def compute_CCD(Settings, silent=False, compare=True):
t0 = time.time()
printif = print if not silent else lambda *k, **w: None
Escf, Ca, Cb, epsa, epsb, _, g, Vnuc = compute_uhf(Settings,
return_C=True,
return_integrals=True,
silent=silent)
printif("""
=======================================================
Spin-Orbital Coupled Cluster Doubles
{}
=======================================================
""".format('\U0001F9D9'))
tsave = []
show_progress(tsave, printif)
t = time.time()
# Create Spin-Orbital arrays
C = np.block([[Ca, np.zeros(Ca.shape)], [np.zeros(Cb.shape), Cb]])
eps = np.concatenate((epsa, epsb))
g = np.kron(np.eye(2), g)
g = np.kron(np.eye(2), g.T)
# re-Sorting orbitals
s = np.argsort(eps)
eps = eps[s]
C = C[:, s]
tsave.append(time.time() - t)
show_progress(tsave, printif)
t = time.time()
# Get the MO integral
nelec = Settings['nalpha'] + Settings['nbeta']
# Converto to Physicists notation
g = g.transpose(0, 2, 1, 3)
# Antisymmetrize
g = g - g.transpose(0, 1, 3, 2)
# Save Slices
ERI = lambda a, b, c, d: np.einsum('ap,bq,cr,ds,abcd->pqrs',
C[:, a],
C[:, b],
C[:, c],
C[:, d],
g,
optimize='optimal')
o = slice(0, nelec)
v = slice(nelec, len(g))
Voovv = ERI(o, o, v, v)
Voooo = ERI(o, o, o, o)
Vvvvv = ERI(v, v, v, v)
Vvoov = ERI(v, o, o, v)
g = None
tsave.append(time.time() - t)
show_progress(tsave, printif)
t = time.time()
# Get eigenvalues Matrix D
new = np.newaxis
eo = eps[:nelec]
ev = eps[nelec:]
D = 1.0 / (eo[:, new, new, new] + eo[new, :, new, new] -
ev[new, new, :, new] - ev[new, new, new, :])
tsave.append(time.time() - t)
show_progress(tsave, printif)
t = time.time()
# Initial guess for amplitudes
T = Voovv * D
# Get MP2 energy
E = CCD_Energy(T, Voovv)
printif('\nMP2 Energy: {:<15.10f}\n'.format(E + Escf))
# Setup iteration options
rms = 0.0
dE = 1
ite = 1
rms_LIM = 10**(-8)
E_LIM = 10**(-12)
t0 = time.time()
printif('\U00003030' * 20)
# Start CC iterations
while abs(dE) > E_LIM or rms > rms_LIM:
t = time.time()
if ite > Settings["cc_max_iter"]:
raise NameError('CCD equations did not converge')
T, rms = CCD_Amplitude(T, D, Voovv, Voooo, Vvvvv, Vvoov)
dE = -E
E = CCD_Energy(T, Voovv)
dE += E
printif("Iteration {}".format(numoji(ite)))
printif("\U000027B0 Correlation energy: {:< 15.10f}".format(E))
printif("\U0001F53A Energy change: {:< 15.10f}".format(dE))
printif("\U00002622 Max RMS residue: {:< 15.10f}".format(rms))
printif("\U0001F570 Time required: {:< 15.10f}".format(
time.time() - t))
printif('\U00003030' * 20)
ite += 1
printif('\U0001F3C1 CCD Energy: {:<15.10f}'.format(E + Escf))
printif('\U000023F3 CCD iterations took %.2f seconds.\n' %
(time.time() - t0))
return (E + Escf)
from input import Settings
if 'active_space' in Settings:
print('TEST')
from CI import compute_CI
compute_CI(Settings)
else:
from uhf import compute_uhf
compute_uhf(Settings)
def compute_SOMP2(Settings, silent=False, psi4compare=True):
printif = print if not silent else lambda *k, **w: None
Escf, Ca, Cb, epsa, epsb, _, g, Vnuc = compute_uhf(Settings,
return_C=True,
return_integrals=True,
psi4compare=psi4compare,
silent=silent)
printif("""
===================================================
Spin-Orbital Møller-Plesset Perturbation Theory
{}
===================================================
