def check_methyl_os_tpss(scf_solver):
"""Try to converge the SCF for the methyl radical molecule with the TPSS 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/methyl_tpss_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)}
terms = [
UTwoIndexTerm(kin, 'kin'),
UDirectTerm(er, 'hartree'),
UGridGroup(mol.obasis, grid, [
ULibXCMGGA('x_tpss'),
ULibXCMGGA('c_tpss'),
]),
UTwoIndexTerm(na, 'ne'),
]
ham = UEffHam(terms, external)
# compute the energy before converging
dm_alpha = mol.orb_alpha.to_dm()
dm_beta = mol.orb_beta.to_dm()
ham.reset(dm_alpha, dm_beta)
ham.compute_energy()
assert abs(ham.cache['energy'] - -39.6216986265) < 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, mol.orb_beta) < 1e-3
# keep a copy of the orbital energies
expected_alpha_energies = mol.orb_alpha.energies.copy()
expected_beta_energies = mol.orb_beta.energies.copy()
# Converge from scratch
occ_model = AufbauOccModel(5, 4)
check_solve(ham, scf_solver, occ_model, olp, kin, na, mol.orb_alpha, mol.orb_beta)
# test orbital energies
assert abs(mol.orb_alpha.energies - expected_alpha_energies).max() < 2e-3
assert abs(mol.orb_beta.energies - expected_beta_energies).max() < 2e-3
ham.compute_energy()
# compare with
assert abs(ham.cache['energy_kin'] - 38.98408965928) < 1e-2
assert abs(ham.cache['energy_ne'] - -109.2368837076) < 1e-2
assert abs(ham.cache['energy_hartree'] + ham.cache['energy_libxc_mgga_x_tpss'] +
ham.cache['energy_libxc_mgga_c_tpss'] - 21.55131145126) < 1e-2
assert abs(ham.cache['energy'] - -39.6216986265) < 1e-3
assert abs(ham.cache['energy_nn'] - 9.0797839705) < 1e-5
def check_h3_os_pbe(scf_solver):
fn_fchk = context.get_fn("test/h3_pbe_321g.fchk")
mol = IOData.from_file(fn_fchk)
grid = BeckeMolGrid(mol.coordinates, mol.numbers, mol.pseudo_numbers, "veryfine", 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 = [
UTwoIndexTerm(kin, "kin"),
UDirectTerm(er, "hartree"),
UGridGroup(mol.obasis, grid, [ULibXCGGA("x_pbe"), ULibXCGGA("c_pbe")]),
UTwoIndexTerm(na, "ne"),
]
ham = UEffHam(terms, external)
# compute the energy before converging
dm_alpha = mol.exp_alpha.to_dm()
dm_beta = mol.exp_beta.to_dm()
ham.reset(dm_alpha, dm_beta)
ham.compute_energy()
assert abs(ham.cache["energy"] - -1.593208400939354e00) < 1e-5
# The convergence should be reasonable, not perfect because of limited
# precision in Gaussian fchk file:
assert convergence_error_eigen(ham, mol.lf, olp, mol.exp_alpha, mol.exp_beta) < 2e-6
# Converge from scratch
occ_model = AufbauOccModel(2, 1)
check_solve(ham, scf_solver, occ_model, mol.lf, olp, kin, na, mol.exp_alpha, mol.exp_beta)
# test orbital energies
expected_energies = np.array(
[-5.41141676e-01, -1.56826691e-01, 2.13089637e-01, 7.13565167e-01, 7.86810564e-01, 1.40663544e00]
)
assert abs(mol.exp_alpha.energies - expected_energies).max() < 2e-5
expected_energies = np.array(
[-4.96730336e-01, -5.81411249e-02, 2.73586652e-01, 7.41987185e-01, 8.76161160e-01, 1.47488421e00]
)
assert abs(mol.exp_beta.energies - expected_energies).max() < 2e-5
ham.compute_energy()
# compare with g09
assert abs(ham.cache["energy_ne"] - -6.934705182067e00) < 1e-5
assert abs(ham.cache["energy_kin"] - 1.948808793424e00) < 1e-5
assert (
abs(
ham.cache["energy_hartree"]
+ ham.cache["energy_libxc_gga_x_pbe"]
