = mydf.cell
mesh = mydf.mesh
ngrids = numpy.prod(mesh)
ni = mydf._numint
xctype = ni._xc_type(xc_code)
if xctype == 'LDA':
deriv = 0
comp = 1
elif xctype == 'GGA':
deriv = 1
comp = 4
elif xctype == 'MGGA':
deriv = 2 if MGGA_DENSITY_LAPL else 1
comp = 6
hermi = 1
weight = cell.vol / ngrids
rhoG = _eval_rhoG(mydf, dm, hermi, kpts, deriv)
rho = tools.ifft(rhoG.reshape(-1,ngrids), mesh).real * (1./weight)
rho = rho.reshape(rhoG.shape)
n_dm, comp, ngrids = rho.shape
if n_dm == 1 and spin == 1:
rho = numpy.repeat(rho, 2, axis=0)
rho *= .5
if xctype == 'LDA':
assert comp == 1
rho = rho[:,0]
else:
assert comp > 1
if spin == 0:
assert n_dm == 1
rho = rho[0]
vxc, fxc = ni.eval_xc_eff(xc_code, rho, deriv=2, xctype=xctype)[1:3]
return rho, vxc, fxc
def _gen_rhf_response(mf, dm0, singlet=None, hermi=0):
'''multigrid version of function pbc.scf.newton_ah._gen_rhf_response
'''
#assert (isinstance(mf, dft.krks.KRKS))
if getattr(mf, 'kpts', None) is not None:
kpts = mf.kpts
else:
kpts = mf.kpt.reshape(1,3)
if singlet is None: # for newton solver
rho0, vxc, fxc = cache_xc_kernel1(mf.with_df, mf.xc, dm0, 0, kpts)
else:
rho0, vxc, fxc = cache_xc_kernel1(mf.with_df, mf.xc, dm0, 1, kpts)
dm0 = None
def vind(dm1):
if hermi == 2:
return numpy.zeros_like(dm1)
if singlet is None: # Without specify singlet, general case
v1 = nr_rks_fxc(mf.with_df, mf.xc, dm0, dm1, hermi,
True, rho0, vxc, fxc, kpts)
elif singlet:
v1 = nr_rks_fxc_st(mf.with_df, mf.xc, dm0, dm1, singlet,
rho0, vxc, fxc, kpts)
else:
v1 = nr_rks_fxc_st(mf.with_df, mf.xc, dm0, dm1, singlet,
rho0, vxc, fxc, kpts)
return v1
return vind
def _gen_uhf_response(mf, dm0, with_j=True, hermi=0):
'''multigrid version of function pbc.scf.newton_ah._gen_uhf_response
'''
#assert (isinstance(mf, dft.kuks.KUKS))
if getattr(mf, 'kpts', None) is not None:
kpts = mf.kpts
else:
kpts = mf.kpt.reshape(1,3)
rho0, vxc, fxc = cache_xc_kernel1(mf.with_df, mf.xc, dm0, 1, kpts)
dm0 = None
def vind(dm1):
if hermi == 2:
return numpy.zeros_like(dm1)
v1 = nr_uks_fxc(mf.with_df, mf.xc, dm0, dm1, hermi,
with_j, rho0, vxc, fxc, kpts)
return v1
return vind
[docs]
def get_rho(mydf, dm, kpts=numpy.zeros((1,3))):
'''Density in real space
'''
cell = mydf.cell
hermi = 1
rhoG = _eval_rhoG(mydf, numpy.asarray(dm), hermi, kpts, deriv=0)
mesh = mydf.mesh
ngrids = numpy.prod(mesh)
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(ngrids), mesh).real * (1./weight)
return rhoR
[docs]
def multi_grids_tasks(cell, fft_mesh=None, verbose=None):
if TASKS_TYPE == 'rcut':
return multi_grids_tasks_for_rcut(cell, fft_mesh, verbose)
else:
return multi_grids_tasks_for_ke_cut(cell, fft_mesh, verbose)
[docs]
def multi_grids_tasks_for_rcut(cell, fft_mesh=None, verbose=None):
log = logger.new_logger(cell, verbose)
if fft_mesh is None:
fft_mesh = cell.mesh
# Split shells based on rcut
rcuts_pgto, kecuts_pgto = _primitive_gto_cutoff(cell)
ao_loc = cell.ao_loc_nr()
def make_cell_dense_exp(shls_dense, r0, r1):
cell_dense = cell.copy(deep=False)
cell_dense._bas = cell._bas.copy()
cell_dense._env = cell._env.copy()
rcut_atom = [0] * cell.natm
ke_cutoff = 0
for ib in shls_dense:
rc = rcuts_pgto[ib]
idx = numpy.where((r1 <= rc) & (rc < r0))[0]