""".format(emoji('wine')))
tasks = """
Building:
\U0001F426 Spin-Orbital arrays {}
\U0001F986 MO integral <OO|VV> {}
\U0001F9A2 Eigenvalues auxiliar array Dijab {}"""
printif(tasks.format('', '', ''))
if not silent: sys.stdout.write("\033[F" * 5)
time.sleep(0.1)
# Create Spin-Orbital arrays
C = np.block([[Ca, np.zeros(Ca.shape)], [np.zeros(Cb.shape), Cb]])
eps = np.concatenate((epsa, epsb))
g = np.kron(np.eye(2), g)
g = np.kron(np.eye(2), g.T)
# re-Sorting orbitals
s = np.argsort(eps)
eps = eps[s]
C = C[:, s]
printif(tasks.format(emoji('check'), '', ''))
if not silent: sys.stdout.write("\033[F" * 5)
time.sleep(0.1)
# Get the MO integral OOVV
nelec = Settings['nalpha'] + Settings['nbeta']
# Converto to Physicists notation
g = g.transpose(0, 2, 1, 3)
ERI = np.einsum('ap,bq,cr,ds,abcd->pqrs',
C[:, :nelec],
C[:, :nelec],
C[:, nelec:],
C[:, nelec:],
g,
optimize='optimal')
# Antisymmetrize
ERI = ERI - ERI.transpose(0, 1, 3, 2)
printif(tasks.format(emoji('check'), emoji('check'), ''))
if not silent: sys.stdout.write("\033[F" * 5)
time.sleep(0.1)
# Get eigenvalues Matrix D
new = np.newaxis
eo = eps[:nelec]
ev = eps[nelec:]
D = 1.0 / (eo[:, new, new, new] + eo[new, :, new, new] -
ev[new, new, :, new] - ev[new, new, new, :])
printif(tasks.format(emoji('check'), emoji('check'), emoji('check')))
time.sleep(0.1)
# Get MP2 energy
printif('\n{} Computing MP2 energy'.format('\U0001F9ED'), end=' ')
Emp2 = (1.0 / 4.0) * np.einsum('ijab,ijab,ijab->', ERI, ERI, D)
printif(emoji('check'))
printif('\n{} MP2 Correlation Energy: {:>16.10f}'.format(
emoji('bolt'), Emp2))
printif('\U0001F308 Final Electronic Energy: {:>16.10f}'.format(Emp2 +
Escf))
if psi4compare:
psi4.set_options({
'basis': Settings['basis'],
'scf_type': 'pk',
'mp2_type': 'conv',
'puream': False,
'reference': 'uhf'
})
psi4_mp2 = psi4.energy('mp2')
printif('\n\n{} Psi4 MP2 Energy: {:>16.10f}'.format(
emoji('eyes'), psi4_mp2))
if abs(psi4_mp2 - Emp2 - Escf) < 1.e-8:
printif('\n ' + emoji('books'), end=' ')
printif('My grade:')
printif(\
"""
AAA
A:::A
A:::::A
A:::::::A
A:::::::::A
A:::::A:::::A
A:::::A A:::::A
A:::::A A:::::A
A:::::A A:::::A
A:::::AAAAAAAAA:::::A
A:::::::::::::::::::::A
A:::::AAAAAAAAAAAAA:::::A
A:::::A A:::::A
A:::::A A:::::A
A:::::A A:::::A
AAAAAAA AAAAAAA""")
return Emp2
def compute_DFMP2(Settings, silent=False):
t0 = time.time()
printif = print if not silent else lambda *k, **w: None
Escf, Ca, Cb, epsa, epsb, = compute_uhf(Settings,
return_C=True,
return_integrals=False)
printif("""
===================================================
Density-Fitting Spin-Orbital
Møller-Plesset Perturbation Theory
{}
===================================================
""".format('\U0001F347' + '\U000027A1' + emoji('wine')))
tasks = """
Building:
\U0001F426 Spin-Orbital arrays {}
\U0001F986 J^(-1/2) (P|Q) {}
\U0001F989 (uv|P) {}
\U0001F9A2 b(ia|Q) {}"""
printif(tasks.format('', '', '', ''))
# Create Spin-Orbital arrays
tsave = []
t = time.time()
C = np.block([[Ca, np.zeros(Ca.shape)], [np.zeros(Cb.shape), Cb]])
eps = np.concatenate((epsa, epsb))
nmo = len(eps)
## Sorting orbitals
s = np.argsort(eps)
eps = eps[s]
C = C[:, s]
tsave.append(time.time() - t)
show_progress(tsave, printif)
# Create slices
nelec = Settings['nalpha'] + Settings['nbeta']
