def update(self, **kwargs):
from PyQuante.Ints import getJ, getK
from PyQuante.LA2 import geigh, mkdens
from PyQuante.rohf import ao2mo
from PyQuante.hartree_fock import get_energy
from PyQuante.NumWrap import eigh, matrixmultiply
if self.orbs is None:
self.orbe, self.orbs = geigh(self.h, self.S)
Da = mkdens(self.orbs, 0, self.nalpha)
Db = mkdens(self.orbs, 0, self.nbeta)
Ja = getJ(self.ERI, Da)
Jb = getJ(self.ERI, Db)
Ka = getK(self.ERI, Da)
Kb = getK(self.ERI, Db)
Fa = self.h + Ja + Jb - Ka
Fb = self.h + Ja + Jb - Kb
energya = get_energy(self.h, Fa, Da)
energyb = get_energy(self.h, Fb, Db)
self.energy = (energya + energyb) / 2 + self.Enuke
Fa = ao2mo(Fa, self.orbs)
Fb = ao2mo(Fb, self.orbs)
# Building the approximate Fock matrices in the MO basis
F = 0.5 * (Fa + Fb)
K = Fb - Fa
# The Fock matrix now looks like
# F-K | F + K/2 | F
# ---------------------------------
# F + K/2 | F | F - K/2
# ---------------------------------
# F | F - K/2 | F + K
# Make explicit slice objects to simplify this
do = slice(0, self.nbeta)
so = slice(self.nbeta, self.nalpha)
uo = slice(self.nalpha, self.norbs)
F[do, do] -= K[do, do]
F[uo, uo] += K[uo, uo]
F[do, so] += 0.5 * K[do, so]
F[so, do] += 0.5 * K[so, do]
F[so, uo] -= 0.5 * K[so, uo]
F[uo, so] -= 0.5 * K[uo, so]
self.orbe, mo_orbs = eigh(F)
self.orbs = matrixmultiply(self.orbs, mo_orbs)
return
def update(self,**opts):
from PyQuante.Ints import getJ,getK
from PyQuante.LA2 import geigh,mkdens
from PyQuante.rohf import ao2mo
from PyQuante.hartree_fock import get_energy
from PyQuante.NumWrap import eigh,matrixmultiply
if self.orbs is None:
self.orbe,self.orbs = geigh(self.h, self.S)
Da = mkdens(self.orbs,0,self.nalpha)
Db = mkdens(self.orbs,0,self.nbeta)
Ja = getJ(self.ERI,Da)
Jb = getJ(self.ERI,Db)
Ka = getK(self.ERI,Da)
Kb = getK(self.ERI,Db)
Fa = self.h+Ja+Jb-Ka
Fb = self.h+Ja+Jb-Kb
energya = get_energy(self.h,Fa,Da)
energyb = get_energy(self.h,Fb,Db)
self.energy = (energya+energyb)/2 + self.Enuke
Fa = ao2mo(Fa,self.orbs)
Fb = ao2mo(Fb,self.orbs)
# Building the approximate Fock matrices in the MO basis
F = 0.5*(Fa+Fb)
K = Fb-Fa
# The Fock matrix now looks like
# F-K | F + K/2 | F
# ---------------------------------
# F + K/2 | F | F - K/2
# ---------------------------------
# F | F - K/2 | F + K
# Make explicit slice objects to simplify this
do = slice(0,self.nbeta)
so = slice(self.nbeta,self.nalpha)
uo = slice(self.nalpha,self.norbs)
F[do,do] -= K[do,do]
F[uo,uo] += K[uo,uo]
F[do,so] += 0.5*K[do,so]
F[so,do] += 0.5*K[so,do]
F[so,uo] -= 0.5*K[so,uo]
F[uo,so] -= 0.5*K[uo,so]
self.orbe,mo_orbs = eigh(F)
self.orbs = matrixmultiply(self.orbs,mo_orbs)
return
def test_mol(mol,**opts):
basis_data = opts.get('basis_data',None)
do_python_tests = opts.get('do_python_tests',True)
make_hf_driver(mol,basis_data=basis_data)
if do_python_tests:
bfs = getbasis(mol,basis_data)
S= getS(bfs)
T = getT(bfs)
V = getV(bfs,mol)
Ints = get2ints(bfs)
nclosed,nopen = mol.get_closedopen()
enuke = mol.get_enuke()
assert nopen==0
h = T+V
orbe,orbs = GHeigenvectors(h,S)
print "Eval of h: ",
print orbe
for i in range(10):
D = mkdens(orbs,0,nclosed)
J = getJ(Ints,D)
K = getK(Ints,D)
orbe,orbs = GHeigenvectors(h+2*J-K,S)
eone = TraceProperty(D,h)
ej = TraceProperty(D,J)
ek = TraceProperty(D,K)
energy = enuke+2*eone+2*ej-ek
print i,energy,enuke,eone,ej,ek
return
def test_mol(mol, **opts):
basis_data = opts.get('basis_data', None)
do_python_tests = opts.get('do_python_tests', True)
make_hf_driver(mol, basis_data=basis_data)
if do_python_tests:
bfs = getbasis(mol, basis_data)
S = getS(bfs)
T = getT(bfs)
V = getV(bfs, mol)
Ints = get2ints(bfs)
nclosed, nopen = mol.get_closedopen()
enuke = mol.get_enuke()
assert nopen == 0
h = T + V
orbe, orbs = GHeigenvectors(h, S)
print "Eval of h: ",
print orbe
for i in range(10):
D = mkdens(orbs, 0, nclosed)
J = getJ(Ints, D)
K = getK(Ints, D)
orbe, orbs = GHeigenvectors(h + 2 * J - K, S)
eone = TraceProperty(D, h)
ej = TraceProperty(D, J)
ek = TraceProperty(D, K)
energy = enuke + 2 * eone + 2 * ej - ek
print i, energy, enuke, eone, ej, ek
return
def update(self,**kwargs):
from PyQuante.LA2 import trace2
from PyQuante.Ints import getJ
from PyQuante.dft import getXC
#self.DoAveraging = kwargs.get('DoAveraging',True)
#if self.DoAveraging:
# self.Averager = DIIS(self.S)
if self.DoAveraging and self.dmat is not None:
self.F = self.Averager.getF(self.F,self.dmat)
self.dmat,self.entropy = self.solver.solve(self.F,**kwargs)
D = self.dmat
self.gr.setdens(D)
self.J = getJ(self.ERI,D)
self.Ej = 2*trace2(D,self.J)
self.Exc,self.XC = getXC(self.gr,self.nel,
functional=self.functional)
self.Eone = 2*trace2(D,self.h)
self.F = self.h+2*self.J+self.XC
self.energy = self.Eone + self.Ej + self.Exc + self.Enuke + self.entropy
return
def get_energy(self,b):
self.iter += 1
ba = b[:self.nbf]
bb = b[self.nbf:]
self.Hoepa = get_Hoep(ba,self.H0,self.Gij)
self.Hoepb = get_Hoep(bb,self.H0,self.Gij)
self.orbea,self.orbsa = geigh(self.Hoepa,self.S)
self.orbeb,self.orbsb = geigh(self.Hoepb,self.S)
if self.etemp:
self.Da,entropya = mkdens_fermi(2*self.nalpha,self.orbea,self.orbsa,
self.etemp)
self.Db,entropyb = mkdens_fermi(2*self.nbeta,self.orbeb,self.orbsb,
self.etemp)
self.entropy = 0.5*(entropya+entropyb)
else:
self.Da = mkdens(self.orbsa,0,self.nalpha)
self.Db = mkdens(self.orbsb,0,self.nbeta)
self.entropy=0
J = getJ(self.Ints,self.Da+self.Db)
Ka = getK(self.Ints,self.Da)
Kb = getK(self.Ints,self.Db)
self.Fa = self.h + J - Ka
self.Fb = self.h + J - Kb
self.energy = 0.5*(trace2(self.h+self.Fa,self.Da) +
