#!/usr/bin/env python
# Copyright 2014-2021 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>
# Timothy Berkelbach <tim.berkelbach@gmail.com>
#
import sys
import json
import ctypes
import warnings
import numpy as np
import scipy.linalg
from scipy.special import erf, erfc
import pyscf.lib.parameters as param
from pyscf import lib
from pyscf.dft import radi
from pyscf.lib import logger
from pyscf.gto import mole
from pyscf.gto import moleintor
from pyscf.gto.mole import conc_env, is_au # noqa
from pyscf.pbc.gto import _pbcintor
from pyscf.pbc.gto.eval_gto import eval_gto as pbc_eval_gto
from pyscf.pbc.tools import pbc as pbctools
from pyscf import __config__
INTEGRAL_PRECISION = getattr(__config__, 'pbc_gto_cell_Cell_precision', 1e-8)
WRAP_AROUND = getattr(__config__, 'pbc_gto_cell_make_kpts_wrap_around', False)
WITH_GAMMA = getattr(__config__, 'pbc_gto_cell_make_kpts_with_gamma', True)
EXP_DELIMITER = getattr(__config__, 'pbc_gto_cell_split_basis_exp_delimiter',
[1.0, 0.5, 0.25, 0.1, 0])
libpbc = _pbcintor.libpbc
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def M(*args, **kwargs):
r'''This is a shortcut to build up Cell object.
Examples:
>>> from pyscf.pbc import gto
>>> cell = gto.M(a=numpy.eye(3)*4, atom='He 1 1 1', basis='6-31g')
'''
cell = Cell()
cell.build(*args, **kwargs)
return cell
C = M
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def pack(cell):
'''Pack the input args of :class:`Cell` to a dict, which can be serialized
with :mod:`pickle`
'''
cldic = mole.pack(cell)
cldic['a'] = cell.a
cldic['precision'] = cell.precision
cldic['ke_cutoff'] = cell.ke_cutoff
cldic['exp_to_discard'] = cell.exp_to_discard
cldic['_mesh'] = cell._mesh
cldic['_rcut'] = cell._rcut
cldic['dimension'] = cell.dimension
cldic['low_dim_ft_type'] = cell.low_dim_ft_type
return cldic
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def unpack(celldic):
'''Convert the packed dict to a :class:`Cell` object, to generate the
input arguments for :class:`Cell` object.
'''
cl = Cell()
cl.__dict__.update(celldic)
return cl
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def dumps(cell):
'''Serialize Cell object to a JSON formatted str.
'''
exclude_keys = set(('output', 'stdout', '_keys', 'symm_orb', 'irrep_id',
'irrep_name', 'lattice_symmetry'))
celldic = dict(cell.__dict__)
for k in exclude_keys:
if k in celldic:
del (celldic[k])
for k in celldic:
if isinstance(celldic[k], np.ndarray):
celldic[k] = celldic[k].tolist()
celldic['atom'] = repr(cell.atom)
celldic['basis']= repr(cell.basis)
celldic['pseudo'] = repr(cell.pseudo)
celldic['ecp'] = repr(cell.ecp)
try:
return json.dumps(celldic)
except TypeError:
def skip_value(dic):
dic1 = {}
for k,v in dic.items():
if (v is None or
isinstance(v, (str, bool, int, float))):
dic1[k] = v
elif isinstance(v, (list, tuple)):
dic1[k] = v # Should I recursively skip_vaule?
elif isinstance(v, set):
dic1[k] = list(v)
elif isinstance(v, dict):
dic1[k] = skip_value(v)
else:
msg =('Function cell.dumps drops attribute %s because '
'it is not JSON-serializable' % k)
warnings.warn(msg)
return dic1
return json.dumps(skip_value(celldic), skipkeys=True)
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def loads(cellstr):
'''Deserialize a str containing a JSON document to a Cell object.
'''
from numpy import array # noqa
celldic = json.loads(cellstr)
cell = Cell()
cell.__dict__.update(celldic)
cell.atom = eval(cell.atom)
cell.basis = eval(cell.basis)
cell.pseudo = eval(cell.pseudo)
cell.ecp = eval(cell.ecp)
cell._atm = np.array(cell._atm, dtype=np.int32)
cell._bas = np.array(cell._bas, dtype=np.int32)
cell._env = np.array(cell._env, dtype=np.double)
cell._ecpbas = np.array(cell._ecpbas, dtype=np.int32)
cell._mesh = np.array(cell._mesh)
# Symmetry class cannot be serialized by dumps function.
# Recreate it manually
if cell.natm > 0 and cell.space_group_symmetry:
cell.build_lattice_symmetry()
return cell
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def conc_cell(cell1, cell2):
'''Concatenate two Cell objects.
'''
mol3 = mole.conc_mol(cell1, cell2)
cell3 = Cell()
cell3.__dict__.update(mol3.__dict__)
# lattice_vectors needs to be consistent with cell3.unit (Bohr)
cell3.a = cell1.lattice_vectors()
cell3.mesh = np.max((cell1.mesh, cell2.mesh), axis=0)
ke_cutoff1 = cell1.ke_cutoff
ke_cutoff2 = cell2.ke_cutoff
if ke_cutoff1 is None and ke_cutoff2 is None:
cell3.ke_cutoff = None
else:
if ke_cutoff1 is None:
ke_cutoff1 = estimate_ke_cutoff(cell1, cell1.precision)
if ke_cutoff2 is None:
ke_cutoff2 = estimate_ke_cutoff(cell2, cell2.precision)
cell3.ke_cutoff = max(ke_cutoff1, ke_cutoff2)
cell3.precision = min(cell1.precision, cell2.precision)
cell3.dimension = max(cell1.dimension, cell2.dimension)
cell3.low_dim_ft_type = cell1.low_dim_ft_type or cell2.low_dim_ft_type
cell3.rcut = max(cell1.rcut, cell2.rcut)
return cell3
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def intor_cross(intor, cell1, cell2, comp=None, hermi=0, kpts=None, kpt=None,
shls_slice=None, **kwargs):
r'''1-electron integrals from two cells like
.. math::
\langle \mu | intor | \nu \rangle, \mu \in cell1, \nu \in cell2
'''
intor, comp = moleintor._get_intor_and_comp(cell1._add_suffix(intor), comp)
if kpts is None:
if kpt is not None:
kpts_lst = np.reshape(kpt, (1,3))
else:
kpts_lst = np.zeros((1,3))
else:
kpts_lst = np.reshape(kpts, (-1,3))
nkpts = len(kpts_lst)
pcell = cell1.copy(deep=False)
pcell.precision = min(cell1.precision, cell2.precision)
pcell._atm, pcell._bas, pcell._env = \
atm, bas, env = conc_env(cell1._atm, cell1._bas, cell1._env,
cell2._atm, cell2._bas, cell2._env)
if shls_slice is None:
shls_slice = (0, cell1.nbas, 0, cell2.nbas)
i0, i1, j0, j1 = shls_slice[:4]
j0 += cell1.nbas
j1 += cell1.nbas
ao_loc = moleintor.make_loc(bas, intor)
ni = ao_loc[i1] - ao_loc[i0]
nj = ao_loc[j1] - ao_loc[j0]
out = np.empty((nkpts,comp,ni,nj), dtype=np.complex128)
if hermi == 0:
aosym = 's1'
else:
aosym = 's2'
fill = getattr(libpbc, 'PBCnr2c_fill_k'+aosym)
fintor = getattr(moleintor.libcgto, intor)
cintopt = lib.c_null_ptr()
rcut = max(cell1.rcut, cell2.rcut)
Ls = cell1.get_lattice_Ls(rcut=rcut)
expkL = np.asarray(np.exp(1j*np.dot(kpts_lst, Ls.T)), order='C')
drv = libpbc.PBCnr2c_drv
kderiv = kwargs.get('kderiv', 0)
if kderiv > 0:
hermi = 0
aosym = 's1'