o = slice(0, nelec)
v = slice(nelec, nmo)
# Build J^(-1/2) using Psi4
t = time.time()
molecule = psi4.geometry(Settings['molecule'])
basis = psi4.core.BasisSet.build(molecule,
'BASIS',
Settings['basis'],
puream=0)
dfbasis = psi4.core.BasisSet.build(molecule,
'DF_BASIS_MP2',
Settings['df_basis'],
puream=0)
mints = psi4.core.MintsHelper(basis)
zero = psi4.core.BasisSet.zero_ao_basis_set()
Jinvs = np.squeeze(mints.ao_eri(dfbasis, zero, dfbasis, zero).np)
Jinvs = psi4.core.Matrix.from_array(Jinvs)
Jinvs.power(-0.5, 1.e-14)
Jinvs = Jinvs.np
tsave.append(time.time() - t)
show_progress(tsave, printif)
# Get integrals uvP
t = time.time()
ao_uvP = np.squeeze(mints.ao_eri(basis, basis, dfbasis, zero).np)
fh = slice(0, int(nmo / 2))
sh = slice(int(nmo / 2), nmo)
## Build spin-blocks
uvP = np.zeros((nmo, nmo, ao_uvP.shape[2]))
uvP[fh, fh, :] = ao_uvP
uvP[sh, sh, :] = ao_uvP
tsave.append(time.time() - t)
show_progress(tsave, printif)
# Get b(iaP)
t = time.time()
biaQ = np.einsum('ui, va, uvP, PQ -> iaQ',
C[:, o],
C[:, v],
uvP,
Jinvs,
optimize='optimal')
print(biaQ)
tsave.append(time.time() - t)
show_progress(tsave, printif)
# Get MP2 energy
printif('\n{} Computing DF-MP2 energy'.format('\U0001F9ED'), end=' ')
t = time.time()
Emp2 = 0
new = np.newaxis
eo = eps[o]
ev = eps[v]
for i in range(nelec):
for j in range(nelec):
D = -ev[:, new] - ev[new, :]
D = D + eo[i] + eo[j]
D = 1.0 / D
bAB = np.einsum('aQ, bQ-> ab', biaQ[i, :, :], biaQ[j, :, :])
bBA = np.einsum('bQ, aQ-> ab', biaQ[i, :, :], biaQ[j, :, :])
Emp2 += np.einsum('ab, ab->',
np.square(bAB - bBA),
D,
optimize='optimal')
Emp2 = Emp2 / 4.0
tsave.append(time.time() - t)
printif('\n{} DF-MP2 Correlation Energy: {:>16.10f}'.format(
emoji('bolt'), Emp2))
printif('\U0001F308 Final Electronic Energy: {:>16.10f}'.format(Emp2 +
Escf))
printif('\n{} Total Computation time: {:10.5f} seconds'.format(
'\U0000231B',
time.time() - t0))
# Compare results with SO-MP2
printif('\n \U0001F4C8 Comparing results with regular MP2:')
t0 = time.time()
E0mp2 = compute_SOMP2(Settings, psi4compare=False, silent=True)
printif('\n{} MP2 Correlation Energy: {:>16.10f}'.format(
emoji('bolt'), E0mp2))
printif('{} DF error: {:>16.10f}'.format(
'\U0000274C', E0mp2 - Emp2))
printif('\n{} Total Computation time: {:10.5f} seconds'.format(
'\U0000231B',
time.time() - t0))
# Compare with Psi4
psi4.set_options({
'basis': Settings['basis'],
'df_basis_mp2': Settings['df_basis'],
'e_convergence': 1e-12,
'scf_type': 'pk',
'puream': False,
'reference': 'uhf'
})
psi4_mp2 = psi4.energy('mp2')
printif('\n\n{} Psi4 DF-MP2 Energy: {:>16.10f}'.format(
emoji('eyes'), psi4_mp2))
def compute_DFMP2(Settings):
Escf, Ca, Cb, epsa, epsb, = compute_uhf(Settings,
return_C=True,
return_integrals=False)
print("""
===================================================
Density-Fitting Møller-Plesset Perturbation Theory
{}
===================================================
""".format(emoji('wine')))
tasks = """
Building:
\U0001F426 Spin-Orbital arrays {}
\U0001F986 MO integral <OO|VV> {}
\U0001F9A2 Eigenvalues auxiliar array Dijab {}"""
print(tasks.format('', '', ''))
sys.stdout.write("\033[F" * 5)
time.sleep(0.1)
# Create Spin-Orbital arrays
#C = np.block([