trace2(self.h+self.Fb,self.Db))\
+ self.Enuke + self.entropy
if self.iter == 1 or self.iter % 10 == 0:
logging.debug("%4d %10.5f %10.5f" % (self.iter,self.energy,dot(b,b)))
return self.energy
def __init__(self,solver):
# Solver is a pointer to a HF or a DFT calculation that has
# already converged
self.solver = solver
self.bfs = self.solver.bfs
self.nbf = len(self.bfs)
self.S = self.solver.S
self.h = self.solver.h
self.Ints = self.solver.Ints
self.molecule = self.solver.molecule
self.nel = self.molecule.get_nel()
self.nclosed, self.nopen = self.molecule.get_closedopen()
self.Enuke = self.molecule.get_enuke()
self.norb = self.nbf
self.orbs = self.solver.orbs
self.orbe = self.solver.orbe
self.Gij = []
for g in range(self.nbf):
gmat = zeros((self.nbf,self.nbf),'d')
self.Gij.append(gmat)
gbf = self.bfs[g]
for i in range(self.nbf):
ibf = self.bfs[i]
for j in range(i+1):
jbf = self.bfs[j]
gij = three_center(ibf,gbf,jbf)
gmat[i,j] = gij
gmat[j,i] = gij
D0 = mkdens(self.orbs,0,self.nclosed)
J0 = getJ(self.Ints,D0)
Vfa = (2.0*(self.nel-1.0)/self.nel)*J0
self.H0 = self.h + Vfa
self.b = zeros(self.nbf,'d')
return
def __init__(self, solver):
# Solver is a pointer to a HF or a DFT calculation that has
# already converged
self.solver = solver
self.bfs = self.solver.bfs
self.nbf = len(self.bfs)
self.S = self.solver.S
self.h = self.solver.h
self.Ints = self.solver.Ints
self.molecule = self.solver.molecule
self.nel = self.molecule.get_nel()
self.nclosed, self.nopen = self.molecule.get_closedopen()
self.Enuke = self.molecule.get_enuke()
self.norb = self.nbf
self.orbs = self.solver.orbs
self.orbe = self.solver.orbe
self.Gij = []
for g in xrange(self.nbf):
gmat = zeros((self.nbf, self.nbf), 'd')
self.Gij.append(gmat)
gbf = self.bfs[g]
for i in xrange(self.nbf):
ibf = self.bfs[i]
for j in xrange(i + 1):
jbf = self.bfs[j]
gij = three_center(ibf, gbf, jbf)
gmat[i, j] = gij
gmat[j, i] = gij
D0 = mkdens(self.orbs, 0, self.nclosed)
J0 = getJ(self.Ints, D0)
Vfa = (2.0 * (self.nel - 1.0) / self.nel) * J0
self.H0 = self.h + Vfa
self.b = zeros(self.nbf, 'd')
return
def get_energy(self, b):
self.iter += 1
ba = b[:self.nbf]
bb = b[self.nbf:]
self.Hoepa = get_Hoep(ba, self.H0, self.Gij)
self.Hoepb = get_Hoep(bb, self.H0, self.Gij)
self.orbea, self.orbsa = geigh(self.Hoepa, self.S)
self.orbeb, self.orbsb = geigh(self.Hoepb, self.S)
if self.etemp:
self.Da, entropya = mkdens_fermi(2 * self.nalpha, self.orbea,
self.orbsa, self.etemp)
self.Db, entropyb = mkdens_fermi(2 * self.nbeta, self.orbeb,
self.orbsb, self.etemp)
self.entropy = 0.5 * (entropya + entropyb)
else:
self.Da = mkdens(self.orbsa, 0, self.nalpha)
self.Db = mkdens(self.orbsb, 0, self.nbeta)
self.entropy = 0
J = getJ(self.Ints, self.Da + self.Db)
Ka = getK(self.Ints, self.Da)
Kb = getK(self.Ints, self.Db)
self.Fa = self.h + J - Ka
self.Fb = self.h + J - Kb
self.energy = 0.5*(trace2(self.h+self.Fa,self.Da) +
trace2(self.h+self.Fb,self.Db))\
+ self.Enuke + self.entropy
if self.iter == 1 or self.iter % 10 == 0:
logging.debug("%4d %10.5f %10.5f" %
(self.iter, self.energy, dot(b, b)))
return self.energy
def update(self, **kwargs):
from PyQuante.LA2 import trace2
from PyQuante.Ints import getJ
from PyQuante.dft import getXC
#self.DoAveraging = kwargs.get('DoAveraging',True)
#if self.DoAveraging:
# self.Averager = DIIS(self.S)
if self.DoAveraging and self.dmat is not None:
self.F = self.Averager.getF(self.F, self.dmat)
self.dmat, self.entropy = self.solver.solve(self.F, **kwargs)
D = self.dmat
self.gr.setdens(D)
self.J = getJ(self.ERI, D)
self.Ej = 2 * trace2(D, self.J)
self.Exc, self.XC = getXC(self.gr,
self.nel,
functional=self.functional)
self.Eone = 2 * trace2(D, self.h)
self.F = self.h + 2 * self.J + self.XC
self.energy = self.Eone + self.Ej + self.Exc + self.Enuke + self.entropy
return
def get_os_hams(Ints,Ds):
# GVB2P5 did this a little more efficiently; they stored
# 2J-K for the core, then J,K for each open shell. Didn't
# seem worth it here, so I'm jst storing J,K separately
Hs = []
for D in Ds:
Hs.append(getJ(Ints,D))
Hs.append(getK(Ints,D))
return Hs
def pyq1_dft(atomtuples=[(2, (0, 0, 0))],
basis='6-31G**',
maxit=10,
xcname='SVWN'):
from PyQuante import Ints, settings, Molecule
from PyQuante.dft import getXC
from PyQuante.MG2 import MG2 as MolecularGrid
from PyQuante.LA2 import mkdens, geigh, trace2
from PyQuante.Ints import getJ
print("PyQ1 DFT run")
atoms = Molecule('Pyq1', atomlist=atomtuples)
bfs = Ints.getbasis(atoms, basis=basis)
S, h, Ints = Ints.getints(bfs, atoms)
nclosed, nopen = nel // 2, nel % 2
assert nopen == 0
enuke = atoms.get_enuke()
grid_nrad = settings.DFTGridRadii
grid_fineness = settings.DFTGridFineness
gr = MolecularGrid(atoms, grid_nrad, grid_fineness)
gr.set_bf_amps(bfs)
orbe, orbs = geigh(h, S)
eold = 0
for i in range(maxit):
D = mkdens(orbs, 0, nclosed)
gr.setdens(D)
J = getJ(Ints, D)
Exc, Vxc = getXC(gr, nel, functional=xcname)
F = h + 2 * J + Vxc
orbe, orbs = geigh(F, S)
Ej = 2 * trace2(D, J)
Eone = 2 * trace2(D, h)
energy = Eone + Ej + Exc + enuke
print(i, energy, Eone, Ej, Exc, enuke)
if np.isclose(energy, eold):
break
eold = energy
return energy
def pyq1_dft(atomtuples=[(2,(0,0,0))],basis = '6-31G**',maxit=10,
xcname='SVWN'):
from PyQuante import Ints,settings,Molecule
from PyQuante.dft import getXC
from PyQuante.MG2 import MG2 as MolecularGrid
from PyQuante.LA2 import mkdens,geigh,trace2
from PyQuante.Ints import getJ
print ("PyQ1 DFT run")
atoms = Molecule('Pyq1',atomlist=atomtuples)
bfs = Ints.getbasis(atoms,basis=basis)
S,h,Ints = Ints.getints(bfs,atoms)
nclosed,nopen = nel//2,nel%2
