#!/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.
#
import numpy as np
from pyscf.pbc import scf, dft, gto
from pyscf.eph.rhf import solve_hmat, _freq_mass_weighted_vec
from pyscf.lib import logger, param
from pyscf.data.nist import MP_ME
'''Electron-Phonon matrix from finite difference for Gamma Point'''
# Note, the code now only return eph matrix at Gamma Point
# cell relaxation needs to be performed before computing eph matrix
[docs]
def copy_mf(mf, cell):
mf1 = mf.__class__(cell)
if isinstance(mf, scf.hf.KohnShamDFT):
mf1.xc = mf.xc
mf1.kpts = mf.kpts
mf1.exxdiv = getattr(mf, 'exxdiv', None)
mf1.conv_tol = mf.conv_tol
mf1.conv_tol_grad = mf.conv_tol_grad
return mf1
[docs]
def run_mfs(mf, cells_a, cells_b):
'''perform a set of calculations on given two sets of cell'''
nconfigs = len(cells_a)
dm0 = mf.make_rdm1()
mflist = []
for i in range(nconfigs):
mf1 = copy_mf(mf, cells_a[i])
mf2 = copy_mf(mf, cells_b[i])
mf1.kernel(dm0=dm0)
mf2.kernel(dm0=dm0)
if not (mf1.converged):
logger.warn(mf, "%ith config mf1 not converged", i)
if not (mf2.converged):
logger.warn(mf, "%ith config mf2 not converged", i)
mflist.append((mf1, mf2))
return mflist
[docs]
def gen_cells(cell, disp):
"""From the given cell, generate 3N cells with a shift on
+ displacement(cell_a) and - displacement(cell_s) on each Cartesian
coordinates
"""
coords = cell.atom_coords()
if cell.unit[0].lower() == 'a':
coords = np.asarray(coords) * param.BOHR
disp_ = disp * param.BOHR
else:
disp_ = disp
natoms = len(coords)
cell_a, cell_s = [],[]
for i in range(natoms):
for x in range(3):
new_coords_a, new_coords_s = coords.copy(), coords.copy()
new_coords_a[i][x] += disp_
new_coords_s[i][x] -= disp_
atoma = [[cell.atom_symbol(j), coord] for (j, coord) in zip(range(natoms), new_coords_a)]
atoms = [[cell.atom_symbol(j), coord] for (j, coord) in zip(range(natoms), new_coords_s)]
cell_a.append(cell.set_geom_(atoma, inplace=False))
cell_s.append(cell.set_geom_(atoms, inplace=False))
return cell_a, cell_s
[docs]
def get_vmat(mf, mfset, disp):
nconfigs = len(mfset)
vmat=[]
mygrad = mf.nuc_grad_method()
veff = mygrad.get_veff()
RESTRICTED = (veff.ndim==4)
v1e = mygrad.get_hcore() - np.asarray(mf.cell.pbc_intor("int1e_ipkin", kpts=mf.kpts))
if RESTRICTED:
vtmp = veff - v1e.transpose(1,0,2,3)
else:
vtmp = veff - v1e.transpose(1,0,2,3)[:,None]
aoslice = mf.cell.aoslice_by_atom()
for i in range(nconfigs):
atmid, axis = np.divmod(i, 3)
p0, p1 = aoslice[atmid][2:]
mf1, mf2 = mfset[i]
if RESTRICTED:
vfull1 = mf1.get_veff() + mf1.get_hcore() \
- np.asarray(mf1.cell.pbc_intor('int1e_kin', kpts=mf1.kpts)) # <u+|V+|v+>
vfull2 = mf2.get_veff() + mf2.get_hcore() \
- np.asarray(mf2.cell.pbc_intor('int1e_kin', kpts=mf2.kpts)) # <u-|V-|v->
else:
vfull1 = mf1.get_veff() + mf1.get_hcore()[None] \
- np.asarray(mf1.cell.pbc_intor('int1e_kin', kpts=mf1.kpts))[None] # <u+|V+|v+>
vfull2 = mf2.get_veff() + mf2.get_hcore()[None] \
- np.asarray(mf2.cell.pbc_intor('int1e_kin', kpts=mf2.kpts))[None] # <u-|V-|v->
vfull = (vfull1 - vfull2)/disp # (<p+|V+|q+>-<p-|V-|q->)/dR
if RESTRICTED:
vfull[:,p0:p1] -= vtmp[axis,:,p0:p1]
vfull[:,:,p0:p1] -= vtmp[axis,:,p0:p1].transpose(0,2,1).conj()
else:
vfull[:,:,p0:p1] -= vtmp[axis,:,:,p0:p1]
vfull[:,:,:,p0:p1] -= vtmp[axis,:,:,p0:p1].transpose(0,1,3,2).conj()
vmat.append(vfull)
vmat= np.asarray(vmat)
if RESTRICTED:
return vmat[:,0]
else:
return vmat[:,:,0]
[docs]
def run_hess(mfset, disp):
natoms = len(mfset[0][0].cell.atom_mass_list())
hess=[]
for (mf1, mf2) in mfset:
grad1 = mf1.nuc_grad_method()
grad2 = mf2.nuc_grad_method()
g1 = grad1.kernel()
g2 = grad2.kernel()
gdelta = (g1-g2) / disp
hess.append(gdelta)
hess = np.asarray(hess).reshape(natoms, 3, natoms, 3).transpose(0,2,1,3)
return hess
[docs]
def kernel(mf, disp=1e-4, mo_rep=False):
if not mf.converged: mf.kernel()
mo_coeff = np.asarray(mf.mo_coeff)
RESTRICTED= (mo_coeff.ndim==3)
cell = mf.cell
cells_a, cells_b = gen_cells(cell, disp/2.0) # generate a bunch of cells with disp/2 on each cartesian coord
mfset = run_mfs(mf, cells_a, cells_b) # run mean field calculations on all these cells
vmat = get_vmat(mf, mfset, disp) # extracting <u|dV|v>/dR
hmat = run_hess(mfset, disp)
omega, vec = solve_hmat(cell, hmat)
mass = cell.atom_mass_list() * MP_ME
vec = _freq_mass_weighted_vec(vec, omega, mass)
if mo_rep:
if RESTRICTED:
vmat = np.einsum('xuv,up,vq->xpq', vmat, mo_coeff[0].conj(), mo_coeff[0])
else:
vmat = np.einsum('xsuv,sup,svq->xspq', vmat, mo_coeff[:,0].conj(), mo_coeff[:,0])
if RESTRICTED:
mat = np.einsum('xJ,xpq->Jpq', vec, vmat)
else:
mat = np.einsum('xJ,xspq->sJpq', vec, vmat)
return mat, omega
if __name__ == '__main__':
cell = gto.Cell()
cell.atom='''
C 0.000000000000 0.000000000000 0.000000000000
C 1.685068664391 1.685068664391 1.685068664391
'''
cell.basis = 'gth-szv'
cell.pseudo = 'gth-pade'
cell.a = '''
0.000000000, 3.370137329, 3.370137329
3.370137329, 0.000000000, 3.370137329
3.370137329, 3.370137329, 0.000000000'''
cell.unit = 'B'
cell.verbose = 4
cell.build()
kpts = cell.make_kpts([1,1,1])
mf = dft.KRKS(cell, kpts)
mf.conv_tol = 1e-14
mf.conv_tol_grad = 1e-8
mf.kernel()
vmat, omega = kernel(mf, mo_rep=True)