mat = np.empty((nkpts,(3**kderiv)*comp,ni,nj), dtype=np.complex128)
if kderiv == 1:
fac = 1j * lib.einsum('kl,lx->xkl', expkL, Ls)
elif kderiv == 2:
fac = -lib.einsum('kl,lx,ly->xykl', expkL, Ls, Ls).reshape(-1,nkpts,len(Ls))
else:
raise NotImplementedError
for x in range(fac.shape[0]):
facx = np.asarray(fac[x], order='C')
drv(fintor, fill, out.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(nkpts), ctypes.c_int(comp), ctypes.c_int(len(Ls)),
Ls.ctypes.data_as(ctypes.c_void_p),
facx.ctypes.data_as(ctypes.c_void_p),
(ctypes.c_int*4)(i0, i1, j0, j1),
ao_loc.ctypes.data_as(ctypes.c_void_p), cintopt,
atm.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(pcell.natm),
bas.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(pcell.nbas),
env.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(env.size))
for k, kpt in enumerate(kpts_lst):
v = out[k]
if hermi != 0:
for ic in range(comp):
lib.hermi_triu(v[ic], hermi=hermi, inplace=True)
mat[k, x*comp:(x+1)*comp] = v.copy()
return mat
drv(fintor, fill, out.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(nkpts), ctypes.c_int(comp), ctypes.c_int(len(Ls)),
Ls.ctypes.data_as(ctypes.c_void_p),
expkL.ctypes.data_as(ctypes.c_void_p),
(ctypes.c_int*4)(i0, i1, j0, j1),
ao_loc.ctypes.data_as(ctypes.c_void_p), cintopt,
atm.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(pcell.natm),
bas.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(pcell.nbas),
env.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(env.size))
mat = []
for k, kpt in enumerate(kpts_lst):
v = out[k]
if hermi != 0:
for ic in range(comp):
lib.hermi_triu(v[ic], hermi=hermi, inplace=True)
if comp == 1:
v = v[0]
if abs(kpt).sum() < 1e-9: # gamma_point
v = v.real
mat.append(v)
if kpts is None or np.shape(kpts) == (3,): # A single k-point
mat = mat[0]
return mat
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def get_nimgs(cell, precision=None):
r'''Choose number of basis function images in lattice sums
to include for given precision in overlap, using
precision ~ \int r^l e^{-\alpha r^2} (r-rcut)^l e^{-\alpha (r-rcut)^2}
~ (rcut^2/(2\alpha))^l e^{\alpha/2 rcut^2}
where \alpha is the smallest exponent in the basis. Note
that assumes an isolated exponent in the middle of the box, so
it adds one additional lattice vector to be safe.
'''
if precision is None:
precision = cell.precision
# nimgs determines the supercell size
rcut = estimate_rcut(cell, precision)
nimgs = cell.get_bounding_sphere(rcut)
return nimgs
def _estimate_rcut(alpha, l, c, precision=INTEGRAL_PRECISION):
'''rcut based on the overlap integrals. This estimation is too conservative
in many cases. A possible replacement can be the value of the basis
function at rcut ~ c*r^(l+2)*exp(-alpha*r^2) < precision'''
theta = alpha * .5
a1 = (alpha * 2)**-.5
norm_ang = (2*l+1)/(4*np.pi)
fac = 2*np.pi*c**2*norm_ang / theta / precision
r0 = 20
# The estimation is enough for overlap. Errors are slightly larger for
# kinetic operator. The error in kinetic integrals may be dominant.
# For kinetic operator, basis becomes 2*a*r*|orig-basis>.
# A penalty term 4*a^2*r^2 is included in the estimation.
fac *= 4*alpha**2
r0 = (np.log(fac * r0 * (r0*.5+a1)**(2*l+2) + 1.) / theta)**.5
r0 = (np.log(fac * r0 * (r0*.5+a1)**(2*l+2) + 1.) / theta)**.5
return r0
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def bas_rcut(cell, bas_id, precision=None):
r'''Estimate the largest distance between the function and its image to
reach the precision in overlap
precision ~ \int g(r-0) g(r-Rcut)
'''
if precision is None:
precision = cell.precision
l = cell.bas_angular(bas_id)
es = cell.bas_exp(bas_id)
cs = abs(cell._libcint_ctr_coeff(bas_id)).max(axis=1)
rcut = _estimate_rcut(es, l, cs, precision)
return rcut.max()
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def estimate_rcut(cell, precision=None):
'''Lattice-sum cutoff for the entire system'''
if cell.nbas == 0:
return 0.01
if precision is None:
precision = cell.precision
exps, cs = _extract_pgto_params(cell, 'min')
ls = cell._bas[:,mole.ANG_OF]
rcut = _estimate_rcut(exps, ls, cs, precision)
return rcut.max()
def _estimate_ke_cutoff(alpha, l, c, precision=INTEGRAL_PRECISION, omega=0):
'''Energy cutoff estimation for nuclear attraction integrals'''
norm_ang = (2*l+1)/(4*np.pi)
fac = 32*np.pi**2*(2*np.pi)**1.5 * c**2*norm_ang / (2*alpha)**(2*l+.5) / precision
Ecut = 20.
if omega <= 0:
Ecut = np.log(fac * (Ecut*2)**(l-.5) + 1.) * 4*alpha
Ecut = np.log(fac * (Ecut*2)**(l-.5) + 1.) * 4*alpha
else:
theta = 1./(1./(4*alpha) + 1./(2*omega**2))
Ecut = np.log(fac * (Ecut*2)**(l-.5) + 1.) * theta
Ecut = np.log(fac * (Ecut*2)**(l-.5) + 1.) * theta
return Ecut
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def estimate_ke_cutoff(cell, precision=None):
'''Energy cutoff estimation for nuclear attraction integrals'''
if cell.nbas == 0:
return 0.
if precision is None:
precision = cell.precision
#precision /= cell.atom_charges().sum()
exps, cs = _extract_pgto_params(cell, 'max')
ls = cell._bas[:,mole.ANG_OF]
Ecut = _estimate_ke_cutoff(exps, ls, cs, precision, cell.omega)
return Ecut.max()
def _extract_pgto_params(cell, op='min'):
'''A helper function for estimate_xxx function'''
es = []
cs = []
if op == 'min':
for i in range(cell.nbas):
e = cell.bas_exp(i)
c = cell._libcint_ctr_coeff(i)
idx = e.argmin()
es.append(e[idx])
cs.append(abs(c[idx]).max())
else:
for i in range(cell.nbas):
e = cell.bas_exp(i)
c = cell._libcint_ctr_coeff(i)
idx = e.argmax()
es.append(e[idx])
cs.append(abs(c[idx]).max())
return np.array(es), np.array(cs)
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def error_for_ke_cutoff(cell, ke_cutoff, omega=None):
'''Error estimation based on nuclear attraction integrals'''
if omega is None:
omega = cell.omega
exps, cs = _extract_pgto_params(cell, 'max')
ls = cell._bas[:,mole.ANG_OF]
norm_ang = (2*ls+1)/(4*np.pi)
fac = 32*np.pi**2*(2*np.pi)**1.5 * cs**2*norm_ang / (2*exps)**(2*ls+.5)
if omega <= 0:
err = fac * (2*ke_cutoff)**(ls-.5) * np.exp(-ke_cutoff/(4*exps))
else:
theta = 1./(1./(4*exps) + 1./(2*omega**2))
err = fac * (2*ke_cutoff)**(ls-.5) * np.exp(-ke_cutoff/theta)
return err.max()
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def get_bounding_sphere(cell, rcut):
'''Finds all the lattice points within a sphere of radius rcut.
