# Author: Artem Pulkin
"""
This and other `_slow` modules implement the time-dependent Hartree-Fock procedure. The primary performance drawback is
that, unlike other 'fast' routines with an implicit construction of the eigenvalue problem, these modules construct
TDHF matrices explicitly via an AO-MO transformation, i.e. with a O(N^5) complexity scaling. As a result, regular
`numpy.linalg.eig` can be used to retrieve TDHF roots in a reliable fashion without any issues related to the Davidson
procedure. Several variants of TDHF are available:
* `pyscf.tdscf.rhf_slow`: the molecular implementation;
* `pyscf.pbc.tdscf.rhf_slow`: PBC (periodic boundary condition) implementation for RHF objects of `pyscf.pbc.scf`
modules;
* (this module) `pyscf.pbc.tdscf.krhf_slow_supercell`: PBC implementation for KRHF objects of `pyscf.pbc.scf` modules.
Works with an arbitrary number of k-points but has a overhead due to an effective construction of a supercell.
* `pyscf.pbc.tdscf.krhf_slow_gamma`: A Gamma-point calculation resembling the original `pyscf.pbc.tdscf.krhf`
module. Despite its name, it accepts KRHF objects with an arbitrary number of k-points but finds only few TDHF roots
corresponding to collective oscillations without momentum transfer;
* `pyscf.pbc.tdscf.krhf_slow`: PBC implementation for KRHF objects of `pyscf.pbc.scf` modules. Works with
an arbitrary number of k-points and employs k-point conservation (diagonalizes matrix blocks separately).
"""
from pyscf.tdscf.common_slow import TDERIMatrixBlocks, PeriodicMFMixin
from pyscf.tdscf import rhf_slow
from pyscf.pbc.lib.kpts_helper import loop_kkk
from pyscf.lib import logger
import numpy
import scipy
from itertools import product
# Convention for these modules:
# * PhysERI, PhysERI4, PhysERI8 are 2-electron integral routines computed directly (for debug purposes), with a 4-fold
# symmetry and with an 8-fold symmetry
# * vector_to_amplitudes reshapes and normalizes the solution
# * TDRHF provides a container
[docs]
class PhysERI(PeriodicMFMixin, TDERIMatrixBlocks):
def __init__(self, model, frozen=None):
"""
The TDHF ERI implementation performing a full transformation of integrals to Bloch functions. No symmetries are
employed in this class.
Args:
model (KRHF): the base model;
frozen (int, Iterable): the number of frozen valence orbitals or the list of frozen orbitals for all
k-points or multiple lists of frozen orbitals for each k-point;
"""
TDERIMatrixBlocks.__init__(self)
PeriodicMFMixin.__init__(self, model, frozen=frozen)
# Phys representation
self.__full_eri_k__ = {}
for k in loop_kkk(len(model.kpts)):
k = k + (self.kconserv[k],)
self.__full_eri_k__[k] = self.ao2mo_k(tuple(self.mo_coeff[j] for j in k), k)
[docs]
def ao2mo_k(self, coeff, k):
"""
Phys ERI in MO basis.
Args:
coeff (Iterable): MO orbitals;
k (Iterable): the 4 k-points MOs correspond to;
Returns:
ERI in MO basis.
"""
coeff = (coeff[0], coeff[2], coeff[1], coeff[3])
k = (k[0], k[2], k[1], k[3])
result = self.model.with_df.ao2mo(coeff, tuple(self.model.kpts[i] for i in k), compact=False)
return result.reshape(
tuple(i.shape[1] for i in coeff)
).swapaxes(1, 2)
def __get_mo_energies__(self, k1, k2):
"""This routine collects occupied and virtual MO energies."""
return self.mo_energy[k1][:self.nocc[k1]], self.mo_energy[k2][self.nocc[k2]:]
[docs]
def tdhf_diag_k(self, k1, k2):
"""
Retrieves the diagonal block.
Args:
k1 (int): first k-index (row);
k2 (int): second k-index (column);
Returns:
The diagonal block.
"""
# Everything is already implemented in molecular code
return super().tdhf_diag(k1, k2)
[docs]
def tdhf_diag(self, pairs=None):
"""
Retrieves the merged diagonal block with specified or all possible k-index pairs.
Args:
pairs (Iterable): pairs of k-points to assemble;
Returns:
The diagonal block.
"""
if pairs is None:
pairs = product(range(len(self.model.kpts)), range(len(self.model.kpts)))
result = []
for k1, k2 in pairs:
result.append(self.tdhf_diag_k(k1, k2))
return scipy.linalg.block_diag(*result)
def __calc_block__(self, item, k):
if k in self.__full_eri_k__:
slc = tuple(slice(self.nocc[_k]) if i == 'o' else slice(self.nocc[_k], None) for i, _k in zip(item, k))
return self.__full_eri_k__[k][slc]
else:
return numpy.zeros(tuple(
self.nocc[_k] if i == 'o' else self.nmo[_k] - self.nocc[_k]
for i, _k in zip(item, k)
))
[docs]
def eri_mknj_k(self, item, k):
"""
Retrieves ERI block using 'mknj' notation.