np1 = len(idx)
cs = cell._libcint_ctr_coeff(ib)
np, nc = cs.shape
if np1 < np: # no pGTO splitting within the shell
pexp = cell._bas[ib,PTR_EXP]
pcoeff = cell._bas[ib,PTR_COEFF]
cs1 = cs[idx]
cell_dense._env[pcoeff:pcoeff+cs1.size] = cs1.T.ravel()
cell_dense._env[pexp:pexp+np1] = cell.bas_exp(ib)[idx]
cell_dense._bas[ib,NPRIM_OF] = np1
ke_cutoff = max(ke_cutoff, kecuts_pgto[ib][idx].max())
ia = cell.bas_atom(ib)
rcut_atom[ia] = max(rcut_atom[ia], rc[idx].max())
cell_dense._bas = cell_dense._bas[shls_dense]
ao_idx = numpy.hstack([numpy.arange(ao_loc[i], ao_loc[i+1])
for i in shls_dense])
cell_dense.rcut = max(rcut_atom)
return cell_dense, ao_idx, ke_cutoff, rcut_atom
def make_cell_sparse_exp(shls_sparse, r0, r1):
cell_sparse = cell.copy(deep=False)
cell_sparse._bas = cell._bas.copy()
cell_sparse._env = cell._env.copy()
for ib in shls_sparse:
idx = numpy.where(r0 <= rcuts_pgto[ib])[0]
np1 = len(idx)
cs = cell._libcint_ctr_coeff(ib)
np, nc = cs.shape
if np1 < np: # no pGTO splitting within the shell
pexp = cell._bas[ib,PTR_EXP]
pcoeff = cell._bas[ib,PTR_COEFF]
cs1 = cs[idx]
cell_sparse._env[pcoeff:pcoeff+cs1.size] = cs1.T.ravel()
cell_sparse._env[pexp:pexp+np1] = cell.bas_exp(ib)[idx]
cell_sparse._bas[ib,NPRIM_OF] = np1
cell_sparse._bas = cell_sparse._bas[shls_sparse]
ao_idx = numpy.hstack([numpy.arange(ao_loc[i], ao_loc[i+1])
for i in shls_sparse])
return cell_sparse, ao_idx
tasks = []
a = cell.lattice_vectors()
if abs(a-numpy.diag(a.diagonal())).max() < 1e-12:
rmax = a.max() * RMAX_FACTOR_ORTH
else:
rmax = a.max() * RMAX_FACTOR_NONORTH
n_delimeter = int(numpy.log(0.005/rmax) / numpy.log(RMAX_RATIO))
rcut_delimeter = rmax * (RMAX_RATIO ** numpy.arange(n_delimeter))
for r0, r1 in zip(numpy.append(1e9, rcut_delimeter),
numpy.append(rcut_delimeter, 0)):
# shells which have high exps (small rcut)
shls_dense = [ib for ib, rc in enumerate(rcuts_pgto)
if numpy.any((r1 <= rc) & (rc < r0))]
if len(shls_dense) == 0:
continue
cell_dense, ao_idx_dense, ke_cutoff, rcut_atom = \
make_cell_dense_exp(shls_dense, r0, r1)
mesh = tools.cutoff_to_mesh(a, ke_cutoff)
if TO_EVEN_GRIDS:
mesh = (mesh+1)//2 * 2 # to the nearest even number
if numpy.all(mesh >= fft_mesh):
# Including all rest shells
shls_dense = [ib for ib, rc in enumerate(rcuts_pgto)
if numpy.any(rc < r0)]
cell_dense, ao_idx_dense = make_cell_dense_exp(shls_dense, r0, 0)[:2]
cell_dense.mesh = mesh = numpy.min([mesh, fft_mesh], axis=0)
grids_dense = gen_grid.UniformGrids(cell_dense)
grids_dense.ao_idx = ao_idx_dense
#grids_dense.rcuts_pgto = [rcuts_pgto[i] for i in shls_dense]
# shells which have low exps (big rcut)
shls_sparse = [ib for ib, rc in enumerate(rcuts_pgto)
if numpy.any(r0 <= rc)]
if len(shls_sparse) == 0:
cell_sparse = None
ao_idx_sparse = []
else:
cell_sparse, ao_idx_sparse = make_cell_sparse_exp(shls_sparse, r0, r1)
cell_sparse.mesh = mesh
if cell_sparse is None:
grids_sparse = None
else:
grids_sparse = gen_grid.UniformGrids(cell_sparse)
grids_sparse.ao_idx = ao_idx_sparse
log.debug('mesh %s nao dense/sparse %d %d rcut %g',
mesh, len(ao_idx_dense), len(ao_idx_sparse), cell_dense.rcut)
tasks.append([grids_dense, grids_sparse])