Defines a parallelipiped given by -N_x <= n_x <= N_x, with x in [1,3]
See Martin p. 85
Args:
rcut : number
real space cut-off for interaction
Returns:
cut : ndarray of 3 ints defining N_x
'''
#Gmat = cell.reciprocal_vectors(norm_to=1)
#n1 = np.ceil(lib.norm(Gmat[0,:])*rcut)
#n2 = np.ceil(lib.norm(Gmat[1,:])*rcut)
#n3 = np.ceil(lib.norm(Gmat[2,:])*rcut)
#cut = np.array([n1, n2, n3]).astype(int)
b = cell.reciprocal_vectors(norm_to=1)
heights_inv = lib.norm(b, axis=1)
nimgs = np.ceil(rcut*heights_inv).astype(int)
for i in range(cell.dimension, 3):
nimgs[i] = 1
return nimgs
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def get_Gv(cell, mesh=None, **kwargs):
'''Calculate three-dimensional G-vectors for the cell; see MH (3.8).
Indices along each direction go as [0...N-1, -N...-1] to follow FFT convention.
Args:
cell : instance of :class:`Cell`
Returns:
Gv : (ngrids, 3) ndarray of floats
The array of G-vectors.
'''
return get_Gv_weights(cell, mesh, **kwargs)[0]
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def get_Gv_weights(cell, mesh=None, **kwargs):
'''Calculate G-vectors and weights.
Returns:
Gv : (ngris, 3) ndarray of floats
The array of G-vectors.
'''
if mesh is None:
mesh = cell.mesh
if 'gs' in kwargs:
warnings.warn('cell.gs is deprecated. It is replaced by cell.mesh,'
'the number of PWs (=2*gs+1) along each direction.')
mesh = [2*n+1 for n in kwargs['gs']]
# Default, the 3D uniform grids
rx = np.fft.fftfreq(mesh[0], 1./mesh[0])
ry = np.fft.fftfreq(mesh[1], 1./mesh[1])
rz = np.fft.fftfreq(mesh[2], 1./mesh[2])
b = cell.reciprocal_vectors()
weights = abs(np.linalg.det(b))
if (cell.dimension < 2 or
(cell.dimension == 2 and cell.low_dim_ft_type == 'inf_vacuum')):
if cell.dimension == 0:
rx, wx = _non_uniform_Gv_base(mesh[0]//2)
ry, wy = _non_uniform_Gv_base(mesh[1]//2)
rz, wz = _non_uniform_Gv_base(mesh[2]//2)
rx /= np.linalg.norm(b[0])
ry /= np.linalg.norm(b[1])
rz /= np.linalg.norm(b[2])
weights = np.einsum('i,j,k->ijk', wx, wy, wz).reshape(-1)
elif cell.dimension == 1:
wx = np.repeat(np.linalg.norm(b[0]), mesh[0])
ry, wy = _non_uniform_Gv_base(mesh[1]//2)
rz, wz = _non_uniform_Gv_base(mesh[2]//2)
ry /= np.linalg.norm(b[1])
rz /= np.linalg.norm(b[2])
weights = np.einsum('i,j,k->ijk', wx, wy, wz).reshape(-1)
elif cell.dimension == 2:
area = np.linalg.norm(np.cross(b[0], b[1]))
wxy = np.repeat(area, mesh[0]*mesh[1])
rz, wz = _non_uniform_Gv_base(mesh[2]//2)
rz /= np.linalg.norm(b[2])
weights = np.einsum('i,k->ik', wxy, wz).reshape(-1)
Gvbase = (rx, ry, rz)
Gv = np.dot(lib.cartesian_prod(Gvbase), b)
# 1/cell.vol == det(b)/(2pi)^3
weights *= 1/(2*np.pi)**3
return Gv, Gvbase, weights
def _non_uniform_Gv_base(n):
#rs, ws = radi.delley(n)
#rs, ws = radi.treutler_ahlrichs(n)
#rs, ws = radi.mura_knowles(n)
rs, ws = radi.gauss_chebyshev(n)
#return np.hstack((0,rs,-rs[::-1])), np.hstack((0,ws,ws[::-1]))
return np.hstack((rs,-rs[::-1])), np.hstack((ws,ws[::-1]))
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def get_SI(cell, Gv=None, mesh=None):
'''Calculate the structure factor (0D, 1D, 2D, 3D) for all atoms; see MH (3.34).
Args:
cell : instance of :class:`Cell`
Gv : (N,3) array
G vectors
Returns:
SI : (natm, ngrids) ndarray, dtype=np.complex128
The structure factor for each atom at each G-vector.
'''
coords = cell.atom_coords()
if Gv is None:
if mesh is None:
mesh = cell.mesh
basex, basey, basez = cell.get_Gv_weights(mesh)[1]
b = cell.reciprocal_vectors()
rb = np.dot(coords, b.T)
SIx = np.exp(-1j*np.einsum('z,g->zg', rb[:,0], basex))
SIy = np.exp(-1j*np.einsum('z,g->zg', rb[:,1], basey))
SIz = np.exp(-1j*np.einsum('z,g->zg', rb[:,2], basez))
SI = SIx[:,:,None,None] * SIy[:,None,:,None] * SIz[:,None,None,:]
natm = coords.shape[0]
SI = SI.reshape(natm, -1)
else:
SI = np.exp(-1j*np.dot(coords, Gv.T))
return SI
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def get_ewald_params(cell, precision=None, mesh=None):
r'''Choose a reasonable value of Ewald 'eta' and 'cut' parameters.
eta^2 is the exponent coefficient of the model Gaussian charge for nucleus
at R: \frac{eta^3}{pi^1.5} e^{-eta^2 (r-R)^2}
Choice is based on largest G vector and desired relative precision.
The relative error in the G-space sum is given by
precision ~ 4\pi Gmax^2 e^{(-Gmax^2)/(4 \eta^2)}
which determines eta. Then, real-space cutoff is determined by (exp.
factors only)
precision ~ erfc(eta*rcut) / rcut ~ e^{(-eta**2 rcut*2)}
Returns:
ew_eta, ew_cut : float
The Ewald 'eta' and 'cut' parameters.
'''
if cell.natm == 0:
return 0, 0
if precision is None:
precision = cell.precision
if (cell.dimension < 2 or
(cell.dimension == 2 and cell.low_dim_ft_type == 'inf_vacuum')):
# Non-uniform PW grids are used for low-dimensional ewald summation. The cutoff
# estimation for long range part based on exp(G^2/(4*eta^2)) does not work for
# non-uniform grids. Smooth model density is preferred.
ew_cut = cell.rcut
ew_eta = np.sqrt(max(np.log(4*np.pi*ew_cut**2/precision)/ew_cut**2, .1))
elif cell.dimension == 2:
a = cell.lattice_vectors()
ew_cut = a[2,2] / 2
# ewovrl ~ erfc(eta*rcut) / rcut ~ e^{(-eta**2 rcut*2)} < precision
log_precision = np.log(precision / (cell.atom_charges().sum()*16*np.pi**2))
ew_eta = (-log_precision)**.5 / ew_cut
else: # dimension == 3
ew_eta = 1./cell.vol**(1./6)
ew_cut = _estimate_rcut(ew_eta**2, 0, 1., precision)
return ew_eta, ew_cut
[docs]
def ewald(cell, ew_eta=None, ew_cut=None):
'''Perform real (R) and reciprocal (G) space Ewald sum for the energy.
