def test_adc2_shift_invert_singlets(self):
refdata = cache.reference_data["h2o_sto3g"]
matrix = adcc.AdcMatrix("adc2", LazyMp(cache.refstate["h2o_sto3g"]))
conv_tol = 1e-5
shift = -0.5
# Construct shift and inverted matrix:
shinv = IterativeInverse(AdcMatrixShifted(matrix, shift),
conv_tol=conv_tol / 10,
Pinv=JacobiPreconditioner,
callback=cg_print)
# Solve for singlets
guesses = adcc.guesses_singlet(matrix, n_guesses=5, block="ph")
symm = IndexSpinSymmetrisation(matrix, enforce_spin_kind="singlet")
res = lanczos(shinv,
guesses,
n_ep=5,
callback=la_print,
explicit_symmetrisation=symm)
assert res.converged
# Undo spectral transformation and compare
eigenvalues = sorted(1 / res.eigenvalues - shift)
ref_singlets = refdata["adc2"]["singlet"]["eigenvalues"][:5]
assert eigenvalues == approx(ref_singlets)
def test_adc2_singlets(self):
refdata = cache.reference_data["h2o_sto3g"]
matrix = adcc.AdcMatrix("adc2", LazyMp(cache.refstate["h2o_sto3g"]))
# Solve for singlets
guesses = adcc.guesses_singlet(matrix, n_guesses=9, block="s")
res = jacobi_davidson(matrix, guesses, n_ep=9)
ref_singlets = refdata["adc2"]["singlet"]["eigenvalues"]
assert res.converged
assert res.eigenvalues == approx(ref_singlets)
def test_adc2_singlets(self):
refdata = cache.reference_data["h2o_sto3g"]
matrix = adcc.AdcMatrix("adc2", LazyMp(cache.refstate["h2o_sto3g"]))
# Solve for singlets
guesses = adcc.guesses_singlet(matrix, n_guesses=5, block="ph")
res = lanczos(matrix, guesses, n_ep=5, which="SM")
ref_singlets = refdata["adc2"]["singlet"]["eigenvalues"][:5]
assert res.converged
assert res.eigenvalues == approx(ref_singlets)
def test_diagonalise_adcmatrix(self):
from adcc.workflow import diagonalise_adcmatrix
refdata = cache.reference_data["h2o_sto3g"]
matrix = adcc.AdcMatrix("adc2",
adcc.LazyMp(cache.refstate["h2o_sto3g"]))
res = diagonalise_adcmatrix(matrix,
n_states=3,
kind="singlet",
eigensolver="davidson")
ref_singlets = refdata["adc2"]["singlet"]["eigenvalues"]
assert res.converged
assert res.eigenvalues == approx(ref_singlets[:3])
guesses = adcc.guesses_singlet(matrix, n_guesses=6, block="ph")
res = diagonalise_adcmatrix(matrix,
n_states=3,
kind="singlet",
guesses=guesses)
ref_singlets = refdata["adc2"]["singlet"]["eigenvalues"]
assert res.converged
assert res.eigenvalues == approx(ref_singlets[:3])
with pytest.raises(InputError): # Too low tolerance
res = diagonalise_adcmatrix(matrix,
n_states=9,
kind="singlet",
eigensolver="davidson",
conv_tol=1e-14)
with pytest.raises(InputError): # Wrong solver method
res = diagonalise_adcmatrix(matrix,
n_states=9,
kind="singlet",
eigensolver="blubber")
with pytest.raises(InputError): # Too few guesses
res = diagonalise_adcmatrix(matrix,
n_states=9,
kind="singlet",
eigensolver="davidson",
guesses=guesses)