def compute_sr_potential(nbasis, er_sr, dms, whichpot):
"""
Add Short-Range Hartree or Exchange Potential to one-electron integrals
using Horton.
Arguments:
----------
nbasis: int
Number of basis functions
er_sr: np.ndarray((nbasis, nbasis, nbasis, nbasis))
Short-Range two-electron integrals
dms: list, np.ndarray((nbasis, nbasis))
Density matrices
whichpot: str
Type of potential to be computed. Options are:
'exchange' for the exchange potential
'hartree' for the Hartree potential
"""
if len(dms) == 1:
# Restricted case
if whichpot == 'hartree':
ham = REffHam([RDirectTerm(er_sr, 'hartree')])
elif whichpot == 'exchange':
ham = REffHam([RExchangeTerm(er_sr, 'x')])
ham.reset(dms[0])
elif len(dms) == 2:
# Unrestricted case
if whichpot == 'hartree':
ham = UEffHam([UDirectTerm(er_sr, 'hartree')])
else:
ham = UEffHam([UExchangeTerm(er_sr, 'x')])
ham.reset(dms)
sr_potential = np.zeros((nbasis, nbasis))
ham.compute_fock(sr_potential)
return sr_potential
def check_water_cs_m05(scf_solver):
"""Try to converge the SCF for the water molecule with the M05 functional.
Parameters
----------
scf_solver : one of the SCFSolver types in HORTON
A configured SCF solver that must be tested.
"""
fn_fchk = context.get_fn('test/water_m05_321g.fchk')
mol = IOData.from_file(fn_fchk)
grid = BeckeMolGrid(mol.coordinates, mol.numbers, mol.pseudo_numbers, 'fine',
random_rotate=False)
olp = mol.obasis.compute_overlap()
kin = mol.obasis.compute_kinetic()
na = mol.obasis.compute_nuclear_attraction(mol.coordinates, mol.pseudo_numbers)
er = mol.obasis.compute_electron_repulsion()
external = {'nn': compute_nucnuc(mol.coordinates, mol.pseudo_numbers)}
libxc_term = RLibXCHybridMGGA('xc_m05')
terms = [
RTwoIndexTerm(kin, 'kin'),
RDirectTerm(er, 'hartree'),
RGridGroup(mol.obasis, grid, [libxc_term]),
RExchangeTerm(er, 'x_hf', libxc_term.get_exx_fraction()),
RTwoIndexTerm(na, 'ne'),
]
ham = REffHam(terms, external)
# compute the energy before converging
dm_alpha = mol.orb_alpha.to_dm()
ham.reset(dm_alpha)
ham.compute_energy()
assert abs(ham.cache['energy'] - -75.9532086800) < 1e-3
# The convergence should be reasonable, not perfect because of limited
# precision in the molden file:
assert convergence_error_eigen(ham, olp, mol.orb_alpha) < 1e-3
# keep a copy of the orbital energies
expected_alpha_energies = mol.orb_alpha.energies.copy()
# Converge from scratch
occ_model = AufbauOccModel(5)
check_solve(ham, scf_solver, occ_model, olp, kin, na, mol.orb_alpha)
# test orbital energies
assert abs(mol.orb_alpha.energies - expected_alpha_energies).max() < 2e-3
ham.compute_energy()
# compare with
assert abs(ham.cache['energy_kin'] - 75.54463056278) < 1e-2
assert abs(ham.cache['energy_ne'] - -198.3003887880) < 1e-2
assert abs(ham.cache['energy_hartree'] + ham.cache['energy_x_hf'] +
ham.cache['energy_libxc_hyb_mgga_xc_m05'] - 3.764537450376E+01) < 1e-2
assert abs(ham.cache['energy'] - -75.9532086800) < 1e-3
assert abs(ham.cache['energy_nn'] - 9.1571750414) < 1e-5
def check_water_cs_hfs(scf_solver):
fn_fchk = context.get_fn('test/water_hfs_321g.fchk')
mol = IOData.from_file(fn_fchk)
grid = BeckeMolGrid(mol.coordinates, mol.numbers, mol.pseudo_numbers, random_rotate=False)
olp = mol.obasis.compute_overlap(mol.lf)
kin = mol.obasis.compute_kinetic(mol.lf)
na = mol.obasis.compute_nuclear_attraction(mol.coordinates, mol.pseudo_numbers, mol.lf)
er = mol.obasis.compute_electron_repulsion(mol.lf)
external = {'nn': compute_nucnuc(mol.coordinates, mol.pseudo_numbers)}
terms = [
RTwoIndexTerm(kin, 'kin'),
RDirectTerm(er, 'hartree'),
RGridGroup(mol.obasis, grid, [
RDiracExchange(),
]),
RTwoIndexTerm(na, 'ne'),
]
ham = REffHam(terms, external)