Formulation of Martin, App. F2.
Returns:
float
The Ewald energy consisting of overlap, self, and G-space sum.
See Also:
pyscf.pbc.gto.get_ewald_params
'''
# If lattice parameter is not set, the cell object is treated as a mole
# object. The nuclear repulsion energy is computed.
if cell.a is None:
return mole.energy_nuc(cell)
if cell.natm == 0:
return 0
chargs = cell.atom_charges()
if ew_eta is None or ew_cut is None:
ew_eta, ew_cut = cell.get_ewald_params()
log_precision = np.log(cell.precision / (chargs.sum()*16*np.pi**2))
ke_cutoff = -2*ew_eta**2*log_precision
mesh = cell.cutoff_to_mesh(ke_cutoff)
logger.debug1(cell, 'mesh for ewald %s', mesh)
coords = cell.atom_coords()
Lall = cell.get_lattice_Ls(rcut=ew_cut)
rLij = coords[:,None,:] - coords[None,:,:] + Lall[:,None,None,:]
r = np.sqrt(np.einsum('Lijx,Lijx->Lij', rLij, rLij))
rLij = None
r[r<1e-16] = 1e200
ewovrl = .5 * np.einsum('i,j,Lij->', chargs, chargs, erfc(ew_eta * r) / r)
# last line of Eq. (F.5) in Martin
ewself = -.5 * np.dot(chargs,chargs) * 2 * ew_eta / np.sqrt(np.pi)
if cell.dimension == 3:
ewself += -.5 * np.sum(chargs)**2 * np.pi/(ew_eta**2 * cell.vol)
# g-space sum (using g grid) (Eq. (F.6) in Martin, but note errors as below)
# Eq. (F.6) in Martin is off by a factor of 2, the
# exponent is wrong (8->4) and the square is in the wrong place
#
# Formula should be
# 1/2 * 4\pi / Omega \sum_I \sum_{G\neq 0} |ZS_I(G)|^2 \exp[-|G|^2/4\eta^2]
# where
# ZS_I(G) = \sum_a Z_a exp (i G.R_a)
Gv, Gvbase, weights = cell.get_Gv_weights(mesh)
absG2 = np.einsum('gi,gi->g', Gv, Gv)
absG2[absG2==0] = 1e200
if cell.dimension != 2 or cell.low_dim_ft_type == 'inf_vacuum':
# TODO: truncated Coulomb for 0D. The non-uniform grids for inf-vacuum
# have relatively large error
coulG = 4*np.pi / absG2
coulG *= weights
ZSI = np.einsum("i,ij->j", chargs, cell.get_SI(Gv))
ZexpG2 = ZSI * np.exp(-absG2/(4*ew_eta**2))
ewg = .5 * np.einsum('i,i,i', ZSI.conj(), ZexpG2, coulG).real
elif cell.dimension == 2: # Truncated Coulomb
# The following 2D ewald summation is taken from:
# R. Sundararaman and T. Arias PRB 87, 2013
def fn(eta,Gnorm,z):
Gnorm_z = Gnorm*z
large_idx = Gnorm_z > 20.0
ret = np.zeros_like(Gnorm_z)
x = Gnorm/2./eta + eta*z
with np.errstate(over='ignore'):
erfcx = erfc(x)
ret[~large_idx] = np.exp(Gnorm_z[~large_idx]) * erfcx[~large_idx]
ret[ large_idx] = np.exp((Gnorm*z-x**2)[large_idx]) * erfcx[large_idx]
return ret
def gn(eta,Gnorm,z):
return np.pi/Gnorm*(fn(eta,Gnorm,z) + fn(eta,Gnorm,-z))
def gn0(eta,z):
return -2*np.pi*(z*erf(eta*z) + np.exp(-(eta*z)**2)/eta/np.sqrt(np.pi))
b = cell.reciprocal_vectors()
inv_area = np.linalg.norm(np.cross(b[0], b[1]))/(2*np.pi)**2
# Perform the reciprocal space summation over all reciprocal vectors
# within the x,y plane.
planarG2_idx = np.logical_and(Gv[:,2] == 0, absG2 > 0.0)
Gv = Gv[planarG2_idx]
absG2 = absG2[planarG2_idx]
absG = absG2**(0.5)
# Performing the G != 0 summation.
rij = coords[:,None,:] - coords[None,:,:]
Gdotr = np.einsum('ijx,gx->ijg', rij, Gv)
ewg = np.einsum('i,j,ijg,ijg->', chargs, chargs, np.cos(Gdotr),
gn(ew_eta,absG,rij[:,:,2:3]))
# Performing the G == 0 summation.
ewg += np.einsum('i,j,ij->', chargs, chargs, gn0(ew_eta,rij[:,:,2]))
ewg *= inv_area*0.5
else:
logger.warn(cell, 'No method for PBC dimension %s, dim-type %s.'
' cell.low_dim_ft_type="inf_vacuum" should be set.',
cell.dimension, cell.low_dim_ft_type)
raise NotImplementedError
logger.debug(cell, 'Ewald components = %.15g, %.15g, %.15g', ewovrl, ewself, ewg)
return ewovrl + ewself + ewg
energy_nuc = ewald
[docs]
def make_kpts(cell, nks, wrap_around=WRAP_AROUND, with_gamma_point=WITH_GAMMA,
scaled_center=None,
space_group_symmetry=False, time_reversal_symmetry=False,
**kwargs):
'''Given number of kpoints along x,y,z , generate kpoints
Args:
nks : (3,) ndarray
Kwargs:
wrap_around : bool
To ensure all kpts are in first Brillouin zone.
with_gamma_point : bool
Whether to shift Monkhorst-pack grid to include gamma-point.
scaled_center : (3,) array
Shift all points in the Monkhorst-pack grid to be centered on
scaled_center, given as the zeroth index of the returned kpts.
Scaled meaning that the k-points are scaled to a grid from
[-1,1] x [-1,1] x [-1,1]
space_group_symmetry : bool
Whether to consider space group symmetry
time_reversal_symmetry : bool
Whether to consider time reversal symmetry
Returns:
kpts in absolute value (unit 1/Bohr). Gamma point is placed at the
first place in the k-points list;
instance of :class:`KPoints` if k-point symmetry is considered
Examples:
>>> cell.make_kpts((4,4,4))
'''
ks_each_axis = []
for n in nks:
if with_gamma_point or scaled_center is not None:
ks = np.arange(n, dtype=float) / n
else:
ks = (np.arange(n)+.5)/n-.5
if wrap_around:
ks[ks>=.5] -= 1
ks_each_axis.append(ks)
if scaled_center is None:
scaled_center = [0.0,0.0,0.0]
scaled_kpts = lib.cartesian_prod(ks_each_axis)
scaled_kpts += np.array(scaled_center)
kpts = cell.get_abs_kpts(scaled_kpts)
if space_group_symmetry or time_reversal_symmetry:
from pyscf.pbc.lib import kpts as libkpts
if space_group_symmetry and not cell.space_group_symmetry:
raise RuntimeError('Using k-point symmetry now requires cell '
'to be built with space group symmetry info:\n'
'cell.space_group_symmetry = True\n'
'cell.symmorphic = False\n'
'cell.build()')
kpts = libkpts.make_kpts(cell, kpts, space_group_symmetry,
time_reversal_symmetry)
return kpts
gen_uniform_grids = get_uniform_grids
def _split_basis(cell, delimiter=EXP_DELIMITER):
'''
Split the contracted basis to small segmant. The new basis has more
shells. Each shell has less primitive basis and thus is more local.