+ ham.cache["energy_libxc_gga_c_pbe"]
- 1.502769385597e00
)
< 1e-5
)
assert abs(ham.cache["energy"] - -1.593208400939354e00) < 1e-5
assert abs(ham.cache["energy_nn"] - 1.8899186021) < 1e-8
def check_co_cs_pbe(scf_solver):
fn_fchk = context.get_fn('test/co_pbe_sto3g.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(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, [
RLibXCGGA('x_pbe'),
RLibXCGGA('c_pbe'),
]),
RTwoIndexTerm(na, 'ne'),
]
ham = REffHam(terms, external)
# Test energy before scf
energy, focks = helper_compute(ham, mol.lf, mol.exp_alpha)
assert abs(energy - -1.116465967841901E+02) < 1e-4
# The convergence should be reasonable, not perfect because of limited
# precision in Gaussian fchk file:
assert convergence_error_eigen(ham, mol.lf, olp, mol.exp_alpha) < 1e-5
# Converge from scratch
occ_model = AufbauOccModel(7)
check_solve(ham, scf_solver, occ_model, mol.lf, olp, kin, na,
mol.exp_alpha)
# test orbital energies
expected_energies = np.array([
-1.86831122E+01, -9.73586915E+00, -1.03946082E+00, -4.09331776E-01,
-3.48686522E-01, -3.48686522E-01, -2.06049056E-01, 5.23730418E-02,
5.23730418E-02, 6.61093726E-01
])
assert abs(mol.exp_alpha.energies - expected_energies).max() < 1e-2
ham.compute_energy()
# compare with g09
assert abs(ham.cache['energy_ne'] - -3.072370116827E+02) < 1e-2
assert abs(ham.cache['energy_kin'] - 1.103410779827E+02) < 1e-2
assert abs(ham.cache['energy_hartree'] +
ham.cache['energy_libxc_gga_x_pbe'] +
ham.cache['energy_libxc_gga_c_pbe'] - 6.273115782683E+01) < 1e-2
assert abs(ham.cache['energy'] - -1.116465967841901E+02) < 1e-4
assert abs(ham.cache['energy_nn'] - 22.5181790889) < 1e-7
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_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()
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
def check_h3_os_pbe(scf_solver):
fn_fchk = context.get_fn('test/h3_pbe_321g.fchk')
mol = IOData.from_file(fn_fchk)
grid = BeckeMolGrid(mol.coordinates, mol.numbers, mol.pseudo_numbers, 'veryfine', 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 = [
UTwoIndexTerm(kin, 'kin'),
UDirectTerm(er, 'hartree'),
UGridGroup(mol.obasis, grid, [
ULibXCGGA('x_pbe'),
ULibXCGGA('c_pbe'),
]),
UTwoIndexTerm(na, 'ne'),
]
ham = UEffHam(terms, external)
# compute the energy before converging
dm_alpha = mol.orb_alpha.to_dm()
dm_beta = mol.orb_beta.to_dm()
ham.reset(dm_alpha, dm_beta)
ham.compute_energy()
assert abs(ham.cache['energy'] - -1.593208400939354E+00) < 1e-5
# The convergence should be reasonable, not perfect because of limited
# precision in Gaussian fchk file:
assert convergence_error_eigen(ham, olp, mol.orb_alpha, mol.orb_beta) < 2e-6
# Converge from scratch
occ_model = AufbauOccModel(2, 1)
check_solve(ham, scf_solver, occ_model, olp, kin, na, mol.orb_alpha, mol.orb_beta)
# test orbital energies
expected_energies = np.array([
-5.41141676E-01, -1.56826691E-01, 2.13089637E-01, 7.13565167E-01,
7.86810564E-01, 1.40663544E+00
])
assert abs(mol.orb_alpha.energies - expected_energies).max() < 2e-5
expected_energies = np.array([
-4.96730336E-01, -5.81411249E-02, 2.73586652E-01, 7.41987185E-01,
8.76161160E-01, 1.47488421E+00
])
assert abs(mol.orb_beta.energies - expected_energies).max() < 2e-5