assert nopen==0
enuke = atoms.get_enuke()
grid_nrad = settings.DFTGridRadii
grid_fineness = settings.DFTGridFineness
gr = MolecularGrid(atoms,grid_nrad,grid_fineness)
gr.set_bf_amps(bfs)
orbe,orbs = geigh(h,S)
eold = 0
for i in range(maxit):
D = mkdens(orbs,0,nclosed)
gr.setdens(D)
J = getJ(Ints,D)
Exc,Vxc = getXC(gr,nel,functional=xcname)
F = h+2*J+Vxc
orbe,orbs = geigh(F,S)
Ej = 2*trace2(D,J)
Eone = 2*trace2(D,h)
energy = Eone + Ej + Exc + enuke
print (i,energy,Eone,Ej,Exc,enuke)
if np.isclose(energy,eold):
break
eold = energy
return energy
def update(self, **kwargs):
from PyQuante.LA2 import trace2
from PyQuante.Ints import getJ, getK
if self.DoAveraging and self.dmat is not None:
self.F = self.Averager.getF(self.F, self.dmat)
self.dmat, self.entropy = self.solver.solve(self.F, **kwargs)
D = self.dmat
self.J = getJ(self.ERI, D)
self.Ej = 2 * trace2(D, self.J)
self.K = getK(self.ERI, D)
self.Exc = -trace2(D, self.K)
self.Eone = 2 * trace2(D, self.h)
self.F = self.h + 2 * self.J - self.K
self.energy = self.Eone + self.Ej + self.Exc + self.Enuke + self.entropy
return
def update(self,**opts):
from PyQuante.LA2 import trace2
from PyQuante.Ints import getJ,getK
if self.DoAveraging and self.dmat is not None:
self.F = self.Averager.getF(self.F,self.dmat)
self.dmat,self.entropy = self.solver.solve(self.F,**opts)
D = self.dmat
self.J = getJ(self.ERI,D)
self.Ej = 2*trace2(D,self.J)
self.K = getK(self.ERI,D)
self.Exc = -trace2(D,self.K)
self.Eone = 2*trace2(D,self.h)
self.F = self.h + 2*self.J - self.K
self.energy = self.Eone + self.Ej + self.Exc + self.Enuke + self.entropy
return
def update(self,**opts):
from PyQuante.LA2 import trace2
from PyQuante.Ints import getJ
from PyQuante.dft import getXC
self.dmat,self.entropy = self.solver.solve(self.F,**opts)
D = self.dmat
self.gr.setdens(D)
self.J = getJ(self.ERI,D)
self.Ej = 2*trace2(D,self.J)
self.Exc,self.XC = getXC(self.gr,self.nel,self.bfgrid,
functional=self.functional)
self.Eone = 2*trace2(D,self.h)
self.F = self.h+2*self.J+self.XC
self.energy = self.Eone + self.Ej + self.Exc + self.Enuke + self.entropy
return
def update(self, **kwargs):
from PyQuante.LA2 import trace2
from PyQuante.Ints import getJ, getK
self.amat, entropya = self.solvera.solve(self.Fa)
self.bmat, entropyb = self.solverb.solve(self.Fb)
Da = self.amat
Db = self.bmat
D = Da + Db
self.entropy = 0.5 * (entropya + entropyb)
self.J = getJ(self.ERI, D)
self.Ej = 0.5 * trace2(D, self.J)
self.Ka = getK(self.ERI, Da)
self.Kb = getK(self.ERI, Db)
self.Exc = -0.5 * (trace2(Da, self.Ka) + trace2(Db, self.Kb))
self.Eone = trace2(D, self.h)
self.Fa = self.h + self.J - self.Ka
self.Fb = self.h + self.J - self.Kb
self.energy = self.Eone + self.Ej + self.Exc + self.Enuke + self.entropy
return
def update(self,**opts):
from PyQuante.LA2 import trace2
from PyQuante.Ints import getJ,getK
self.amat,entropya = self.solvera.solve(self.Fa)
self.bmat,entropyb = self.solverb.solve(self.Fb)
Da = self.amat
Db = self.bmat
D = Da+Db
self.entropy = 0.5*(entropya+entropyb)
self.J = getJ(self.ERI,D)
self.Ej = 0.5*trace2(D,self.J)
self.Ka = getK(self.ERI,Da)
self.Kb = getK(self.ERI,Db)
self.Exc = -0.5*(trace2(Da,self.Ka)+trace2(Db,self.Kb))
self.Eone = trace2(D,self.h)
self.Fa = self.h + self.J - self.Ka
self.Fb = self.h + self.J - self.Kb
self.energy = self.Eone + self.Ej + self.Exc + self.Enuke + self.entropy
return
def __init__(self, solver):
# Solver is a pointer to a UHF calculation that has
# already converged
self.solver = solver
self.bfs = self.solver.bfs
self.nbf = len(self.bfs)
self.S = self.solver.S
self.h = self.solver.h
self.Ints = self.solver.Ints
self.molecule = self.solver.molecule
self.nel = self.molecule.get_nel()
self.nalpha, self.nbeta = self.molecule.get_alphabeta()
self.Enuke = self.molecule.get_enuke()
self.norb = self.nbf
self.orbsa = self.solver.orbsa
self.orbsb = self.solver.orbsb
self.orbea = self.solver.orbea
self.orbeb = self.solver.orbeb
self.Gij = []
for g in xrange(self.nbf):
gmat = zeros((self.nbf, self.nbf), 'd')
self.Gij.append(gmat)
gbf = self.bfs[g]
for i in xrange(self.nbf):
ibf = self.bfs[i]
for j in xrange(i + 1):
jbf = self.bfs[j]
gij = three_center(ibf, gbf, jbf)
gmat[i, j] = gij
gmat[j, i] = gij
D0 = mkdens(self.orbsa, 0, self.nalpha) + mkdens(
self.orbsb, 0, self.nbeta)
J0 = getJ(self.Ints, D0)
Vfa = ((self.nel - 1.) / self.nel) * J0
self.H0 = self.h + Vfa
self.b = zeros(2 * self.nbf, 'd')
return
def __init__(self,solver):
# Solver is a pointer to a UHF calculation that has
# already converged
self.solver = solver
self.bfs = self.solver.bfs
self.nbf = len(self.bfs)
self.S = self.solver.S
self.h = self.solver.h
self.Ints = self.solver.Ints
self.molecule = self.solver.molecule
self.nel = self.molecule.get_nel()
self.nalpha, self.nbeta = self.molecule.get_alphabeta()
self.Enuke = self.molecule.get_enuke()
self.norb = self.nbf
self.orbsa = self.solver.orbsa
self.orbsb = self.solver.orbsb
self.orbea = self.solver.orbea
self.orbeb = self.solver.orbeb
self.Gij = []
for g in range(self.nbf):
gmat = zeros((self.nbf,self.nbf),'d')
self.Gij.append(gmat)
gbf = self.bfs[g]
for i in range(self.nbf):
ibf = self.bfs[i]
for j in range(i+1):
jbf = self.bfs[j]
gij = three_center(ibf,gbf,jbf)
gmat[i,j] = gij
gmat[j,i] = gij
D0 = mkdens(self.orbsa,0,self.nalpha)+mkdens(self.orbsb,0,self.nbeta)
J0 = getJ(self.Ints,D0)
Vfa = ((self.nel-1.)/self.nel)*J0
self.H0 = self.h + Vfa
self.b = zeros(2*self.nbf,'d')
return
def oep_uhf_an(atoms,orbsa,orbsb,**opts):
"""oep_hf - Form the optimized effective potential for HF exchange.