if numpy.all(mesh >= fft_mesh):
break
return tasks
[docs]
def multi_grids_tasks_for_ke_cut(cell, fft_mesh=None, verbose=None):
log = logger.new_logger(cell, verbose)
if fft_mesh is None:
fft_mesh = cell.mesh
# Split shells based on rcut
rcuts_pgto, kecuts_pgto = _primitive_gto_cutoff(cell)
ao_loc = cell.ao_loc_nr()
# cell that needs dense integration grids
def make_cell_dense_exp(shls_dense, ke0, ke1):
cell_dense = cell.copy(deep=False)
cell_dense._bas = cell._bas.copy()
cell_dense._env = cell._env.copy()
rcut_atom = [0] * cell.natm
ke_cutoff = 0
for ib in shls_dense:
ke = kecuts_pgto[ib]
idx = numpy.where((ke0 < ke) & (ke <= ke1))[0]
np1 = len(idx)
cs = cell._libcint_ctr_coeff(ib)
np, nc = cs.shape
if np1 < np: # no pGTO splitting within the shell
pexp = cell._bas[ib,PTR_EXP]
pcoeff = cell._bas[ib,PTR_COEFF]
cs1 = cs[idx]
cell_dense._env[pcoeff:pcoeff+cs1.size] = cs1.T.ravel()
cell_dense._env[pexp:pexp+np1] = cell.bas_exp(ib)[idx]
cell_dense._bas[ib,NPRIM_OF] = np1
ke_cutoff = max(ke_cutoff, ke[idx].max())
ia = cell.bas_atom(ib)
rcut_atom[ia] = max(rcut_atom[ia], rcuts_pgto[ib][idx].max())
cell_dense._bas = cell_dense._bas[shls_dense]
ao_idx = numpy.hstack([numpy.arange(ao_loc[i], ao_loc[i+1])
for i in shls_dense])
cell_dense.rcut = max(rcut_atom)
return cell_dense, ao_idx, ke_cutoff, rcut_atom
# cell that needs sparse integration grids
def make_cell_sparse_exp(shls_sparse, ke0, ke1):
cell_sparse = cell.copy(deep=False)
cell_sparse._bas = cell._bas.copy()
cell_sparse._env = cell._env.copy()
for ib in shls_sparse:
idx = numpy.where(kecuts_pgto[ib] <= ke0)[0]
np1 = len(idx)
cs = cell._libcint_ctr_coeff(ib)
np, nc = cs.shape
if np1 < np: # no pGTO splitting within the shell
pexp = cell._bas[ib,PTR_EXP]
pcoeff = cell._bas[ib,PTR_COEFF]
cs1 = cs[idx]
cell_sparse._env[pcoeff:pcoeff+cs1.size] = cs1.T.ravel()
cell_sparse._env[pexp:pexp+np1] = cell.bas_exp(ib)[idx]
cell_sparse._bas[ib,NPRIM_OF] = np1
cell_sparse._bas = cell_sparse._bas[shls_sparse]
ao_idx = numpy.hstack([numpy.arange(ao_loc[i], ao_loc[i+1])
for i in shls_sparse])
return cell_sparse, ao_idx
a = cell.lattice_vectors()
if abs(a-numpy.diag(a.diagonal())).max() < 1e-12:
init_mesh = INIT_MESH_ORTH
else:
init_mesh = INIT_MESH_NONORTH
ke_cutoff_min = tools.mesh_to_cutoff(cell.lattice_vectors(), init_mesh)
ke_cutoff_max = max([ke.max() for ke in kecuts_pgto])
ke1 = ke_cutoff_min.min()
ke_delimeter = [0, ke1]
while ke1 < ke_cutoff_max:
ke1 *= KE_RATIO
ke_delimeter.append(ke1)
tasks = []
for ke0, ke1 in zip(ke_delimeter[:-1], ke_delimeter[1:]):
# shells which have high exps (small rcut)
shls_dense = [ib for ib, ke in enumerate(kecuts_pgto)
if numpy.any((ke0 < ke) & (ke <= ke1))]
if len(shls_dense) == 0:
continue
mesh = tools.cutoff_to_mesh(a, ke1)
if TO_EVEN_GRIDS:
mesh = int((mesh+1)//2) * 2 # to the nearest even number
if numpy.all(mesh >= fft_mesh):
# Including all rest shells
shls_dense = [ib for ib, ke in enumerate(kecuts_pgto)
if numpy.any(ke0 < ke)]
cell_dense, ao_idx_dense = make_cell_dense_exp(shls_dense, ke0,
ke_cutoff_max+1)[:2]
else:
cell_dense, ao_idx_dense, ke_cutoff, rcut_atom = \
make_cell_dense_exp(shls_dense, ke0, ke1)