'''
_bas = []
_env = cell._env.copy()
contr_coeff = []
for ib in range(cell.nbas):
pexp = cell._bas[ib,mole.PTR_EXP]
pcoeff1 = cell._bas[ib,mole.PTR_COEFF]
nc = cell.bas_nctr(ib)
es = cell.bas_exp(ib)
cs = cell._libcint_ctr_coeff(ib)
l = cell.bas_angular(ib)
if cell.cart:
degen = (l + 1) * (l + 2) // 2
else:
degen = l * 2 + 1
mask = np.ones(es.size, dtype=bool)
count = 0
for thr in delimiter:
idx = np.where(mask & (es >= thr))[0]
np1 = len(idx)
if np1 > 0:
pcoeff0, pcoeff1 = pcoeff1, pcoeff1 + np1 * nc
cs1 = cs[idx]
_env[pcoeff0:pcoeff1] = cs1.T.ravel()
btemp = cell._bas[ib].copy()
btemp[mole.NPRIM_OF] = np1
btemp[mole.PTR_COEFF] = pcoeff0
btemp[mole.PTR_EXP] = pexp
_bas.append(btemp)
mask[idx] = False
pexp += np1
count += 1
contr_coeff.append(np.vstack([np.eye(degen*nc)] * count))
pcell = cell.copy(deep=False)
pcell._bas = np.asarray(np.vstack(_bas), dtype=np.int32)
pcell._env = _env
return pcell, scipy.linalg.block_diag(*contr_coeff)
[docs]
def tot_electrons(cell, nkpts=1):
'''Total number of electrons
'''
if cell._nelectron is None:
nelectron = cell.atom_charges().sum() * nkpts - cell.charge
else: # Custom cell.nelectron stands for num. electrons per cell
nelectron = cell._nelectron * nkpts
# Round off to the nearest integer
nelectron = int(nelectron+0.5)
return nelectron
def _mesh_inf_vaccum(cell):
#prec ~ exp(-0.436392335*mesh -2.99944305)*nelec
meshz = (np.log(cell.nelectron/cell.precision)-2.99944305)/0.436392335
# meshz has to be even number due to the symmetry on z+ and z-
return int(meshz*.5 + .999) * 2
[docs]
class Cell(mole.MoleBase):
'''A Cell object holds the basic information of a crystal.
Attributes:
a : (3,3) ndarray
Lattice primitive vectors. Each row represents a lattice vector
Reciprocal lattice vectors are given by b1,b2,b3 = 2 pi inv(a).T
mesh : (3,) list of ints
The number G-vectors along each direction.
The default value is estimated based on :attr:`precision`
pseudo : dict or str
To define pseudopotential.
precision : float
To control Ewald sums and lattice sums accuracy
rcut : float
Cutoff radius (unit Bohr) in lattice summation. The default value
is estimated based on the required :attr:`precision`.
ke_cutoff : float
If set, defines a spherical cutoff of planewaves, with .5 * G**2 < ke_cutoff
The default value is estimated based on :attr:`precision`
dimension : int
Periodic dimensions. Default is 3
low_dim_ft_type : str
For semi-empirical periodic systems, whether to calculate
integrals at the non-PBC dimension using the sampled mesh grids in
infinity vacuum (inf_vacuum) or truncated Coulomb potential
(analytic_2d_1). Unless explicitly specified, analytic_2d_1 is
used for 2D system and inf_vacuum is assumed for 1D and 0D.
space_group_symmetry : bool
Whether to consider space group symmetry. Default is False.
symmorphic : bool
Whether the lattice is symmorphic. If set to True, even if the
lattice is non-symmorphic, only symmorphic space group symmetry
will be considered. Default is False, meaning the space group is
determined by the lattice symmetry to be symmorphic or non-symmorphic.
lattice_symmetry : None or :class:`pbc.symm.Symmetry` instance
The object containing the lattice symmetry information. Default is None.
(See other attributes in :class:`Mole`)
Examples:
>>> mol = Mole(atom='H^2 0 0 0; H 0 0 1.1', basis='sto3g')
>>> cl = Cell()
>>> cl.build(a='3 0 0; 0 3 0; 0 0 3', atom='C 1 1 1', basis='sto3g')
>>> print(cl.atom_symbol(0))
C
'''
precision = getattr(__config__, 'pbc_gto_cell_Cell_precision', 1e-8)
exp_to_discard = getattr(__config__, 'pbc_gto_cell_Cell_exp_to_discard', None)
_keys = {
'precision', 'exp_to_discard',
'a', 'ke_cutoff', 'pseudo', 'dimension', 'low_dim_ft_type',
'space_group_symmetry', 'symmorphic', 'lattice_symmetry', 'mesh', 'rcut',
}
def __init__(self, **kwargs):
mole.MoleBase.__init__(self)
self.a = None # lattice vectors, (a1,a2,a3)
# if set, defines a spherical cutoff
# of fourier components, with .5 * G**2 < ke_cutoff
self.ke_cutoff = None
self.dimension = 3
# TODO: Simple hack for now; the implementation of ewald depends on the
# density-fitting class. This determines how the ewald produces
# its energy.
self.low_dim_ft_type = None
self.space_group_symmetry = False
self.symmorphic = False
self.lattice_symmetry = None
##################################################
# These attributes are initialized by build function if not specified
self.mesh = None
self.rcut = None
for key, val in kwargs.items():
setattr(self, key, val)
@property
def mesh(self):
return self._mesh
@mesh.setter
def mesh(self, x):
self._mesh = x
self._mesh_from_build = False
@property
def rcut(self):
return self._rcut
@rcut.setter
def rcut(self, x):
self._rcut = x
self._rcut_from_build = False
@property
def ew_eta(self):
warnings.warn("cell.ew_eta is deprecated. Use function get_ewald_params instead.")
return self.get_ewald_params()[0]
@property
def ew_cut(self):
warnings.warn("cell.ew_cut is deprecated. Use function get_ewald_params instead.")
return self.get_ewald_params()[1]
@ew_eta.setter
def ew_eta(self, val):
warnings.warn("ew_eta is no longer stored in the cell object. Setting it has no effect")
@ew_cut.setter
def ew_cut(self, val):
warnings.warn("ew_cut is no longer stored in the cell object. Setting it has no effect")
@property
def nelec(self):
ne = self.nelectron
nalpha = (ne + self.spin) // 2
nbeta = nalpha - self.spin
# nelec method defined in Mole class raises error when the attributes .spin
# and .nelectron are inconsistent. In PBC, when the system has even number of
# k-points, it is valid that .spin is odd while .nelectron is even.
if nalpha + nbeta != ne:
warnings.warn('Electron number %d and spin %d are not consistent '
'in cell\n' % (ne, self.spin))
return nalpha, nbeta
def __getattr__(self, key):
'''To support accessing methods (cell.HF, cell.KKS, cell.KUCCSD, ...)
from Cell object.