Implementation of Wu and Yang's Approximate Newton Scheme
from J. Theor. Comp. Chem. 2, 627 (2003).
oep_uhf(atoms,orbs,**opts)
atoms A Molecule object containing a list of the atoms
orbs A matrix of guess orbitals
Options
-------
bfs None The basis functions to use for the wfn
pbfs None The basis functions to use for the pot
basis_data None The basis data to use to construct bfs
integrals None The one- and two-electron integrals to use
If not None, S,h,Ints
"""
maxiter = opts.get('maxiter',100)
tol = opts.get('tol',1e-5)
ETemp = opts.get('ETemp',False)
bfs = opts.get('bfs',None)
if not bfs:
basis = opts.get('basis',None)
bfs = getbasis(atoms,basis)
# The basis set for the potential can be set different from
# that used for the wave function
pbfs = opts.get('pbfs',None)
if not pbfs: pbfs = bfs
npbf = len(pbfs)
integrals = opts.get('integrals',None)
if integrals:
S,h,Ints = integrals
else:
S,h,Ints = getints(bfs,atoms)
nel = atoms.get_nel()
nclosed,nopen = atoms.get_closedopen()
nalpha,nbeta = nclosed+nopen,nclosed
Enuke = atoms.get_enuke()
# Form the OEP using Yang/Wu, PRL 89 143002 (2002)
nbf = len(bfs)
norb = nbf
ba = zeros(npbf,'d')
bb = zeros(npbf,'d')
# Form and store all of the three-center integrals
# we're going to need.
# These are <ibf|gbf|jbf> (where 'bf' indicates basis func,
# as opposed to MO)
# N^3 storage -- obviously you don't want to do this for
# very large systems
Gij = []
for g in range(npbf):
gmat = zeros((nbf,nbf),'d')
Gij.append(gmat)
gbf = pbfs[g]
for i in range(nbf):
ibf = bfs[i]
for j in range(i+1):
jbf = bfs[j]
gij = three_center(ibf,gbf,jbf)
gmat[i,j] = gij
gmat[j,i] = gij
# Compute the Fermi-Amaldi potential based on the LDA density.
# We're going to form this matrix from the Coulombic matrix that
# arises from the input orbitals. D0 and J0 refer to the density
# matrix and corresponding Coulomb matrix
D0 = mkdens(orbsa,0,nalpha)+mkdens(orbsb,0,nbeta)
J0 = getJ(Ints,D0)
Vfa = ((nel-1.)/nel)*J0
H0 = h + Vfa
eold = 0
for iter in range(maxiter):
Hoepa = get_Hoep(ba,H0,Gij)
Hoepb = get_Hoep(ba,H0,Gij)
orbea,orbsa = geigh(Hoepa,S)
orbeb,orbsb = geigh(Hoepb,S)
if ETemp:
efermia = get_efermi(2*nalpha,orbea,ETemp)
occsa = get_fermi_occs(efermia,orbea,ETemp)
Da = mkdens_occs(orbsa,occsa)
efermib = get_efermi(2*nbeta,orbeb,ETemp)
occsb = get_fermi_occs(efermib,orbeb,ETemp)
Db = mkdens_occs(orbsb,occsb)
entropy = 0.5*(get_entropy(occsa,ETemp)+get_entropy(occsb,ETemp))
else:
Da = mkdens(orbsa,0,nalpha)
Db = mkdens(orbsb,0,nbeta)
J = getJ(Ints,Da) + getJ(Ints,Db)
Ka = getK(Ints,Da)
Kb = getK(Ints,Db)
energy = (trace2(2*h+J-Ka,Da)+trace2(2*h+J-Kb,Db))/2\
+Enuke
if ETemp: energy += entropy
if abs(energy-eold) < tol:
break
else:
eold = energy
logging.debug("OEP AN Opt: %d %f" % (iter,energy))
# Do alpha and beta separately
# Alphas
dV_ao = J-Ka-Vfa
dV = matrixmultiply(orbsa,matrixmultiply(dV_ao,transpose(orbsa)))
X = zeros((nbf,nbf),'d')
c = zeros(nbf,'d')
for k in range(nbf):
Gk = matrixmultiply(orbsa,matrixmultiply(Gij[k],
transpose(orbsa)))
for i in range(nalpha):
for a in range(nalpha,norb):
c[k] += dV[i,a]*Gk[i,a]/(orbea[i]-orbea[a])
for l in range(nbf):
Gl = matrixmultiply(orbsa,matrixmultiply(Gij[l],
transpose(orbsa)))
for i in range(nalpha):
for a in range(nalpha,norb):
X[k,l] += Gk[i,a]*Gl[i,a]/(orbea[i]-orbea[a])
# This should actually be a pseudoinverse...
ba = solve(X,c)
# Betas
dV_ao = J-Kb-Vfa
dV = matrixmultiply(orbsb,matrixmultiply(dV_ao,transpose(orbsb)))
X = zeros((nbf,nbf),'d')
c = zeros(nbf,'d')
for k in range(nbf):
Gk = matrixmultiply(orbsb,matrixmultiply(Gij[k],
transpose(orbsb)))
for i in range(nbeta):
for a in range(nbeta,norb):
c[k] += dV[i,a]*Gk[i,a]/(orbeb[i]-orbeb[a])
for l in range(nbf):
Gl = matrixmultiply(orbsb,matrixmultiply(Gij[l],
transpose(orbsb)))
for i in range(nbeta):
for a in range(nbeta,norb):
X[k,l] += Gk[i,a]*Gl[i,a]/(orbeb[i]-orbeb[a])