# [Ca, np.zeros(Ca.shape)],
# [np.zeros(Cb.shape), Cb]
# ])
#eps = np.concatenate((epsa, epsb))
eps = epsa
nmo = len(eps)
# re-Sorting orbitals
#s = np.argsort(eps)
#eps = eps[s]
#C = C[:,s]
print(tasks.format(emoji('check'), '', ''))
# Create slices
nelec = Settings['nalpha'] + Settings['nbeta']
ndocc = int(nelec / 2)
nmo = len(eps)
nvir = nmo - ndocc
o = slice(0, ndocc)
v = slice(ndocc, nmo)
# Build J^(-1/2) using Psi4
molecule = psi4.geometry(Settings['molecule'])
basis = psi4.core.BasisSet.build(molecule,
'BASIS',
Settings['basis'],
puream=0)
dfbasis = psi4.core.BasisSet.build(molecule,
'DF_BASIS_MP2',
Settings['df_basis'],
puream=0)
mints = psi4.core.MintsHelper(basis)
zero = psi4.core.BasisSet.zero_ao_basis_set()
Jinvs = np.squeeze(mints.ao_eri(dfbasis, zero, dfbasis, zero).np)
Jinvs = psi4.core.Matrix.from_array(Jinvs)
Jinvs.power(-0.5, 1.e-14)
Jinvs = Jinvs.np
# Get integrals uvP
uvP = np.squeeze(mints.ao_eri(basis, basis, dfbasis, zero).np)
#fh = slice(0, int(nmo/2))
#sh = slice(int(nmo/2), nmo)
#uvP = np.zeros((nmo, nmo, ao_uvP.shape[2]))
#uvP[fh, fh, :] = ao_uvP
#uvP[sh, sh, :] = ao_uvP
#uvP = uvP[s,:,:]
#uvP = uvP[:,s,:]
#print(uvP.shape)
# Get b(iaP)
# b(uvQ)
biaQ = np.einsum('uvP, PQ-> uvQ', uvP, Jinvs)
# b(ivQ)
biaQ = np.einsum('ui, uvQ-> ivQ', Ca[:, o], biaQ)
# b(iaQ)
biaQ = np.einsum('va, ivQ-> iaQ', Ca[:, v], biaQ)
# Get eigenvalues Matrix D
new = np.newaxis
eo = eps[o]
ev = eps[v]
D = 1.0 / (eo[:, new, new, new] - ev[new, :, new, new] +
eo[new, new, :, new] - ev[new, new, new, :])
# Get MP2 energy
print('\n{} Computing MP2 energy'.format('\U0001F9ED'), end=' ')
Emp2 = 0
for i in range(ndocc):
for j in range(ndocc):
bAB = np.einsum('aQ, bQ-> ab', biaQ[i, :, :], biaQ[j, :, :])
bBA = np.einsum('bQ, aQ-> ab', biaQ[i, :, :], biaQ[j, :, :])
Emp2 += np.einsum('ab, ab, ab->',
bAB, (2 * bAB - bBA),
D[i, :, j, :],
optimize='optimal')
print('\n{} MP2 Correlation Energy: {:>16.10f}'.format(
emoji('bolt'), Emp2))
print('\U0001F308 Final Electronic Energy: {:>16.10f}'.format(Emp2 +
Escf))
psi4.set_options({
'basis': Settings['basis'],
'df_basis_mp2': Settings['df_basis'],
'e_convergence': 1e-12,
'scf_type': 'pk',
'puream': False,
'reference': 'uhf'
})
psi4_mp2 = psi4.energy('mp2')
print('\n\n{} Psi4 MP2 Energy: {:>16.10f}'.format(
emoji('eyes'), psi4_mp2))
def compute_CI(Settings):
Escf, C, _, _, _, h, g, Vnuc = compute_uhf(Settings,
return_C=True,
return_integrals=True)
print("""
=========================================
Configuration Interaction Starting
{}
=========================================
""".format(emoji('brinde')))
# Initial workup
nmo = len(C)
nelec = Settings['nalpha'] + Settings['nbeta']
if nelec % 2 != 0:
raise NameError('This code works only for closed-shell molecules')
ndocc = int(nelec / 2)
nvir = nmo - ndocc
# Read in excitation level
if type(Settings["excitation_level"]) == int:
exlv = int(Settings["excitation_level"])
elif type(Settings["excitation_level"]) == str:
if Settings["excitation_level"].lower() == 'full':
exlv = nelec
elif Settings["excitation_level"].lower() == 'cis':
exlv = 1
elif Settings["excitation_level"].lower() == 'cisd':
exlv = 2
elif Settings["excitation_level"].lower() == 'cisdt':
exlv = 3
elif Settings["excitation_level"].lower() == 'cisdtq':
exlv = 4
