def test_mol_cache():
# test if cache is stored correctly
cache_fname = "_temp_cache.h5"
# remove the cache if exists
if os.path.exists(cache_fname):
os.remove(cache_fname)
moldesc = "H 0 0 0"
mol = Mol(moldesc, basis="3-21G").set_cache(cache_fname)
h = mol.get_hamiltonian()
h.build()
# read the stored file
with h5py.File(cache_fname, "r") as f:
olp_cache = torch.as_tensor(f["hamilton/overlap"])
olp = h.get_overlap().fullmatrix()
assert torch.allclose(olp, olp_cache)
# try again with different atom, if cache is set, then it should be same
# as previous (although it is a wrong result)
# TODO: raise a warning if mol properties do not match
moldesc1 = "Li 0 0 0"
mol1 = Mol(moldesc1, basis="3-21G").set_cache(cache_fname)
h1 = mol1.get_hamiltonian()
h1.build()
# remove the cache
if os.path.exists(cache_fname):
os.remove(cache_fname)
olp1 = h1.get_overlap().fullmatrix()
assert torch.allclose(olp, olp1)
def test_mol_cache():
# test if cache is stored correctly
cache_fname = "_temp_cache.h5"
# remove the cache if exists
if os.path.exists(cache_fname):
os.remove(cache_fname)
moldesc = "H 0 0 0; H 1 0 0"
mol = Mol(moldesc, basis="3-21G").set_cache(cache_fname)
h = mol.get_hamiltonian()
h.build()
olp1 = h.get_overlap().fullmatrix()
# read the stored file
with h5py.File(cache_fname, "r") as f:
olp_cache = torch.as_tensor(f["hamilton/overlap"])
# test with a new exact same system, for sanity check
mol_copy = Mol(moldesc, basis="3-21G").set_cache(cache_fname)
h_copy = mol_copy.get_hamiltonian()
h_copy.build()
olp1_copy = h_copy.get_overlap().fullmatrix()
assert torch.allclose(olp1, olp1_copy)
# store a different cache into the same file, to make sure the next
# mol loads from it
with h5py.File(cache_fname, "w") as f:
olp_cache2 = 2 * olp_cache
f["hamilton/overlap"] = olp_cache2
# the same exact system, but the cache has been altered
mol = Mol(moldesc, basis="3-21G").set_cache(cache_fname)
h = mol.get_hamiltonian()
h.build()
olp2 = h.get_overlap().fullmatrix()
assert not torch.allclose(olp1, olp2)
# Try again with different positions, if cache is set, then it should be same
# as previous (although it is a wrong result)
# It must raise a warning for different system
with pytest.warns(UserWarning, match=r"Mismatch [ \w]*cached signature[ \w]*"):
moldesc1 = "H 1 0 0; H 0.5 0 0"
mol1 = Mol(moldesc1, basis="3-21G").set_cache(cache_fname, ["hamilton.overlap"])
h1 = mol1.get_hamiltonian()
h1.build()
# remove the cache
if os.path.exists(cache_fname):
os.remove(cache_fname)
def system1(request):
poss = torch.tensor([[0.0, 0.0, 0.8], [0.0, 0.0, -0.8]], dtype=dtype)
moldesc = ([1, 1], poss)
# untruncated grid is required to pass the hamiltonian tests
m = Mol(moldesc, basis="3-21G", dtype=dtype, grid=3,
orthogonalize_basis=request.param["basis_ortho"])
m.setup_grid()
hamilton = m.get_hamiltonian()
hamilton.build()
hamilton.setup_grid(m.get_grid())
return m
def dqcelrepxc(atom: str, spin: int = 0, xc: str = "lda_x", basis: str = BASIS):
# returns the electron repulsion and xc operator using DQC
mol = Mol(atom, spin=spin, basis=basis, dtype=DTYPE, grid=4)
qc = KS(mol, xc=xc)
hamilt = mol.get_hamiltonian()
if spin == 0:
# set dm to be an identity matrix
dm = torch.eye(hamilt.nao, dtype=DTYPE)
velrepxc = hamilt.get_vxc(dm) + hamilt.get_elrep(dm)
return velrepxc.fullmatrix()
else:
dmu = torch.eye(hamilt.nao, dtype=DTYPE)
dm = SpinParam(u=dmu, d=dmu)
vxc = hamilt.get_vxc(dm)
elrep = hamilt.get_elrep(dm.u + dm.d)
return torch.cat(((vxc.u + elrep).fullmatrix().unsqueeze(0),
(vxc.d + elrep).fullmatrix().unsqueeze(0)), dim=0)