# This should actually be a pseudoinverse...
bb = solve(X,c)
logging.info("Final OEP energy = %f" % energy)
return energy,(orbea,orbeb),(orbsa,orbsb)
def pyq1_rohf(atomtuples=[(2,(0,0,0))],basis = '6-31G**',maxit=10,mult=3):
from PyQuante import Ints,settings,Molecule
from PyQuante.hartree_fock import get_energy
from PyQuante.MG2 import MG2 as MolecularGrid
from PyQuante.LA2 import mkdens,geigh,trace2,simx
from PyQuante.Ints import getJ,getK
print ("PyQ1 ROHF run")
atoms = Molecule('Pyq1',atomlist=atomtuples,multiplicity=mult)
bfs = Ints.getbasis(atoms,basis=basis)
S,h,I2e = Ints.getints(bfs,atoms)
nbf = norbs = len(bfs)
nel = atoms.get_nel()
nalpha,nbeta = atoms.get_alphabeta()
enuke = atoms.get_enuke()
orbe,orbs = geigh(h,S)
eold = 0
for i in range(maxit):
Da = mkdens(orbs,0,nalpha)
Db = mkdens(orbs,0,nbeta)
Ja = getJ(I2e,Da)
Jb = getJ(I2e,Db)
Ka = getK(I2e,Da)
Kb = getK(I2e,Db)
Fa = h+Ja+Jb-Ka
Fb = h+Ja+Jb-Kb
energya = get_energy(h,Fa,Da)
energyb = get_energy(h,Fb,Db)
eone = (trace2(Da,h) + trace2(Db,h))/2
etwo = (trace2(Da,Fa) + trace2(Db,Fb))/2
energy = (energya+energyb)/2 + enuke
print (i,energy,eone,etwo,enuke)
if abs(energy-eold) < 1e-5: break
eold = energy
Fa = simx(Fa,orbs)
Fb = simx(Fb,orbs)
# Building the approximate Fock matrices in the MO basis
F = 0.5*(Fa+Fb)
K = Fb-Fa
# The Fock matrix now looks like
# F-K | F + K/2 | F
# ---------------------------------
# F + K/2 | F | F - K/2
# ---------------------------------
# F | F - K/2 | F + K
# Make explicit slice objects to simplify this
do = slice(0,nbeta)
so = slice(nbeta,nalpha)
uo = slice(nalpha,norbs)
F[do,do] -= K[do,do]
F[uo,uo] += K[uo,uo]
F[do,so] += 0.5*K[do,so]
F[so,do] += 0.5*K[so,do]
F[so,uo] -= 0.5*K[so,uo]
F[uo,so] -= 0.5*K[uo,so]
orbe,mo_orbs = np.linalg.eigh(F)
orbs = np.dot(orbs,mo_orbs)
return energy,orbe,orbs
def oep(atoms,orbs,energy_func,grad_func=None,**opts):
"""oep - Form the optimized effective potential for a given energy expression
oep(atoms,orbs,energy_func,grad_func=None,**opts)
atoms A Molecule object containing a list of the atoms
orbs A matrix of guess orbitals
energy_func The function that returns the energy for the given method
grad_func The function that returns the force for the given method
Options
-------
verbose False Output terse information to stdout (default)
True Print out additional information
ETemp False Use ETemp value for finite temperature DFT (default)
float Use (float) for the electron temperature
bfs None The basis functions to use. List of CGBF's
basis_data None The basis data to use to construct bfs
integrals None The one- and two-electron integrals to use
If not None, S,h,Ints
"""
verbose = opts.get('verbose',False)
ETemp = opts.get('ETemp',False)
opt_method = opts.get('opt_method','BFGS')
bfs = opts.get('bfs',None)
if not bfs:
basis = opts.get('basis',None)
bfs = getbasis(atoms,basis)
# The basis set for the potential can be set different from
# that used for the wave function
pbfs = opts.get('pbfs',None)
if not pbfs: pbfs = bfs
npbf = len(pbfs)
integrals = opts.get('integrals',None)
if integrals:
S,h,Ints = integrals
else:
S,h,Ints = getints(bfs,atoms)
nel = atoms.get_nel()
nocc,nopen = atoms.get_closedopen()
Enuke = atoms.get_enuke()
# Form the OEP using Yang/Wu, PRL 89 143002 (2002)
nbf = len(bfs)
norb = nbf
bp = zeros(nbf,'d')
bvec = opts.get('bvec',None)
if bvec:
assert len(bvec) == npbf
b = array(bvec)
else:
b = zeros(npbf,'d')
# Form and store all of the three-center integrals
# we're going to need.
# These are <ibf|gbf|jbf> (where 'bf' indicates basis func,
# as opposed to MO)
# N^3 storage -- obviously you don't want to do this for
# very large systems
Gij = []
for g in range(npbf):
gmat = zeros((nbf,nbf),'d')
Gij.append(gmat)
gbf = pbfs[g]
for i in range(nbf):
ibf = bfs[i]
for j in range(i+1):
jbf = bfs[j]
gij = three_center(ibf,gbf,jbf)
gmat[i,j] = gij
gmat[j,i] = gij
# Compute the Fermi-Amaldi potential based on the LDA density.
# We're going to form this matrix from the Coulombic matrix that
# arises from the input orbitals. D0 and J0 refer to the density
# matrix and corresponding Coulomb matrix
D0 = mkdens(orbs,0,nocc)
J0 = getJ(Ints,D0)
Vfa = (2*(nel-1.)/nel)*J0
H0 = h + Vfa
b = fminBFGS(energy_func,b,grad_func,
(nbf,nel,nocc,ETemp,Enuke,S,h,Ints,H0,Gij),
logger=logging)
energy,orbe,orbs = energy_func(b,nbf,nel,nocc,ETemp,Enuke,
S,h,Ints,H0,Gij,return_flag=1)
return energy,orbe,orbs
def oep_hf_an(atoms,orbs,**opts):
"""oep_hf - Form the optimized effective potential for HF exchange.
Implementation of Wu and Yang's Approximate Newton Scheme
from J. Theor. Comp. Chem. 2, 627 (2003).
oep_hf(atoms,orbs,**opts)
atoms A Molecule object containing a list of the atoms
orbs A matrix of guess orbitals
Options
-------
bfs None The basis functions to use for the wfn
pbfs None The basis functions to use for the pot
basis_data None The basis data to use to construct bfs
integrals None The one- and two-electron integrals to use
If not None, S,h,Ints
"""
maxiter = opts.get('maxiter',100)
tol = opts.get('tol',1e-5)
bfs = opts.get('bfs',None)
if not bfs:
basis = opts.get('basis',None)
bfs = getbasis(atoms,basis)
# The basis set for the potential can be set different from
# that used for the wave function
pbfs = opts.get('pbfs',None)
if not pbfs: pbfs = bfs
npbf = len(pbfs)
integrals = opts.get('integrals',None)
if integrals:
S,h,Ints = integrals
else:
S,h,Ints = getints(bfs,atoms)
nel = atoms.get_nel()
nocc,nopen = atoms.get_closedopen()
Enuke = atoms.get_enuke()
# Form the OEP using Yang/Wu, PRL 89 143002 (2002)
nbf = len(bfs)
norb = nbf
bp = zeros(nbf,'d')
bvec = opts.get('bvec',None)
if bvec:
assert len(bvec) == npbf
b = array(bvec)
else:
b = zeros(npbf,'d')