elif Settings["excitation_level"].lower() == 'cisdtqp':
exlv = 5
else:
raise TypeError('Invalid excitation level: {}'.format(
Settings["excitation_level"]))
else:
raise TypeError('Invalid excitation level: {}'.format(
Settings["excitation_level"]))
# Convert AO integrals to Molecular integrals
h = np.einsum('up,vq,uv->pq', C, C, h, optimize='optimal')
ERI = np.einsum('ap,bq,cr,ds,abcd->pqrs',
C,
C,
C,
C,
g,
optimize='optimal')
############### STEP 1 ###############
######## Read Active Space #########
## The active_space must be a string containing the letters 'o', 'a' and 'u':
## 'o' represents frozen doubly occupied orbitals;
## 'a' represents active orbitals;
## 'u' represents frozen unoccupied orbitals.
## However, the active_space can also be given in three alternative ways:
## active_space = 'full': for a full CI computation;
## active_space = 'none': for a emputy active space (output energy is SCF energy);
## If the active_space string is shorter than expected, remaining orbitals will be
## considered 'u' (unoccupied)
## Note that, the first orbital has index 0
active_space = Settings['active_space']
if active_space == 'full':
active_space = 'a' * nmo
elif active_space == 'none':
active_space = 'o' * ndocc + 'u' * nvir
# If the active space string is smaller than the number of orbitals,
# append 'u' (unoccupied) at the end
while len(active_space) < nmo:
active_space += 'u'
# Check if active space size is consistem with given integrals
if len(active_space) != nmo:
raise NameError(
'Active Space size is {} than the number of molecular orbitals'.
format('greater' if len(active_space) > nmo else 'smaller'))
nfrozen = active_space.count('o')
nactive = active_space.count('a')
active_elec = nelec - 2 * nfrozen
# Check if number of frozen orbitals is valid (must be less than the number of electron pairs)
if active_elec < 0:
raise NameError(
'Invalid number of frozen orbitals ({}), for {} electrons'.format(
nfrozen, nelec))
# Check if there are enough active orbitals for the active electrons
if 2 * nactive < active_elec:
raise NameError(
'{} active orbitals not enough for {} active electrons'.format(
nactive, active_elec))
print("Active Space:")
out = ''
for x in active_space:
if x == 'o':
out += emoji('Obold')
elif x == 'a':
out += emoji('O')
elif x == 'u':
out += emoji('X')
else:
raise TypeError('Invalid active space key: {}'.format(x))
print(out + '\n')
print("Number of active spatial orbitals: {}".format(numoji(nactive)))
print("Number of active electrons: {}".format(numoji(active_elec)))
print("Number of frozen orbitals: {}\n".format(numoji(nfrozen)))
############### STEP 2 ###############
###### Generate Determinants #######
# Creating a template string. Active orbitals are represented by {}
# Occupied orbitals are 1, and unnocupied 0.
# For example: 11{}{}{}000 represents a system with 8 orbitals where the two lowest ones are frozen (doubly occupied)
# and the three highest ones are frozen (unnocupied). Thus, there are 3 active orbitals.
template_space = active_space.replace('o', '1')
template_space = template_space.replace('u', '0')
template_space = template_space.replace('a', '{}')
# Produce a reference determinant
ref = Determinant(a = ('1'*ndocc + '0'*nvir), \
b = ('1'*ndocc + '0'*nvir))