cell_dense.mesh = mesh = numpy.min([mesh, fft_mesh], axis=0)
grids_dense = gen_grid.UniformGrids(cell_dense)
grids_dense.ao_idx = ao_idx_dense
#grids_dense.rcuts_pgto = [rcuts_pgto[i] for i in shls_dense]
# shells which have low exps (big rcut)
shls_sparse = [ib for ib, ke in enumerate(kecuts_pgto)
if numpy.any(ke <= ke0)]
if len(shls_sparse) == 0:
cell_sparse = None
ao_idx_sparse = []
else:
cell_sparse, ao_idx_sparse = make_cell_sparse_exp(shls_sparse, ke0, ke1)
cell_sparse.mesh = mesh
if cell_sparse is None:
grids_sparse = None
else:
grids_sparse = gen_grid.UniformGrids(cell_sparse)
grids_sparse.ao_idx = ao_idx_sparse
log.debug('mesh %s nao dense/sparse %d %d rcut %g',
mesh, len(ao_idx_dense), len(ao_idx_sparse), cell_dense.rcut)
tasks.append([grids_dense, grids_sparse])
if numpy.all(mesh >= fft_mesh):
break
return tasks
def _primitive_gto_cutoff(cell, precision=None):
'''Cutoff radius, above which each shell decays to a value less than the
required precision'''
if precision is None:
precision = cell.precision
vol = cell.vol
weight_penalty = vol
precision = cell.precision / max(weight_penalty, 1)
omega = cell.omega
rcut = []
ke_cutoff = []
for ib in range(cell.nbas):
l = cell.bas_angular(ib)
es = cell.bas_exp(ib)
cs = abs(cell._libcint_ctr_coeff(ib)).max(axis=1)
norm_ang = ((2*l+1)/(4*numpy.pi))**.5
fac = 2*numpy.pi/vol * cs*norm_ang/es / precision
r = cell.rcut
r = (numpy.log(fac * r**(l+1) + 1.) / es)**.5
r = (numpy.log(fac * r**(l+1) + 1.) / es)**.5
ke_guess = gto.cell._estimate_ke_cutoff(es, l, cs, precision, omega)
rcut.append(r)
ke_cutoff.append(ke_guess)
return rcut, ke_cutoff
[docs]
class MultiGridFFTDF(fft.FFTDF):
_keys = {'tasks'}
def __init__(self, cell, kpts=numpy.zeros((1,3))):
fft.FFTDF.__init__(self, cell, kpts)
self.tasks = None
[docs]
def build(self):
self.tasks = multi_grids_tasks(self.cell, self.mesh, self.verbose)
return self
[docs]
def reset(self, cell=None):
self.tasks = None
return fft.FFTDF.reset(cell)
get_pp = get_pp
get_nuc = get_nuc
[docs]
def get_jk(self, dm, hermi=1, kpts=None, kpts_band=None,
with_j=True, with_k=True, exxdiv='ewald', **kwargs):
from pyscf.pbc.df import fft_jk
if with_k:
logger.warn(self, 'MultiGridFFTDF does not support HFX. '
'HFX is computed by FFTDF.get_k_kpts function.')
if kpts is None:
if numpy.all(self.kpts == 0): # Gamma-point J/K by default
kpts = numpy.zeros(3)
else:
kpts = self.kpts
else:
kpts = numpy.asarray(kpts)
vj = vk = None
if kpts.shape == (3,):
if with_k:
vk = fft_jk.get_jk(self, dm, hermi, kpts, kpts_band,
False, True, exxdiv)[1]
vj = get_j_kpts(self, dm, hermi, kpts.reshape(1,3), kpts_band)
if kpts_band is None:
vj = vj[...,0,:,:]
else:
if with_k:
vk = fft_jk.get_k_kpts(self, dm, hermi, kpts, kpts_band, exxdiv)
if with_j:
vj = get_j_kpts(self, dm, hermi, kpts, kpts_band)
return vj, vk
get_rho = get_rho
[docs]
def multigrid_fftdf(mf):
'''Use MultiGridFFTDF to replace the default FFTDF integration method in
the DFT object.
'''
mf.with_df, old_df = MultiGridFFTDF(mf.cell), mf.with_df
mf.with_df.__dict__.update(old_df.__dict__)
return mf
multigrid = multigrid_fftdf # for backward compatibility
def _pgto_shells(cell):
return cell._bas[:,NPRIM_OF].sum()