# Form and store all of the three-center integrals
# we're going to need.
# These are <ibf|gbf|jbf> (where 'bf' indicates basis func,
# as opposed to MO)
# N^3 storage -- obviously you don't want to do this for
# very large systems
Gij = []
for g in range(npbf):
gmat = zeros((nbf,nbf),'d')
Gij.append(gmat)
gbf = pbfs[g]
for i in range(nbf):
ibf = bfs[i]
for j in range(i+1):
jbf = bfs[j]
gij = three_center(ibf,gbf,jbf)
gmat[i,j] = gij
gmat[j,i] = gij
# Compute the Fermi-Amaldi potential based on the LDA density.
# We're going to form this matrix from the Coulombic matrix that
# arises from the input orbitals. D0 and J0 refer to the density
# matrix and corresponding Coulomb matrix
D0 = mkdens(orbs,0,nocc)
J0 = getJ(Ints,D0)
Vfa = (2*(nel-1.)/nel)*J0
H0 = h + Vfa
b = zeros(nbf,'d')
eold = 0
for iter in range(maxiter):
Hoep = get_Hoep(b,H0,Gij)
orbe,orbs = geigh(Hoep,S)
D = mkdens(orbs,0,nocc)
Vhf = get2JmK(Ints,D)
energy = trace2(2*h+Vhf,D)+Enuke
if abs(energy-eold) < tol:
break
else:
eold = energy
logging.debug("OEP AN Opt: %d %f" % (iter,energy))
dV_ao = Vhf-Vfa
dV = matrixmultiply(transpose(orbs),matrixmultiply(dV_ao,orbs))
X = zeros((nbf,nbf),'d')
c = zeros(nbf,'d')
Gkt = zeros((nbf,nbf),'d')
for k in range(nbf):
# This didn't work; in fact, it made things worse:
Gk = matrixmultiply(transpose(orbs),matrixmultiply(Gij[k],orbs))
for i in range(nocc):
for a in range(nocc,norb):
c[k] += dV[i,a]*Gk[i,a]/(orbe[i]-orbe[a])
for l in range(nbf):
Gl = matrixmultiply(transpose(orbs),matrixmultiply(Gij[l],orbs))
for i in range(nocc):
for a in range(nocc,norb):
X[k,l] += Gk[i,a]*Gl[i,a]/(orbe[i]-orbe[a])
# This should actually be a pseudoinverse...
b = solve(X,c)
logging.info("Final OEP energy = %f" % energy)
return energy,orbe,orbs
def oep(atoms, orbs, energy_func, grad_func=None, **opts):
"""oep - Form the optimized effective potential for a given energy expression
oep(atoms,orbs,energy_func,grad_func=None,**opts)
atoms A Molecule object containing a list of the atoms
orbs A matrix of guess orbitals
energy_func The function that returns the energy for the given method
grad_func The function that returns the force for the given method
Options
-------
verbose False Output terse information to stdout (default)
True Print out additional information
ETemp False Use ETemp value for finite temperature DFT (default)
float Use (float) for the electron temperature
bfs None The basis functions to use. List of CGBF's
basis_data None The basis data to use to construct bfs
integrals None The one- and two-electron integrals to use
If not None, S,h,Ints
"""
verbose = opts.get('verbose', False)
ETemp = opts.get('ETemp', False)
opt_method = opts.get('opt_method', 'BFGS')
bfs = opts.get('bfs', None)
if not bfs:
basis = opts.get('basis', None)
bfs = getbasis(atoms, basis)
# The basis set for the potential can be set different from
# that used for the wave function
pbfs = opts.get('pbfs', None)
if not pbfs: pbfs = bfs
npbf = len(pbfs)
integrals = opts.get('integrals', None)
if integrals:
S, h, Ints = integrals
else:
S, h, Ints = getints(bfs, atoms)
nel = atoms.get_nel()
nocc, nopen = atoms.get_closedopen()
Enuke = atoms.get_enuke()
# Form the OEP using Yang/Wu, PRL 89 143002 (2002)
nbf = len(bfs)
norb = nbf
bp = zeros(nbf, 'd')
bvec = opts.get('bvec', None)
if bvec:
assert len(bvec) == npbf
b = array(bvec)
else:
b = zeros(npbf, 'd')
# Form and store all of the three-center integrals
# we're going to need.
# These are <ibf|gbf|jbf> (where 'bf' indicates basis func,
# as opposed to MO)
# N^3 storage -- obviously you don't want to do this for
# very large systems
Gij = []
for g in xrange(npbf):
gmat = zeros((nbf, nbf), 'd')
Gij.append(gmat)
gbf = pbfs[g]
for i in xrange(nbf):
ibf = bfs[i]
for j in xrange(i + 1):
jbf = bfs[j]
gij = three_center(ibf, gbf, jbf)
gmat[i, j] = gij
gmat[j, i] = gij
# Compute the Fermi-Amaldi potential based on the LDA density.
# We're going to form this matrix from the Coulombic matrix that
# arises from the input orbitals. D0 and J0 refer to the density
# matrix and corresponding Coulomb matrix
D0 = mkdens(orbs, 0, nocc)
J0 = getJ(Ints, D0)
Vfa = (2 * (nel - 1.) / nel) * J0
H0 = h + Vfa
b = fminBFGS(energy_func,
b,
grad_func,
(nbf, nel, nocc, ETemp, Enuke, S, h, Ints, H0, Gij),
logger=logging)
energy, orbe, orbs = energy_func(b,
nbf,
nel,
nocc,
ETemp,
Enuke,
S,
h,
Ints,
H0,
Gij,
return_flag=1)
return energy, orbe, orbs
def oep_hf_an(atoms, orbs, **opts):
"""oep_hf - Form the optimized effective potential for HF exchange.
Implementation of Wu and Yang's Approximate Newton Scheme
from J. Theor. Comp. Chem. 2, 627 (2003).
oep_hf(atoms,orbs,**opts)
atoms A Molecule object containing a list of the atoms
orbs A matrix of guess orbitals
Options
-------
bfs None The basis functions to use for the wfn
pbfs None The basis functions to use for the pot
basis_data None The basis data to use to construct bfs
integrals None The one- and two-electron integrals to use
If not None, S,h,Ints
"""
maxiter = opts.get('maxiter', 100)
tol = opts.get('tol', 1e-5)
bfs = opts.get('bfs', None)
if not bfs:
basis = opts.get('basis', None)
bfs = getbasis(atoms, basis)
# The basis set for the potential can be set different from
# that used for the wave function
pbfs = opts.get('pbfs', None)
if not pbfs: pbfs = bfs
npbf = len(pbfs)
integrals = opts.get('integrals', None)
if integrals:
S, h, Ints = integrals
else:
S, h, Ints = getints(bfs, atoms)
nel = atoms.get_nel()
nocc, nopen = atoms.get_closedopen()
Enuke = atoms.get_enuke()
# Form the OEP using Yang/Wu, PRL 89 143002 (2002)
nbf = len(bfs)
norb = nbf
bp = zeros(nbf, 'd')
bvec = opts.get('bvec', None)
if bvec:
assert len(bvec) == npbf
b = array(bvec)
else:
b = zeros(npbf, 'd')