'''
if key[0] == '_': # Skip private attributes and Python builtins
raise AttributeError('Cell object does not have attribute %s' % key)
elif key in ('_ipython_canary_method_should_not_exist_',
'_repr_mimebundle_'):
# https://github.com/mewwts/addict/issues/26
# https://github.com/jupyter/notebook/issues/2014
raise AttributeError
# Import all available modules. Some methods are registered to other
# classes/modules when importing modules in __all__.
from pyscf.pbc import __all__ # noqa
from pyscf.pbc import scf, dft
from pyscf.dft import XC
for mod in (scf, dft):
method = getattr(mod, key, None)
if callable(method):
return method(self)
if key[0] == 'K': # with k-point sampling
if 'TD' in key[:4]:
if key in ('KTDHF', 'KTDA'):
mf = scf.KHF(self)
else:
mf = dft.KKS(self)
xc = key.split('TD', 1)[1]
if xc in XC:
mf.xc = xc
key = 'KTDDFT'
else:
mf = scf.KHF(self)
# Remove prefix 'K' because methods are registered without the leading 'K'
key = key[1:]
else:
if 'TD' in key[:3]:
if key in ('TDHF', 'TDA'):
mf = scf.HF(self)
else:
mf = dft.KS(self)
xc = key.split('TD', 1)[1]
if xc in XC:
mf.xc = xc
key = 'TDDFT'
else:
mf = scf.HF(self)
method = getattr(mf, key)
if self.nelectron != 0:
mf.run()
return method
tot_electrons = tot_electrons
def _build_symmetry(self, kpts=None, **kwargs):
'''Construct symmetry adapted crystalline atomic orbitals
'''
from pyscf.pbc.lib.kpts import KPoints
from pyscf.pbc.symm.basis import symm_adapted_basis
if kpts is None:
return mole.Mole._build_symmetry(self)
elif isinstance(kpts, KPoints):
self.symm_orb, self.irrep_id = symm_adapted_basis(self, kpts)
return self
else:
raise RuntimeError('Symmetry information not found in kpts. '
'kpts needs to be initialized as a KPoints object.')
[docs]
def symmetrize_mesh(self, mesh=None):
if mesh is None:
mesh = self.mesh
if not self.space_group_symmetry:
return mesh
_, mesh1 = self.lattice_symmetry.check_mesh_symmetry(mesh=mesh,
return_mesh=True)
if np.prod(mesh1) != np.prod(mesh):
logger.debug(self, 'mesh %s is symmetrized as %s', mesh, mesh1)
return mesh1
[docs]
def build_lattice_symmetry(self, check_mesh_symmetry=True):
'''Build cell.lattice_symmetry object.
Kwargs:
check_mesh_symmetry : bool
For nonsymmorphic symmetry groups, `cell.mesh` may have
lower symmetry than the lattice. In this case, if
`check_mesh_symmetry` is `True`, the lower symmetry group will
be used. Otherwise, if `check_mesh_symmetry` is `False`,
the mesh grid will be modified to satisfy the higher symmetry.
Default value is `True`.
Note:
This function modifies the attributes of `cell`.
'''
from pyscf.pbc.symm import Symmetry
self.lattice_symmetry = Symmetry(self).build(
space_group_symmetry=True,
symmorphic=self.symmorphic,
check_mesh_symmetry=check_mesh_symmetry)
if not check_mesh_symmetry:
_mesh_from_build = self._mesh_from_build
self.mesh = self.symmetrize_mesh()
self._mesh_from_build = _mesh_from_build
return self
#Note: Exculde dump_input, parse_arg, basis from kwargs to avoid parsing twice
[docs]
def build(self, dump_input=True, parse_arg=mole.ARGPARSE,
a=None, mesh=None, ke_cutoff=None, precision=None, nimgs=None,
h=None, dimension=None, rcut= None, low_dim_ft_type=None,
space_group_symmetry=None, symmorphic=None, *args, **kwargs):
'''Setup Mole molecule and Cell and initialize some control parameters.
Whenever you change the value of the attributes of :class:`Cell`,
you need call this function to refresh the internal data of Cell.
Kwargs:
a : (3,3) ndarray
The real-space cell lattice vectors. Each row represents
a lattice vector.
mesh : (3,) ndarray of ints
The number of *positive* G-vectors along each direction.
ke_cutoff : float
If set, defines a spherical cutoff of planewaves, with .5 * G**2 < ke_cutoff
The default value is estimated based on :attr:`precision`
precision : float
To control Ewald sums and lattice sums accuracy
nimgs : (3,) ndarray of ints
Number of repeated images in lattice summation to produce
periodicity. This value can be estimated based on the required
precision. It's recommended NOT making changes to this value.
rcut : float
Cutoff radius (unit Bohr) in lattice summation to produce
periodicity. The value can be estimated based on the required
precision. It's recommended NOT making changes to this value.
h : (3,3) ndarray
a.T. Deprecated
dimension : int
Default is 3
low_dim_ft_type : str
For semi-empirical periodic systems, whether to calculate
integrals at the non-PBC dimension using the sampled mesh grids in
infinity vacuum (inf_vacuum) or truncated Coulomb potential
(analytic_2d_1). Unless explicitly specified, analytic_2d_1 is
used for 2D system and inf_vacuum is assumed for 1D and 0D.
space_group_symmetry : bool
Whether to consider space group symmetry. Default is False.
symmorphic : bool
Whether the lattice is symmorphic. If set to True, even if the
lattice is non-symmorphic, only symmorphic space group symmetry
will be considered. Default is False, meaning the space group is
determined by the lattice symmetry to be symmorphic or non-symmorphic.
'''
if h is not None: self.h = h
if a is not None: self.a = a
if mesh is not None: self.mesh = mesh
if nimgs is not None: self.nimgs = nimgs
if dimension is not None: self.dimension = dimension
if precision is not None: self.precision = precision
if rcut is not None: self.rcut = rcut
if ke_cutoff is not None: self.ke_cutoff = ke_cutoff
if low_dim_ft_type is not None: self.low_dim_ft_type = low_dim_ft_type
if space_group_symmetry is not None:
self.space_group_symmetry = space_group_symmetry
if symmorphic is not None:
self.symmorphic = symmorphic
dump_input = dump_input and not self._built and self.verbose > logger.NOTE
mole.MoleBase.build(self, dump_input, parse_arg, *args, **kwargs)
exp_min = np.array([self.bas_exp(ib).min() for ib in range(self.nbas)])
if self.exp_to_discard is None:
if np.any(exp_min < 0.1):
sys.stderr.write('''WARNING!