ham.compute_energy()
# compare with g09
assert abs(ham.cache['energy_ne'] - -6.934705182067E+00) < 1e-5
assert abs(ham.cache['energy_kin'] - 1.948808793424E+00) < 1e-5
assert abs(ham.cache['energy_hartree'] + ham.cache['energy_libxc_gga_x_pbe'] + ham.cache['energy_libxc_gga_c_pbe'] - 1.502769385597E+00) < 1e-5
assert abs(ham.cache['energy'] - -1.593208400939354E+00) < 1e-5
assert abs(ham.cache['energy_nn'] - 1.8899186021) < 1e-8
def check_co_cs_pbe(scf_solver):
fn_fchk = context.get_fn('test/co_pbe_sto3g.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(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, [
RLibXCGGA('x_pbe'),
RLibXCGGA('c_pbe'),
]),
RTwoIndexTerm(na, 'ne'),
]
ham = REffHam(terms, external)
# Test energy before scf
energy, focks = helper_compute(ham, mol.lf, mol.exp_alpha)
assert abs(energy - -1.116465967841901E+02) < 1e-4
# The convergence should be reasonable, not perfect because of limited
# precision in Gaussian fchk file:
assert convergence_error_eigen(ham, mol.lf, olp, mol.exp_alpha) < 1e-5
# Converge from scratch
occ_model = AufbauOccModel(7)
check_solve(ham, scf_solver, occ_model, mol.lf, olp, kin, na, mol.exp_alpha)
# test orbital energies
expected_energies = np.array([
-1.86831122E+01, -9.73586915E+00, -1.03946082E+00, -4.09331776E-01,
-3.48686522E-01, -3.48686522E-01, -2.06049056E-01, 5.23730418E-02,
5.23730418E-02, 6.61093726E-01
])
assert abs(mol.exp_alpha.energies - expected_energies).max() < 1e-2
ham.compute_energy()
# compare with g09
assert abs(ham.cache['energy_ne'] - -3.072370116827E+02) < 1e-2
assert abs(ham.cache['energy_kin'] - 1.103410779827E+02) < 1e-2
assert abs(ham.cache['energy_hartree'] + ham.cache['energy_libxc_gga_x_pbe'] + ham.cache['energy_libxc_gga_c_pbe'] - 6.273115782683E+01) < 1e-2
assert abs(ham.cache['energy'] - -1.116465967841901E+02) < 1e-4
assert abs(ham.cache['energy_nn'] - 22.5181790889) < 1e-7
def error(self, ham, lf, overlap, *exps):
'''See :py:func:`horton.meanfield.convergence.convergence_error_eigen`.'''
return convergence_error_eigen(ham, lf, overlap, *exps)
def error(self, ham, lf, overlap, *exps):
'''See :py:func:`horton.meanfield.convergence.convergence_error_eigen`.'''
return convergence_error_eigen(ham, lf, overlap, *exps)
def check_h3_os_hfs(scf_solver):
fn_fchk = context.get_fn('test/h3_hfs_321g.fchk')
mol = IOData.from_file(fn_fchk)
grid = BeckeMolGrid(mol.coordinates,
mol.numbers,
mol.pseudo_numbers,
'veryfine',
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)}
libxc_term = ULibXCLDA('x')
terms1 = [
UTwoIndexTerm(kin, 'kin'),
UDirectTerm(er, 'hartree'),
UGridGroup(mol.obasis, grid, [libxc_term]),
UTwoIndexTerm(na, 'ne'),
]
ham1 = UEffHam(terms1, external)
builtin_term = UDiracExchange()
terms2 = [
UTwoIndexTerm(kin, 'kin'),
UDirectTerm(er, 'hartree'),
UGridGroup(mol.obasis, grid, [builtin_term]),
UTwoIndexTerm(na, 'ne'),
]
ham2 = UEffHam(terms2, external)
# Compare the potential computed by libxc with the builtin implementation
energy1, focks1 = helper_compute(ham1, mol.lf, mol.exp_alpha, mol.exp_beta)
energy2, focks2 = helper_compute(ham2, mol.lf, mol.exp_alpha, mol.exp_beta)
libxc_pot = ham1.cache.load('pot_libxc_lda_x_both')[:, 0]
builtin_pot = ham2.cache.load('pot_x_dirac_alpha')