# Form and store all of the three-center integrals
# we're going to need.
# These are <ibf|gbf|jbf> (where 'bf' indicates basis func,
# as opposed to MO)
# N^3 storage -- obviously you don't want to do this for
# very large systems
Gij = []
for g in xrange(npbf):
gmat = zeros((nbf, nbf), 'd')
Gij.append(gmat)
gbf = pbfs[g]
for i in xrange(nbf):
ibf = bfs[i]
for j in xrange(i + 1):
jbf = bfs[j]
gij = three_center(ibf, gbf, jbf)
gmat[i, j] = gij
gmat[j, i] = gij
# Compute the Fermi-Amaldi potential based on the LDA density.
# We're going to form this matrix from the Coulombic matrix that
# arises from the input orbitals. D0 and J0 refer to the density
# matrix and corresponding Coulomb matrix
D0 = mkdens(orbs, 0, nocc)
J0 = getJ(Ints, D0)
Vfa = (2 * (nel - 1.) / nel) * J0
H0 = h + Vfa
b = zeros(nbf, 'd')
eold = 0
for iter in xrange(maxiter):
Hoep = get_Hoep(b, H0, Gij)
orbe, orbs = geigh(Hoep, S)
D = mkdens(orbs, 0, nocc)
Vhf = get2JmK(Ints, D)
energy = trace2(2 * h + Vhf, D) + Enuke
if abs(energy - eold) < tol:
break
else:
eold = energy
logging.debug("OEP AN Opt: %d %f" % (iter, energy))
dV_ao = Vhf - Vfa
dV = matrixmultiply(transpose(orbs), matrixmultiply(dV_ao, orbs))
X = zeros((nbf, nbf), 'd')
c = zeros(nbf, 'd')
Gkt = zeros((nbf, nbf), 'd')
for k in xrange(nbf):
# This didn't work; in fact, it made things worse:
Gk = matrixmultiply(transpose(orbs), matrixmultiply(Gij[k], orbs))
for i in xrange(nocc):
for a in xrange(nocc, norb):
c[k] += dV[i, a] * Gk[i, a] / (orbe[i] - orbe[a])
for l in xrange(nbf):
Gl = matrixmultiply(transpose(orbs),
matrixmultiply(Gij[l], orbs))
for i in xrange(nocc):
for a in xrange(nocc, norb):
X[k, l] += Gk[i, a] * Gl[i, a] / (orbe[i] - orbe[a])
# This should actually be a pseudoinverse...
b = solve(X, c)
logger.info("Final OEP energy = %f" % energy)
return energy, orbe, orbs
def pyq1_rohf(atomtuples=[(2, (0, 0, 0))], basis='6-31G**', maxit=10, mult=3):
from PyQuante import Ints, settings, Molecule
from PyQuante.hartree_fock import get_energy
from PyQuante.MG2 import MG2 as MolecularGrid
from PyQuante.LA2 import mkdens, geigh, trace2, simx
from PyQuante.Ints import getJ, getK
print("PyQ1 ROHF run")
atoms = Molecule('Pyq1', atomlist=atomtuples, multiplicity=mult)
bfs = Ints.getbasis(atoms, basis=basis)
S, h, I2e = Ints.getints(bfs, atoms)
nbf = norbs = len(bfs)
nel = atoms.get_nel()
nalpha, nbeta = atoms.get_alphabeta()
enuke = atoms.get_enuke()
orbe, orbs = geigh(h, S)
eold = 0
for i in range(maxit):
Da = mkdens(orbs, 0, nalpha)
Db = mkdens(orbs, 0, nbeta)
Ja = getJ(I2e, Da)
Jb = getJ(I2e, Db)
Ka = getK(I2e, Da)
Kb = getK(I2e, Db)
Fa = h + Ja + Jb - Ka
Fb = h + Ja + Jb - Kb
energya = get_energy(h, Fa, Da)
energyb = get_energy(h, Fb, Db)
eone = (trace2(Da, h) + trace2(Db, h)) / 2
etwo = (trace2(Da, Fa) + trace2(Db, Fb)) / 2
energy = (energya + energyb) / 2 + enuke
print(i, energy, eone, etwo, enuke)
if abs(energy - eold) < 1e-5: break
eold = energy
Fa = simx(Fa, orbs)
Fb = simx(Fb, orbs)
# Building the approximate Fock matrices in the MO basis
F = 0.5 * (Fa + Fb)
K = Fb - Fa
# The Fock matrix now looks like
# F-K | F + K/2 | F
# ---------------------------------
# F + K/2 | F | F - K/2
# ---------------------------------
# F | F - K/2 | F + K
# Make explicit slice objects to simplify this
do = slice(0, nbeta)
so = slice(nbeta, nalpha)
uo = slice(nalpha, norbs)
F[do, do] -= K[do, do]
F[uo, uo] += K[uo, uo]
F[do, so] += 0.5 * K[do, so]
F[so, do] += 0.5 * K[so, do]
F[so, uo] -= 0.5 * K[so, uo]
F[uo, so] -= 0.5 * K[uo, so]
orbe, mo_orbs = np.linalg.eigh(F)
orbs = np.dot(orbs, mo_orbs)
return energy, orbe, orbs
def oep_uhf_an(atoms, orbsa, orbsb, **opts):
"""oep_hf - Form the optimized effective potential for HF exchange.
Implementation of Wu and Yang's Approximate Newton Scheme
from J. Theor. Comp. Chem. 2, 627 (2003).
oep_uhf(atoms,orbs,**opts)
atoms A Molecule object containing a list of the atoms
orbs A matrix of guess orbitals
Options
-------
bfs None The basis functions to use for the wfn
pbfs None The basis functions to use for the pot
basis_data None The basis data to use to construct bfs
integrals None The one- and two-electron integrals to use
If not None, S,h,Ints
"""
maxiter = opts.get('maxiter', 100)
tol = opts.get('tol', 1e-5)
ETemp = opts.get('ETemp', False)
bfs = opts.get('bfs', None)
if not bfs:
basis = opts.get('basis', None)
bfs = getbasis(atoms, basis)
# The basis set for the potential can be set different from
# that used for the wave function
pbfs = opts.get('pbfs', None)
if not pbfs: pbfs = bfs
npbf = len(pbfs)
integrals = opts.get('integrals', None)
if integrals:
S, h, Ints = integrals
else:
S, h, Ints = getints(bfs, atoms)
nel = atoms.get_nel()
nclosed, nopen = atoms.get_closedopen()
nalpha, nbeta = nclosed + nopen, nclosed
Enuke = atoms.get_enuke()
# Form the OEP using Yang/Wu, PRL 89 143002 (2002)
nbf = len(bfs)
norb = nbf
ba = zeros(npbf, 'd')
bb = zeros(npbf, 'd')
# Form and store all of the three-center integrals
# we're going to need.
# These are <ibf|gbf|jbf> (where 'bf' indicates basis func,
# as opposed to MO)
# N^3 storage -- obviously you don't want to do this for
# very large systems
Gij = []
for g in xrange(npbf):
gmat = zeros((nbf, nbf), 'd')
Gij.append(gmat)
gbf = pbfs[g]
for i in xrange(nbf):
ibf = bfs[i]
for j in xrange(i + 1):
jbf = bfs[j]
gij = three_center(ibf, gbf, jbf)
