def test_rks_energy_df(xc, atomzs, dist, energy_true, grid):
# test to see if the energy calculated by DQC agrees with PySCF using
# density fitting
poss = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]],
dtype=dtype) * dist
mol = Mol((atomzs, poss), basis="6-311++G**", dtype=dtype, grid=grid)
mol.densityfit(method="coulomb", auxbasis="def2-sv(p)-jkfit")
qc = KS(mol, xc=xc, restricted=True).run()
ene = qc.energy()
assert torch.allclose(ene, ene * 0 + energy_true)
def test_uks_energy_mols_df(xc, atomzs, dist, spin, energy_true):
# check the energy of molecules with non-0 spins with density fitting
poss = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]],
dtype=dtype) * dist
mol = Mol((atomzs, poss),
basis="6-311++G**",
grid=3,
dtype=dtype,
spin=spin)
mol.densityfit(method="coulomb", auxbasis="def2-sv(p)-jkfit")
qc = KS(mol, xc=xc, restricted=False).run()
ene = qc.energy()
assert torch.allclose(ene, ene * 0 + energy_true, rtol=1e-6, atol=0.0)
def test_rks_energy_df(xc, atomzs, dist, energy_true, grid):
# test to see if the energy calculated by DQC agrees with PySCF using
# density fitting
if xc == "mgga_x_scan":
if atomzs == [1, 1]:
pytest.xfail("Psi4 and PySCF don't converge")
for lowmem in [False, True]: # simulating low memory condition
if lowmem:
init_value = config.THRESHOLD_MEMORY
config.THRESHOLD_MEMORY = 1000000 # 1 MB
poss = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]],
dtype=dtype) * dist
mol = Mol((atomzs, poss), basis="6-311++G**", dtype=dtype, grid=grid)
mol.densityfit(method="coulomb", auxbasis="def2-sv(p)-jkfit")
qc = KS(mol, xc=xc, restricted=True).run()
ene = qc.energy()
# error to be < 1 kcal/mol
assert torch.allclose(ene, ene * 0 + energy_true, atol=1.1e-3, rtol=0)
if lowmem:
# restore the value
config.THRESHOLD_MEMORY = init_value