# Libxc apparently approximates values of the potential below 1e-4 with zero.
assert abs(libxc_pot - builtin_pot).max() < 1e-4
# Check of the libxc energy matches our implementation
assert abs(energy1 - energy2) < 1e-10
ex1 = ham1.cache['energy_libxc_lda_x']
ex2 = ham2.cache['energy_x_dirac']
assert abs(ex1 - ex2) < 1e-10
# The convergence should be reasonable, not perfect because of limited
# precision in Gaussian fchk file:
assert convergence_error_eigen(ham1, mol.lf, olp, mol.exp_alpha,
mol.exp_beta) < 1e-5
assert convergence_error_eigen(ham2, mol.lf, olp, mol.exp_alpha,
mol.exp_beta) < 1e-5
occ_model = AufbauOccModel(2, 1)
for ham in ham1, ham2:
# Converge from scratch
check_solve(ham, scf_solver, occ_model, mol.lf, olp, kin, na,
mol.exp_alpha, mol.exp_beta)
# test orbital energies
expected_energies = np.array([
-4.93959157E-01, -1.13961330E-01, 2.38730924E-01, 7.44216538E-01,
8.30143356E-01, 1.46613581E+00
])
assert abs(mol.exp_alpha.energies - expected_energies).max() < 1e-5
expected_energies = np.array([
-4.34824166E-01, 1.84114514E-04, 3.24300545E-01, 7.87622756E-01,
9.42415831E-01, 1.55175481E+00
])
assert abs(mol.exp_beta.energies - expected_energies).max() < 1e-5
ham.compute_energy()
# compare with g09
assert abs(ham.cache['energy_ne'] - -6.832069993374E+00) < 1e-5
assert abs(ham.cache['energy_kin'] - 1.870784279014E+00) < 1e-5
assert abs(ham.cache['energy'] - -1.412556114057104E+00) < 1e-5
assert abs(ham.cache['energy_nn'] - 1.8899186021) < 1e-8
assert abs(ham1.cache['energy_hartree'] +
ham1.cache['energy_libxc_lda_x'] - 1.658810998195E+00) < 1e-6
assert abs(ham2.cache['energy_hartree'] + ham2.cache['energy_x_dirac'] -
1.658810998195E+00) < 1e-6
def check_n2_cs_hfs(scf_solver):
fn_fchk = context.get_fn('test/n2_hfs_sto3g.fchk')
mol = IOData.from_file(fn_fchk)
grid = BeckeMolGrid(mol.coordinates,
mol.numbers,
mol.pseudo_numbers,
'veryfine',
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)}
libxc_term = RLibXCLDA('x')
terms1 = [
RTwoIndexTerm(kin, 'kin'),
RDirectTerm(er, 'hartree'),
RGridGroup(mol.obasis, grid, [libxc_term]),
RTwoIndexTerm(na, 'ne'),
]
ham1 = REffHam(terms1, external)
builtin_term = RDiracExchange()
terms2 = [
RTwoIndexTerm(kin, 'kin'),
RDirectTerm(er, 'hartree'),
RGridGroup(mol.obasis, grid, [builtin_term]),
RTwoIndexTerm(na, 'ne'),
]
ham2 = REffHam(terms2, external)
# Compare the potential computed by libxc with the builtin implementation
energy1, focks1 = helper_compute(ham1, mol.lf, mol.exp_alpha)
energy2, focks2 = helper_compute(ham2, mol.lf, mol.exp_alpha)
libxc_pot = ham1.cache.load('pot_libxc_lda_x_alpha')
builtin_pot = ham2.cache.load('pot_x_dirac_alpha')