# Produces a list of active electrons in active orbitals and get all permutations of it.
# For example. Say we have a template 11{}{}{}000 as in the example above. If the system contains 6 electrons
# we have one pair of active electrons. The list of active electrons will look like '100' and the permutations
# will be generated (as lists): ['1', '0', '0'], ['0', '1', '0'], and ['0', '0', '1'].
# Each permutation is then merged with the template above to generate a string used ot create a Determinant object
# For example. 11{}{}{}000 is combine with ['0','1', '0'] to produce an alpha/beta string 11010000.
# These strings are then combined to form various determinants
print("Generating excitations", end=' ')
perms = set(
permutations('1' * int(active_elec / 2) +
'0' * int(nactive - active_elec / 2)))
print(emoji('check'))
determinants = []
for p1 in perms:
for p2 in perms:
newdet = Determinant(a=template_space.format(*p1),
b=template_space.format(*p2))
if ref - newdet > 2 * exlv:
continue
else:
determinants.append(newdet)
ndets = len(determinants)
nelements = int((ndets * (ndets + 1)) / 2)
print("Number of determinants: {}".format(numoji(ndets)))
print("Number of unique matrix elements: {}\n".format(
numoji(nelements)))
# Build Hamiltonian matrix
print("Building Hamiltonian Matrix", end=' ')
H = get_Hamiltonian(determinants, h, ERI)
print(emoji('check'))
# Diagonalize Hamiltonian
print("Diagonalizing Hamiltonian Matrix", end=' ')
Eci, Cci = np.linalg.eigh(H)
print(emoji('check'))
E0 = Eci[0] + Vnuc
C0 = Cci[:, 0]
print('\nFinal CASCI Energy: {:<14.10f}'.format(E0))
if exlv == nelec:
psi4.set_options({
'basis': Settings['basis'],
'scf_type': 'pk',
'puream': False,
'FROZEN_DOCC': [nfrozen],
'ACTIVE': [nactive],
'FCI': True,
'reference': 'rhf'
})
print('Final DETCI Energy: {:<14.10f}'.format(psi4.energy('detci')))
# Print wavefunction analysis
ndet = 10
args = np.argsort(C0 * C0)
sortedC = C0[args]
sortedC = sortedC[::-1]
sortedC = sortedC[:ndet]
sortedD = np.array(determinants)[args]
sortedD = sortedD[::-1]
sortedD = sortedD[:ndet]
print('\n\n' + emoji('bchart') + ' Wave function analysis')
print('=' * 25)
print('\n')
print(' ' * 10 + emoji('top') +
' Top {} determinants as voted by the public\n'.format(ndet))
# Header
l = len(ref.short_info())
header = ' {:^7s} {:^8s} {:^' + str(l) + 's} {:^9s}'
print(header.format('%', 'Coef', 'Determinant', 'Exc lv'))
print(' ' * 4 + '=' * 7 + ' ' + '=' * 8 + ' ' + '=' * l + ' ' +
'=' * 9)
# Actual info
f = True
for c, d in zip(sortedC, sortedD):
sq = c * c
ex = int((ref - d) / 2)
if f:
print(' ' * 2 + emoji('trophy') +
'{:7.5f} {: 8.5f} {} {:^9s}'.format(
sq, c, d.short_info(), numoji(ex)))
f = False
else:
print(' ' * 4 + '{:7.5f} {: 8.5f} {} {:^9s}'.format(
sq, c, d.short_info(), numoji(ex)))
def compute_dfCEPA(Settings, silent=False, compare=False):
t0 = time.time()
printif = print if not silent else lambda *k, **w: None
Escf, Ca, Cb, epsa, epsb, _, g, Vnuc = compute_uhf(Settings,
return_C=True,
return_integrals=True)
printif("""
=======================================================
Density-Fitting CEPA0
(For those who havent heard Coupled Cluster exists)
{}
=======================================================
""".format('\U0001F474'))
tsave = []
show_progress(tsave, printif)
# Create Spin-Orbital arrays
t = time.time()
C = np.block([[Ca, np.zeros(Ca.shape)], [np.zeros(Cb.shape), Cb]])
eps = np.concatenate((epsa, epsb))
g = np.kron(np.eye(2), g)
g = np.kron(np.eye(2), g.T)