gmat[i, j] = gij
gmat[j, i] = gij
# Compute the Fermi-Amaldi potential based on the LDA density.
# We're going to form this matrix from the Coulombic matrix that
# arises from the input orbitals. D0 and J0 refer to the density
# matrix and corresponding Coulomb matrix
D0 = mkdens(orbsa, 0, nalpha) + mkdens(orbsb, 0, nbeta)
J0 = getJ(Ints, D0)
Vfa = ((nel - 1.) / nel) * J0
H0 = h + Vfa
eold = 0
for iter in xrange(maxiter):
Hoepa = get_Hoep(ba, H0, Gij)
Hoepb = get_Hoep(ba, H0, Gij)
orbea, orbsa = geigh(Hoepa, S)
orbeb, orbsb = geigh(Hoepb, S)
if ETemp:
efermia = get_efermi(2 * nalpha, orbea, ETemp)
occsa = get_fermi_occs(efermia, orbea, ETemp)
Da = mkdens_occs(orbsa, occsa)
efermib = get_efermi(2 * nbeta, orbeb, ETemp)
occsb = get_fermi_occs(efermib, orbeb, ETemp)
Db = mkdens_occs(orbsb, occsb)
entropy = 0.5 * (get_entropy(occsa, ETemp) +
get_entropy(occsb, ETemp))
else:
Da = mkdens(orbsa, 0, nalpha)
Db = mkdens(orbsb, 0, nbeta)
J = getJ(Ints, Da) + getJ(Ints, Db)
Ka = getK(Ints, Da)
Kb = getK(Ints, Db)
energy = (trace2(2*h+J-Ka,Da)+trace2(2*h+J-Kb,Db))/2\
+Enuke
if ETemp: energy += entropy
if abs(energy - eold) < tol:
break
else:
eold = energy
logging.debug("OEP AN Opt: %d %f" % (iter, energy))
# Do alpha and beta separately
# Alphas
dV_ao = J - Ka - Vfa
dV = matrixmultiply(orbsa, matrixmultiply(dV_ao, transpose(orbsa)))
X = zeros((nbf, nbf), 'd')
c = zeros(nbf, 'd')
for k in xrange(nbf):
Gk = matrixmultiply(orbsa, matrixmultiply(Gij[k],
transpose(orbsa)))
for i in xrange(nalpha):
for a in xrange(nalpha, norb):
c[k] += dV[i, a] * Gk[i, a] / (orbea[i] - orbea[a])
for l in xrange(nbf):
Gl = matrixmultiply(orbsa,
matrixmultiply(Gij[l], transpose(orbsa)))
for i in xrange(nalpha):
for a in xrange(nalpha, norb):
X[k, l] += Gk[i, a] * Gl[i, a] / (orbea[i] - orbea[a])
# This should actually be a pseudoinverse...
ba = solve(X, c)
# Betas
dV_ao = J - Kb - Vfa
dV = matrixmultiply(orbsb, matrixmultiply(dV_ao, transpose(orbsb)))
X = zeros((nbf, nbf), 'd')
c = zeros(nbf, 'd')
for k in xrange(nbf):
Gk = matrixmultiply(orbsb, matrixmultiply(Gij[k],
transpose(orbsb)))
for i in xrange(nbeta):
for a in xrange(nbeta, norb):
c[k] += dV[i, a] * Gk[i, a] / (orbeb[i] - orbeb[a])
for l in xrange(nbf):
Gl = matrixmultiply(orbsb,
matrixmultiply(Gij[l], transpose(orbsb)))
for i in xrange(nbeta):
for a in xrange(nbeta, norb):
X[k, l] += Gk[i, a] * Gl[i, a] / (orbeb[i] - orbeb[a])
# This should actually be a pseudoinverse...
bb = solve(X, c)
logger.info("Final OEP energy = %f" % energy)
return energy, (orbea, orbeb), (orbsa, orbsb)
def rohf(atoms,**opts):
"""\
rohf(atoms,**opts) - Restriced Open Shell Hartree Fock
atoms A Molecule object containing the molecule
"""
ConvCriteria = opts.get('ConvCriteria',1e-5)
MaxIter = opts.get('MaxIter',40)
DoAveraging = opts.get('DoAveraging',True)
averaging = opts.get('averaging',0.95)
verbose = opts.get('verbose',True)
bfs = opts.get('bfs',None)
if not bfs:
basis_data = opts.get('basis_data',None)
bfs = getbasis(atoms,basis_data)
nbf = len(bfs)
integrals = opts.get('integrals', None)
if integrals:
S,h,Ints = integrals
else:
S,h,Ints = getints(bfs,atoms)
nel = atoms.get_nel()
nalpha,nbeta = atoms.get_alphabeta()
S,h,Ints = getints(bfs,atoms)
orbs = opts.get('orbs',None)
if orbs is None:
orbe,orbs = geigh(h,S)
norbs = nbf
enuke = atoms.get_enuke()
eold = 0.
if verbose: print "ROHF calculation on %s" % atoms.name
if verbose: print "Nbf = %d" % nbf
if verbose: print "Nalpha = %d" % nalpha
if verbose: print "Nbeta = %d" % nbeta
if verbose: print "Averaging = %s" % DoAveraging
print "Optimization of HF orbitals"
for i in xrange(MaxIter):
if verbose: print "SCF Iteration:",i,"Starting Energy:",eold
Da = mkdens(orbs,0,nalpha)
Db = mkdens(orbs,0,nbeta)
if DoAveraging:
if i:
Da = averaging*Da + (1-averaging)*Da0
Db = averaging*Db + (1-averaging)*Db0
Da0 = Da
Db0 = Db
Ja = getJ(Ints,Da)
Jb = getJ(Ints,Db)
Ka = getK(Ints,Da)
Kb = getK(Ints,Db)
Fa = h+Ja+Jb-Ka
Fb = h+Ja+Jb-Kb
energya = get_energy(h,Fa,Da)
energyb = get_energy(h,Fb,Db)
eone = (trace2(Da,h) + trace2(Db,h))/2
etwo = (trace2(Da,Fa) + trace2(Db,Fb))/2
energy = (energya+energyb)/2 + enuke
print i,energy,eone,etwo,enuke
if abs(energy-eold) < ConvCriteria: break
eold = energy
Fa = ao2mo(Fa,orbs)
Fb = ao2mo(Fb,orbs)
# Building the approximate Fock matrices in the MO basis
F = 0.5*(Fa+Fb)
K = Fb-Fa
# The Fock matrix now looks like
# F-K | F + K/2 | F
# ---------------------------------
# F + K/2 | F | F - K/2
# ---------------------------------
# F | F - K/2 | F + K
# Make explicit slice objects to simplify this
do = slice(0,nbeta)
so = slice(nbeta,nalpha)
uo = slice(nalpha,norbs)
F[do,do] -= K[do,do]
F[uo,uo] += K[uo,uo]
F[do,so] += 0.5*K[do,so]
F[so,do] += 0.5*K[so,do]
F[so,uo] -= 0.5*K[so,uo]
F[uo,so] -= 0.5*K[uo,so]
orbe,mo_orbs = eigh(F)
orbs = matrixmultiply(orbs,mo_orbs)
if verbose:
print "Final ROHF energy for system %s is %f" % (atoms.name,energy)
return energy,orbe,orbs