# Libxc apparently approximates values of the potential below 1e-4 with zero.
assert abs(libxc_pot - builtin_pot).max() < 1e-4
# Check of the libxc energy matches our implementation
assert abs(energy1 - energy2) < 1e-10
ex1 = ham1.cache['energy_libxc_lda_x']
ex2 = ham2.cache['energy_x_dirac']
assert abs(ex1 - ex2) < 1e-10
# The convergence should be reasonable, not perfect because of limited
# precision in Gaussian fchk file:
assert convergence_error_eigen(ham1, mol.lf, olp, mol.exp_alpha) < 1e-5
assert convergence_error_eigen(ham2, mol.lf, olp, mol.exp_alpha) < 1e-5
occ_model = AufbauOccModel(7)
for ham in ham1, ham2:
# Converge from scratch
check_solve(ham, scf_solver, occ_model, mol.lf, olp, kin, na,
mol.exp_alpha)
# test orbital energies
expected_energies = np.array([
-1.37107053E+01,
-1.37098006E+01,
-9.60673085E-01,
-3.57928483E-01,
-3.16017655E-01,
-3.16017655E-01,
-2.12998316E-01,
6.84030479E-02,
6.84030479E-02,
7.50192517E-01,
])
assert abs(mol.exp_alpha.energies - expected_energies).max() < 3e-5
ham.compute_energy()
assert abs(ham.cache['energy_ne'] - -2.981579553570E+02) < 1e-5
assert abs(ham.cache['energy_kin'] - 1.061620887711E+02) < 1e-5
assert abs(ham.cache['energy'] - -106.205213597) < 1e-4
assert abs(ham.cache['energy_nn'] - 23.3180604505) < 1e-8
assert abs(ham1.cache['energy_hartree'] +
ham1.cache['energy_libxc_lda_x'] - 6.247259253877E+01) < 1e-4
assert abs(ham2.cache['energy_hartree'] + ham2.cache['energy_x_dirac'] -
6.247259253877E+01) < 1e-4
def check_h3_os_hfs(scf_solver):
fn_fchk = context.get_fn('test/h3_hfs_321g.fchk')
mol = IOData.from_file(fn_fchk)
grid = BeckeMolGrid(mol.coordinates, mol.numbers, mol.pseudo_numbers, 'veryfine', 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)}
libxc_term = ULibXCLDA('x')
terms1 = [
UTwoIndexTerm(kin, 'kin'),
UDirectTerm(er, 'hartree'),
UGridGroup(mol.obasis, grid, [libxc_term]),
UTwoIndexTerm(na, 'ne'),
]
ham1 = UEffHam(terms1, external)
builtin_term = UDiracExchange()
terms2 = [
UTwoIndexTerm(kin, 'kin'),
UDirectTerm(er, 'hartree'),
UGridGroup(mol.obasis, grid, [builtin_term]),
UTwoIndexTerm(na, 'ne'),
]
ham2 = UEffHam(terms2, external)
# Compare the potential computed by libxc with the builtin implementation
energy1, focks1 = helper_compute(ham1, mol.lf, mol.exp_alpha, mol.exp_beta)
energy2, focks2 = helper_compute(ham2, mol.lf, mol.exp_alpha, mol.exp_beta)
libxc_pot = ham1.cache.load('pot_libxc_lda_x_both')[:,0]
builtin_pot = ham2.cache.load('pot_x_dirac_alpha')
# Libxc apparently approximates values of the potential below 1e-4 with zero.
assert abs(libxc_pot - builtin_pot).max() < 1e-4
# Check of the libxc energy matches our implementation
assert abs(energy1 - energy2) < 1e-10
ex1 = ham1.cache['energy_libxc_lda_x']
ex2 = ham2.cache['energy_x_dirac']
assert abs(ex1 - ex2) < 1e-10
# The convergence should be reasonable, not perfect because of limited
# precision in Gaussian fchk file:
assert convergence_error_eigen(ham1, mol.lf, olp, mol.exp_alpha, mol.exp_beta) < 1e-5
assert convergence_error_eigen(ham2, mol.lf, olp, mol.exp_alpha, mol.exp_beta) < 1e-5
occ_model = AufbauOccModel(2, 1)
for ham in ham1, ham2:
# Converge from scratch
check_solve(ham, scf_solver, occ_model, mol.lf, olp, kin, na, mol.exp_alpha, mol.exp_beta)
# test orbital energies
expected_energies = np.array([