Very diffused basis functions are found in the basis set. They may lead to severe
linear dependence and numerical instability. You can set cell.exp_to_discard=0.1
to remove the diffused Gaussians whose exponents are less than 0.1.\n\n''')
elif np.any(exp_min < self.exp_to_discard):
# Discard functions of small exponents in basis
_basis = {}
for symb, basis_now in self._basis.items():
basis_add = []
for b in basis_now:
l = b[0]
if isinstance(b[1], int):
kappa = b[1]
b_coeff = np.array(b[2:])
else:
kappa = 0
b_coeff = np.array(b[1:])
es = b_coeff[:,0]
if np.any(es < self.exp_to_discard):
b_coeff = b_coeff[es>=self.exp_to_discard]
# contraction coefficients may be completely zero after removing one primitive
# basis. Removing the zero-coefficient basis.
b_coeff = b_coeff[:,np.all(b_coeff!=0, axis=0)]
if b_coeff.size > 0:
if kappa == 0:
basis_add.append([l] + b_coeff.tolist())
else:
basis_add.append([l,kappa] + b_coeff.tolist())
else:
basis_add.append(b)
_basis[symb] = basis_add
self._basis = _basis
steep_shls = []
nprim_drop = 0
nctr_drop = 0
_env = self._env.copy()
for ib in range(len(self._bas)):
l = self.bas_angular(ib)
nprim = self.bas_nprim(ib)
nc = self.bas_nctr(ib)
es = self.bas_exp(ib)
ptr = self._bas[ib,mole.PTR_COEFF]
cs = self._env[ptr:ptr+nprim*nc].reshape(nc,nprim).T
if np.any(es < self.exp_to_discard):
cs = cs[es>=self.exp_to_discard]
es = es[es>=self.exp_to_discard]
nprim_old, nc_old = nprim, nc
# contraction coefficients may be completely zero after removing one primitive
# basis. Removing the zero-coefficient basis.
cs = cs[:,np.all(cs!=0, axis=0)]
nprim, nc = cs.shape
self._bas[ib,mole.NPRIM_OF] = nprim
self._bas[ib,mole.NCTR_OF] = nc
nprim_drop = nprim_old - nprim + nprim_drop
nctr_drop = nc_old - nc + nctr_drop
if cs.size > 0:
pe = self._bas[ib,mole.PTR_EXP]
_env[pe:pe+nprim] = es
cs = mole._nomalize_contracted_ao(l, es, cs)
_env[ptr:ptr+nprim*nc] = cs.T.reshape(-1)
if nprim > 0:
steep_shls.append(ib)
self._env = _env
self._bas = np.asarray(self._bas[steep_shls], order='C')
logger.info(self, 'Discarded %d diffused primitive functions, '
'%d contracted functions', nprim_drop, nctr_drop)
#logger.debug1(self, 'Old shells %s', steep_shls)
# The rest initialization requires lattice parameters. If .a is not
# set, pass the rest initialization.
if self.a is None:
return self
if self.rcut is None or self._rcut_from_build:
self._rcut = estimate_rcut(self, self.precision)
self._rcut_from_build = True
_a = self.lattice_vectors()
if np.linalg.det(_a) < 0:
sys.stderr.write('''WARNING!
Lattice are not in right-handed coordinate system. This can cause wrong value for some integrals.
It's recommended to resort the lattice vectors to\na = %s\n\n''' % _a[[0,2,1]])
if self.dimension == 2 and self.low_dim_ft_type != 'inf_vacuum':
# check vacuum size. See Fig 1 of PRB, 73, 2015119
#Lz_guess = self.rcut*(1+np.sqrt(2))
Lz_guess = self.rcut * 2
if np.linalg.norm(_a[2]) < 0.7 * Lz_guess:
sys.stderr.write('''WARNING!
Size of vacuum may not be enough. The recommended vacuum size is %s AA (%s Bohr)\n\n'''
% (Lz_guess*param.BOHR, Lz_guess))
if self.mesh is None or self._mesh_from_build:
if self.ke_cutoff is None:
ke_cutoff = estimate_ke_cutoff(self, self.precision)
else:
ke_cutoff = self.ke_cutoff
self._mesh = pbctools.cutoff_to_mesh(_a, ke_cutoff)
if (self.dimension < 2 or
(self.dimension == 2 and self.low_dim_ft_type == 'inf_vacuum')):
self._mesh[self.dimension:] = _mesh_inf_vaccum(self)
self._mesh_from_build = True
if self.space_group_symmetry:
_check_mesh_symm = not self._mesh_from_build
self.build_lattice_symmetry(check_mesh_symmetry=_check_mesh_symm)
if dump_input:
logger.info(self, 'lattice vectors a1 [%.9f, %.9f, %.9f]', *_a[0])
logger.info(self, ' a2 [%.9f, %.9f, %.9f]', *_a[1])
logger.info(self, ' a3 [%.9f, %.9f, %.9f]', *_a[2])
logger.info(self, 'dimension = %s', self.dimension)
logger.info(self, 'low_dim_ft_type = %s', self.low_dim_ft_type)
logger.info(self, 'Cell volume = %g', self.vol)
# Check atoms coordinates
if self.dimension > 0 and self.natm > 0:
scaled_atom_coords = np.linalg.solve(_a.T, self.atom_coords().T).T
atom_boundary_max = scaled_atom_coords[:,:self.dimension].max(axis=0)
atom_boundary_min = scaled_atom_coords[:,:self.dimension].min(axis=0)
if (np.any(atom_boundary_max > 1) or np.any(atom_boundary_min < -1)):
logger.warn(self, 'Atoms found out of the primitive cell.')
if self.exp_to_discard is not None:
logger.info(self, 'exp_to_discard = %s', self.exp_to_discard)
logger.info(self, 'rcut = %s (nimgs = %s)', self.rcut, self.nimgs)
logger.info(self, 'lattice sum = %d cells', len(self.get_lattice_Ls()))
logger.info(self, 'precision = %g', self.precision)
logger.info(self, 'pseudo = %s', self.pseudo)
if ke_cutoff is not None:
logger.info(self, 'ke_cutoff = %s', ke_cutoff)
logger.info(self, ' = %s mesh (%d PWs)',
self.mesh, np.prod(self.mesh))
else:
logger.info(self, 'mesh = %s (%d PWs)',
self.mesh, np.prod(self.mesh))
Ecut = pbctools.mesh_to_cutoff(self.lattice_vectors(), self.mesh)
logger.info(self, ' = ke_cutoff %s', Ecut)
if self.space_group_symmetry:
self.lattice_symmetry.dump_info()
return self
kernel = build
@property
def h(self):
return np.asarray(self.a).T
@h.setter
def h(self, x):
warnings.warn('cell.h is deprecated. It is replaced by the '
'(row-based) lattice vectors cell.a: cell.a = cell.h.T\n')
if isinstance(x, str):
x = x.replace(';',' ').replace(',',' ').replace('\n',' ')
self.a = np.asarray([float(z) for z in x.split()]).reshape(3,3).T
else:
self.a = np.asarray(x).T
@property
def _h(self):
return self.lattice_vectors().T
@property
def vol(self):
return abs(np.linalg.det(self.lattice_vectors()))
@property
def Gv(self):
return self.get_Gv()
@property
def gs(self):
return [n//2 for n in self.mesh]
@gs.setter
def gs(self, x):
warnings.warn('cell.gs is deprecated. It is replaced by cell.mesh,'
'the number of PWs (=2*gs+1) along each direction.')
self.mesh = [2*n+1 for n in x]
@property
def drop_exponent(self):
return self.exp_to_discard
@drop_exponent.setter
def drop_exponent(self, x):
self.exp_to_discard = x
@property
def nimgs(self):
return self.get_bounding_sphere(self.rcut)
@nimgs.setter
def nimgs(self, x):
b = self.reciprocal_vectors(norm_to=1)
heights_inv = lib.norm(b, axis=1)
self.rcut = max(np.asarray(x) / heights_inv)
rcut_guess = estimate_rcut(self, self.precision)
if self.rcut > rcut_guess*1.5:
msg = ('.nimgs is a deprecated attribute. It is replaced by .rcut '
'attribute for lattic sum cutoff radius. The given nimgs '
'%s is far over the estimated cutoff radius %s. ' %
(x, rcut_guess))
warnings.warn(msg)
[docs]
def lattice_vectors(self):
'''Convert the primitive lattice vectors.
