def test_mol_grid(moldesc):
# default: level 4
m = Mol(moldesc, basis="6-311++G**", dtype=dtype)
m.setup_grid()
rgrid = m.get_grid().get_rgrid()
# only check the dimension and the type because the number of grid points
# can be changed
assert rgrid.shape[1] == 3
assert m.get_grid().coord_type == "cart"
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 test_uks_grad_vxc(xccls, xcparams, atomz):
# check if the gradients w.r.t. vxc parameters are obtained correctly
poss = torch.tensor([[0.0, 0.0, 0.0]], dtype=dtype)
mol = Mol(([atomz], poss), basis="3-21G", dtype=dtype, grid=3)
mol.setup_grid()
def get_energy(*params):
xc = xccls(*params)
qc = KS(mol, xc=xc, restricted=False).run()
ene = qc.energy()
return ene
params = tuple(
torch.tensor(p, dtype=dtype).requires_grad_() for p in xcparams)
torch.autograd.gradcheck(get_energy, params)
def test_rks_grad_vext(xc, atomzs, dist, vext_p):
# check if the gradient w.r.t. vext is obtained correctly (only check 1st
# grad because we don't need 2nd grad at the moment)
poss = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]],
dtype=dtype) * dist
mol = Mol((atomzs, poss), basis="3-21G", dtype=dtype, grid=3)
mol.setup_grid()
rgrid = mol.get_grid().get_rgrid() # (ngrid, ndim)
rgrid_norm = torch.norm(rgrid, dim=-1) # (ngrid,)
def get_energy(vext_params):
vext = rgrid_norm * rgrid_norm * vext_params # (ngrid,)
qc = KS(mol, xc=xc, vext=vext, restricted=True).run()
ene = qc.energy()
return ene
vext_params = torch.tensor(vext_p, dtype=dtype).requires_grad_()
torch.autograd.gradcheck(get_energy, (vext_params, ))