Args:
item (str): a 4-character string of 'mknj' letters;
k (Iterable): k indexes;
Returns:
The corresponding block of ERI (phys notation).
"""
# Everything is already implemented in molecular code
return super().eri_mknj(item, k)
[docs]
def eri_mknj(self, item, pairs_row=None, pairs_column=None):
"""
Retrieves the merged ERI block using 'mknj' notation with all k-indexes.
Args:
item (str): a 4-character string of 'mknj' letters;
pairs_row (Iterable): iterator for pairs of row k-points (first index in the output matrix);
pairs_column (Iterable): iterator for pairs of column k-points (second index in the output matrix);
Returns:
The corresponding block of ERI (phys notation).
"""
if pairs_row is None:
pairs_row = product(range(len(self.model.kpts)), range(len(self.model.kpts)))
if pairs_column is None:
pairs_column = product(range(len(self.model.kpts)), range(len(self.model.kpts)))
# Second index has to support re-iterations
pairs_column = tuple(pairs_column)
result = []
for k1, k2 in pairs_row:
result.append([])
for k3, k4 in pairs_column:
result[-1].append(self.eri_mknj_k(item, (k1, k2, k3, k4)))
r = numpy.block(result)
return r / len(self.model.kpts)
[docs]
class PhysERI4(PhysERI):
symmetries = [
((0, 1, 2, 3), False),
((1, 0, 3, 2), False),
((2, 3, 0, 1), True),
((3, 2, 1, 0), True),
]
def __init__(self, model, frozen=None):
"""
The TDHF ERI implementation performing a partial transformation of integrals to Bloch functions. A 4-fold
symmetry of complex-valued wavefunctions is employed in this class.
Args:
model (KRHF): the base model;
frozen (int, Iterable): the number of frozen valence orbitals or the list of frozen orbitals for all
k-points or multiple lists of frozen orbitals for each k-point;
"""
TDERIMatrixBlocks.__init__(self)
PeriodicMFMixin.__init__(self, model, frozen=frozen)
def __calc_block__(self, item, k):
if self.kconserv[k[:3]] == k[3]:
logger.info(self.model, "Computing {} {} ...".format(''.join(item), repr(k)))
return self.ao2mo_k(tuple(
self.mo_coeff[_k][:, :self.nocc[_k]] if i == "o" else self.mo_coeff[_k][:, self.nocc[_k]:]
for i, _k in zip(item, k)
), k)
else:
return numpy.zeros(tuple(
self.nocc[_k] if i == 'o' else self.nmo[_k] - self.nocc[_k]
for i, _k in zip(item, k)
))
[docs]
class PhysERI8(PhysERI4):
symmetries = [
((0, 1, 2, 3), False),
((1, 0, 3, 2), False),
((2, 3, 0, 1), False),
((3, 2, 1, 0), False),
((2, 1, 0, 3), False),
((3, 0, 1, 2), False),
((0, 3, 2, 1), False),
((1, 2, 3, 0), False),
]
def __init__(self, model, frozen=None):
"""
The TDHF ERI implementation performing a partial transformation of integrals to Bloch functions. An 8-fold
symmetry of real-valued wavefunctions is employed in this class.
Args:
model (KRHF): the base model;
frozen (int, Iterable): the number of frozen valence orbitals or the list of frozen orbitals for all
k-points or multiple lists of frozen orbitals for each k-point;
"""
super().__init__(model, frozen=frozen)
[docs]
def vector_to_amplitudes(vectors, nocc, nmo):
"""
Transforms (reshapes) and normalizes vectors into amplitudes.
Args:
vectors (numpy.ndarray): raw eigenvectors to transform;
nocc (tuple): numbers of occupied orbitals;
nmo (tuple): the total numbers of AOs per k-point;
Returns:
Amplitudes with the following shape: (# of roots, 2 (x or y), # of kpts, # of kpts, # of occupied orbitals,
# of virtual orbitals).
"""
if not all(i == nocc[0] for i in nocc):
raise NotImplementedError("Varying occupation numbers are not implemented yet")
nk = len(nocc)
nocc = nocc[0]
if not all(i == nmo[0] for i in nmo):
raise NotImplementedError("Varying AO spaces are not implemented yet")
nmo = nmo[0]
vectors = numpy.asanyarray(vectors)
vectors = vectors.reshape(2, nk, nk, nocc, nmo-nocc, vectors.shape[1])
norm = (abs(vectors) ** 2).sum(axis=(1, 2, 3, 4))
norm = 2 * (norm[0] - norm[1])
vectors /= norm ** .5
return vectors.transpose(5, 0, 1, 2, 3, 4)
[docs]
class TDRHF(rhf_slow.TDRHF):
eri4 = PhysERI4
eri8 = PhysERI8
v2a = staticmethod(vector_to_amplitudes)