Return 3x3 array in which each row represents one direction of the
lattice vectors (unit in Bohr)
'''
if isinstance(self.a, str):
a = self.a.replace(';',' ').replace(',',' ').replace('\n',' ')
a = np.asarray([float(x) for x in a.split()]).reshape(3,3)
else:
a = np.asarray(self.a, dtype=np.double)
if isinstance(self.unit, str):
if is_au(self.unit):
return a
else:
return a/param.BOHR
else:
return a/self.unit
[docs]
def get_scaled_positions(self):
''' Get scaled atom positions.
'''
a = self.lattice_vectors()
atm_pos = self.atom_coords()
scaled_atm_pos = np.dot(atm_pos,np.linalg.inv(a))
return scaled_atm_pos
[docs]
def reciprocal_vectors(self, norm_to=2*np.pi):
r'''
.. math::
\begin{align}
\mathbf{b_1} &= 2\pi \frac{\mathbf{a_2} \times \mathbf{a_3}}{\mathbf{a_1} \cdot (\mathbf{a_2} \times \mathbf{a_3})} \\
\mathbf{b_2} &= 2\pi \frac{\mathbf{a_3} \times \mathbf{a_1}}{\mathbf{a_2} \cdot (\mathbf{a_3} \times \mathbf{a_1})} \\
\mathbf{b_3} &= 2\pi \frac{\mathbf{a_1} \times \mathbf{a_2}}{\mathbf{a_3} \cdot (\mathbf{a_1} \times \mathbf{a_2})}
\end{align}
''' # noqa: E501
a = self.lattice_vectors()
if self.dimension == 1:
assert (abs(np.dot(a[0], a[1])) < 1e-9 and
abs(np.dot(a[0], a[2])) < 1e-9 and
abs(np.dot(a[1], a[2])) < 1e-9)
elif self.dimension == 2:
assert (abs(np.dot(a[0], a[2])) < 1e-9 and
abs(np.dot(a[1], a[2])) < 1e-9)
b = np.linalg.inv(a.T)
return norm_to * b
[docs]
def get_abs_kpts(self, scaled_kpts):
'''Get absolute k-points (in 1/Bohr), given "scaled" k-points in
fractions of lattice vectors.
Args:
scaled_kpts : (nkpts, 3) ndarray of floats
Returns:
abs_kpts : (nkpts, 3) ndarray of floats
'''
return np.dot(scaled_kpts, self.reciprocal_vectors())
[docs]
def get_scaled_kpts(self, abs_kpts, kpts_in_ibz=True):
'''Get scaled k-points, given absolute k-points in 1/Bohr.
Args:
abs_kpts : (nkpts, 3) ndarray of floats or :class:`KPoints` object
kpts_in_ibz : bool
If True, return k-points in IBZ; otherwise, return k-points in BZ.
Default value is True. This has effects only if abs_kpts is a
:class:`KPoints` object
Returns:
scaled_kpts : (nkpts, 3) ndarray of floats
'''
from pyscf.pbc.lib.kpts import KPoints
if isinstance(abs_kpts, KPoints):
if kpts_in_ibz:
return abs_kpts.kpts_scaled_ibz
else:
return abs_kpts.kpts_scaled
return 1./(2*np.pi)*np.dot(abs_kpts, self.lattice_vectors().T)
[docs]
def cutoff_to_mesh(self, ke_cutoff):
'''Convert KE cutoff to FFT-mesh
Args:
ke_cutoff : float
KE energy cutoff in a.u.
Returns:
mesh : (3,) array
'''
a = self.lattice_vectors()
dim = self.dimension
mesh = pbctools.cutoff_to_mesh(a, ke_cutoff)
if dim < 2 or (dim == 2 and self.low_dim_ft_type == 'inf_vacuum'):
mesh[dim:] = self.mesh[dim:]
return mesh
make_kpts = get_kpts = make_kpts
pack = pack
[docs]
@classmethod
@lib.with_doc(unpack.__doc__)
def unpack(cls, moldic):
return unpack(moldic)
[docs]
@lib.with_doc(unpack.__doc__)
def unpack_(self, moldic):
self.__dict__.update(moldic)
return self
dumps = dumps
[docs]
@classmethod
@lib.with_doc(loads.__doc__)
def loads(cls, molstr):
return loads(molstr)
[docs]
@lib.with_doc(unpack.__doc__)
def loads_(self, molstr):
self.__dict__.update(loads(molstr).__dict__)
return self
bas_rcut = bas_rcut
get_lattice_Ls = pbctools.get_lattice_Ls
get_nimgs = get_nimgs
get_ewald_params = get_ewald_params
get_bounding_sphere = get_bounding_sphere
get_Gv = get_Gv
get_Gv_weights = get_Gv_weights
get_SI = get_SI
ewald = ewald
energy_nuc = get_enuc = ewald
gen_uniform_grids = get_uniform_grids = get_uniform_grids
__add__ = conc_cell
[docs]
def pbc_intor(self, intor, comp=None, hermi=0, kpts=None, kpt=None,
shls_slice=None, **kwargs):
r'''One-electron integrals with PBC.
.. math::
\sum_T \int \mu(r) * [intor] * \nu (r-T) dr
See also Mole.intor
'''
if not self._built:
logger.warn(self, 'Warning: intor envs of %s not initialized.', self)
# FIXME: Whether to check _built and call build? ._bas and .basis
# may not be consistent. calling .build() may leads to wrong intor env.
#self.build(False, False)
return intor_cross(intor, self, self, comp, hermi, kpts, kpt,
shls_slice, **kwargs)
pbc_eval_ao = pbc_eval_gto = pbc_eval_gto
[docs]
@lib.with_doc(pbc_eval_gto.__doc__)
def eval_gto(self, eval_name, coords, comp=None, kpts=None, kpt=None,
shls_slice=None, non0tab=None, ao_loc=None, cutoff=None,
out=None):
if eval_name[:3] == 'PBC':
return self.pbc_eval_gto(eval_name, coords, comp, kpts, kpt,
shls_slice, non0tab, ao_loc, cutoff, out)
else:
return mole.eval_gto(self, eval_name, coords, comp,
shls_slice, non0tab, ao_loc, cutoff, out)
eval_ao = eval_gto
[docs]
def from_ase(self, ase_atom):
'''Update cell based on given ase atom object
Examples:
>>> from ase.lattice import bulk
>>> cell.from_ase(bulk('C', 'diamond', a=LATTICE_CONST))
'''
from pyscf.pbc.tools import pyscf_ase
self.a = ase_atom.cell
self.atom = pyscf_ase.ase_atoms_to_pyscf(ase_atom)
return self
[docs]
def to_mol(self):
'''Return a Mole object using the same atoms and basis functions as
the Cell object.
'''
#FIXME: should cell be converted to mole object? If cell is converted
# and a mole object is returned, many attributes (e.g. the GTH basis,
# gth-PP) will not be recognized by mole.build function.
mol = self.view(mole.Mole)
delattr(mol, 'a')
delattr(mol, '_mesh')
if mol.symmetry:
mol._build_symmetry()
return mol
del (INTEGRAL_PRECISION, WRAP_AROUND, WITH_GAMMA, EXP_DELIMITER)