-4.93959157E-01, -1.13961330E-01, 2.38730924E-01, 7.44216538E-01,
8.30143356E-01, 1.46613581E+00
])
assert abs(mol.exp_alpha.energies - expected_energies).max() < 1e-5
expected_energies = np.array([
-4.34824166E-01, 1.84114514E-04, 3.24300545E-01, 7.87622756E-01,
9.42415831E-01, 1.55175481E+00
])
assert abs(mol.exp_beta.energies - expected_energies).max() < 1e-5
ham.compute_energy()
# compare with g09
assert abs(ham.cache['energy_ne'] - -6.832069993374E+00) < 1e-5
assert abs(ham.cache['energy_kin'] - 1.870784279014E+00) < 1e-5
assert abs(ham.cache['energy'] - -1.412556114057104E+00) < 1e-5
assert abs(ham.cache['energy_nn'] - 1.8899186021) < 1e-8
assert abs(ham1.cache['energy_hartree'] + ham1.cache['energy_libxc_lda_x'] - 1.658810998195E+00) < 1e-6
assert abs(ham2.cache['energy_hartree'] + ham2.cache['energy_x_dirac'] - 1.658810998195E+00) < 1e-6
def check_n2_cs_hfs(scf_solver):
fn_fchk = context.get_fn('test/n2_hfs_sto3g.fchk')
mol = IOData.from_file(fn_fchk)
grid = BeckeMolGrid(mol.coordinates, mol.numbers, mol.pseudo_numbers, 'veryfine', 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)}
libxc_term = RLibXCLDA('x')
terms1 = [
RTwoIndexTerm(kin, 'kin'),
RDirectTerm(er, 'hartree'),
RGridGroup(mol.obasis, grid, [libxc_term]),
RTwoIndexTerm(na, 'ne'),
]
ham1 = REffHam(terms1, external)
builtin_term = RDiracExchange()
terms2 = [
RTwoIndexTerm(kin, 'kin'),
RDirectTerm(er, 'hartree'),
RGridGroup(mol.obasis, grid, [builtin_term]),
RTwoIndexTerm(na, 'ne'),
]
ham2 = REffHam(terms2, external)
# Compare the potential computed by libxc with the builtin implementation
energy1, focks1 = helper_compute(ham1, mol.lf, mol.exp_alpha)
energy2, focks2 = helper_compute(ham2, mol.lf, mol.exp_alpha)
libxc_pot = ham1.cache.load('pot_libxc_lda_x_alpha')
builtin_pot = ham2.cache.load('pot_x_dirac_alpha')
# Libxc apparently approximates values of the potential below 1e-4 with zero.
assert abs(libxc_pot - builtin_pot).max() < 1e-4
# Check of the libxc energy matches our implementation
assert abs(energy1 - energy2) < 1e-10
ex1 = ham1.cache['energy_libxc_lda_x']
ex2 = ham2.cache['energy_x_dirac']
assert abs(ex1 - ex2) < 1e-10
# The convergence should be reasonable, not perfect because of limited
# precision in Gaussian fchk file:
assert convergence_error_eigen(ham1, mol.lf, olp, mol.exp_alpha) < 1e-5
assert convergence_error_eigen(ham2, mol.lf, olp, mol.exp_alpha) < 1e-5
occ_model = AufbauOccModel(7)
for ham in ham1, ham2:
# Converge from scratch
check_solve(ham, scf_solver, occ_model, mol.lf, olp, kin, na, mol.exp_alpha)
# test orbital energies
expected_energies = np.array([
-1.37107053E+01, -1.37098006E+01, -9.60673085E-01, -3.57928483E-01,
-3.16017655E-01, -3.16017655E-01, -2.12998316E-01, 6.84030479E-02,
6.84030479E-02, 7.50192517E-01,
])
assert abs(mol.exp_alpha.energies - expected_energies).max() < 3e-5
ham.compute_energy()
assert abs(ham.cache['energy_ne'] - -2.981579553570E+02) < 1e-5
assert abs(ham.cache['energy_kin'] - 1.061620887711E+02) < 1e-5
assert abs(ham.cache['energy'] - -106.205213597) < 1e-4
assert abs(ham.cache['energy_nn'] - 23.3180604505) < 1e-8
assert abs(ham1.cache['energy_hartree'] + ham1.cache['energy_libxc_lda_x'] - 6.247259253877E+01) < 1e-4
assert abs(ham2.cache['energy_hartree'] + ham2.cache['energy_x_dirac'] - 6.247259253877E+01) < 1e-4