# The convergence should be reasonable, not perfect because of limited
# precision in Gaussian fchk file and different integration grids:
assert convergence_error_eigen(ham, mol.lf, olp, mol.exp_alpha) < 3e-5
# Recompute the orbitals and orbital energies. This should be reasonably OK.
dm_alpha = mol.exp_alpha.to_dm()
ham.reset(dm_alpha)
ham.compute_energy()
fock_alpha = mol.lf.create_two_index()
ham.compute_fock(fock_alpha)
mol.exp_alpha.from_fock(fock_alpha, olp)
expected_energies = np.array([
-1.83691041E+01, -8.29412411E-01, -4.04495188E-01, -1.91740814E-01,
-1.32190590E-01, 1.16030419E-01, 2.08119657E-01, 9.69825207E-01,
9.99248500E-01, 1.41697384E+00, 1.47918828E+00, 1.61926596E+00,
2.71995350E+00
])
assert abs(mol.exp_alpha.energies - expected_energies).max() < 2e-4
assert abs(ham.cache['energy_ne'] - -1.977921986200E+02) < 1e-7
assert abs(ham.cache['energy_kin'] - 7.525067610865E+01) < 1e-9
assert abs(ham.cache['energy_hartree'] + ham.cache['energy_x_dirac'] - 3.864299848058E+01) < 2e-4
assert abs(ham.cache['energy'] - -7.474134898935590E+01) < 2e-4
assert abs(ham.cache['energy_nn'] - 9.1571750414) < 2e-8
# Converge from scratch and check energies
occ_model = AufbauOccModel(5)
check_solve(ham, scf_solver, occ_model, mol.lf, olp, kin, na, mol.exp_alpha)
ham.compute_energy()
assert abs(ham.cache['energy_ne'] - -1.977921986200E+02) < 1e-4
assert abs(ham.cache['energy_kin'] - 7.525067610865E+01) < 1e-4
assert abs(ham.cache['energy_hartree'] + ham.cache['energy_x_dirac'] - 3.864299848058E+01) < 2e-4
assert abs(ham.cache['energy'] - -7.474134898935590E+01) < 2e-4
def check_water_cs_hfs(scf_solver):
fn_fchk = context.get_fn('test/water_hfs_321g.fchk')
mol = IOData.from_file(fn_fchk)
grid = BeckeMolGrid(mol.coordinates, mol.numbers, mol.pseudo_numbers, random_rotate=False)
olp = mol.obasis.compute_overlap()
kin = mol.obasis.compute_kinetic()
na = mol.obasis.compute_nuclear_attraction(mol.coordinates, mol.pseudo_numbers)
er = mol.obasis.compute_electron_repulsion()
external = {'nn': compute_nucnuc(mol.coordinates, mol.pseudo_numbers)}
terms = [
RTwoIndexTerm(kin, 'kin'),
RDirectTerm(er, 'hartree'),
RGridGroup(mol.obasis, grid, [
RDiracExchange(),
]),
RTwoIndexTerm(na, 'ne'),
]
ham = REffHam(terms, external)
# The convergence should be reasonable, not perfect because of limited
# precision in Gaussian fchk file and different integration grids:
assert convergence_error_eigen(ham, olp, mol.orb_alpha) < 3e-5
# Recompute the orbitals and orbital energies. This should be reasonably OK.
dm_alpha = mol.orb_alpha.to_dm()
ham.reset(dm_alpha)
ham.compute_energy()
fock_alpha = np.zeros(dm_alpha.shape)
ham.compute_fock(fock_alpha)
mol.orb_alpha.from_fock(fock_alpha, olp)
expected_energies = np.array([
-1.83691041E+01, -8.29412411E-01, -4.04495188E-01, -1.91740814E-01,
-1.32190590E-01, 1.16030419E-01, 2.08119657E-01, 9.69825207E-01,
9.99248500E-01, 1.41697384E+00, 1.47918828E+00, 1.61926596E+00,
2.71995350E+00
])
assert abs(mol.orb_alpha.energies - expected_energies).max() < 2e-4
assert abs(ham.cache['energy_ne'] - -1.977921986200E+02) < 1e-7
assert abs(ham.cache['energy_kin'] - 7.525067610865E+01) < 1e-9
assert abs(ham.cache['energy_hartree'] + ham.cache['energy_x_dirac'] - 3.864299848058E+01) < 2e-4
assert abs(ham.cache['energy'] - -7.474134898935590E+01) < 2e-4
assert abs(ham.cache['energy_nn'] - 9.1571750414) < 2e-8
# Converge from scratch and check energies
occ_model = AufbauOccModel(5)
check_solve(ham, scf_solver, occ_model, olp, kin, na, mol.orb_alpha)
ham.compute_energy()
assert abs(ham.cache['energy_ne'] - -1.977921986200E+02) < 1e-4
assert abs(ham.cache['energy_kin'] - 7.525067610865E+01) < 1e-4
assert abs(ham.cache['energy_hartree'] + ham.cache['energy_x_dirac'] - 3.864299848058E+01) < 2e-4
assert abs(ham.cache['energy'] - -7.474134898935590E+01) < 2e-4