# re-Sorting orbitals
s = np.argsort(eps)
eps = eps[s]
C = C[:, s]
tsave.append(time.time() - t)
show_progress(tsave, printif)
# Get the MO integral
t = time.time()
nelec = Settings['nalpha'] + Settings['nbeta']
# Converto to Physicists notation
g = g.transpose(0, 2, 1, 3)
# Antisymmetrize
g = g - g.transpose(0, 1, 3, 2)
# Save Slices
ERI = lambda ax, bx, cx, dx: np.einsum('ap,bq,cr,ds,abcd->pqrs',
C[:, ax],
C[:, bx],
C[:, cx],
C[:, dx],
g,
optimize='optimal')
nmo = len(g)
o = slice(0, nelec)
v = slice(nelec, nmo)
Voovv = ERI(o, o, v, v)
Voooo = ERI(o, o, o, o)
Vvoov = ERI(v, o, o, v)
tsave.append(time.time() - t)
show_progress(tsave, printif)
# Use Density-Fitting for the Vvvvv case
# Build J^(-1/2) using Psi4
t = time.time()
molecule = psi4.geometry(Settings['molecule'])
basis = psi4.core.BasisSet.build(molecule,
'BASIS',
Settings['basis'],
puream=0)
dfbasis = psi4.core.BasisSet.build(molecule,
'DF_BASIS_MP2',
Settings['df_basis'],
puream=0)
mints = psi4.core.MintsHelper(basis)
zero = psi4.core.BasisSet.zero_ao_basis_set()
Jinvs = np.squeeze(mints.ao_eri(dfbasis, zero, dfbasis, zero).np)
Jinvs = psi4.core.Matrix.from_array(Jinvs)
Jinvs.power(-0.5, 1.e-14)
Jinvs = Jinvs.np
tsave.append(time.time() - t)
show_progress(tsave, printif)
# Get integrals uvP
t = time.time()
ao_uvP = np.squeeze(mints.ao_eri(basis, basis, dfbasis, zero).np)
fh = slice(0, int(nmo / 2))
sh = slice(int(nmo / 2), nmo)
## Build spin-blocks
uvP = np.zeros((nmo, nmo, ao_uvP.shape[2]))
uvP[fh, fh, :] = ao_uvP
uvP[sh, sh, :] = ao_uvP
tsave.append(time.time() - t)
show_progress(tsave, printif)
# Get b(abP)
t = time.time()
babQ = np.einsum('ua, vb, uvP, PQ -> abQ',
C[:, v],
C[:, v],
uvP,
Jinvs,
optimize='optimal')
tsave.append(time.time() - t)
show_progress(tsave, printif)
# Get eigenvalues Matrix D
t = time.time()
new = np.newaxis
eo = eps[:nelec]
ev = eps[nelec:]
D = 1.0 / (eo[:, new, new, new] + eo[new, :, new, new] -
ev[new, new, :, new] - ev[new, new, new, :])
tsave.append(time.time() - t)
show_progress(tsave, printif)
t = time.time()
# Initial guess for amplitudes
T = Voovv * D
# Get MP2 energy
E = CEPA_Energy(T, Voovv)
print('\nMP2 Energy: {:<15.10f}\n'.format(E + Escf))
# Setup iteration options
rms = 0.0
dE = 1
ite = 1
rms_LIM = 10**(-8)
E_LIM = 10**(-12)
t0 = time.time()
printif('\U00003030' * 20)
# Start CC iterations
while abs(dE) > E_LIM or rms > rms_LIM:
t = time.time()
if ite > Settings["cc_max_iter"]:
raise NameError('CEPA0 equations did not converge')
T, rms = CEPA_Amplitude(T, D, Voovv, Voooo, babQ, Vvoov)
dE = -E
E = CEPA_Energy(T, Voovv)
dE += E
printif("Iteration {}".format(numoji(ite)))
printif("\U000027B0 Correlation energy: {:< 15.10f}".format(E))
printif("\U0001F53A Energy change: {:< 15.10f}".format(dE))
printif("\U00002622 Max RMS residue: {:< 15.10f}".format(rms))
printif("\U0001F570 Time required: {:< 15.10f}".format(
time.time() - t))
printif('\U00003030' * 20)
ite += 1
printif('\U0001F3C1 DF-CEPA0 Energy: {:<15.10f}'.format(E + Escf))
printif('\U000023F3 CEPA0 iterations took %.2f seconds.\n' %
(time.time() - t0))
if compare:
from CEPA import compute_CEPA
printif('\n Running regular CEPA...\n')
t0 = time.time()
Efull = compute_CEPA(Settings, silent=True, compare=False)
printif('\U0001F3C1 CEPA0 Energy: {:<15.10f}'.format(Efull))
printif('\U000023F3 CEPA0 iterations took %.2f seconds.\n' %
(time.time() - t0))
printif(
'\n\U0000274C Density Fitting Error: {:<15.10f}'.format(E +
Escf -
Efull))
return (E + Escf)
