def test_DipoleDerivGenerator_by_SCF(self):
from pyxdh.Utilities.test_molecules import Mol_H2O2
H2O2 = Mol_H2O2()
mol = H2O2.mol
hf_eng = H2O2.hf_eng
hf_eng.kernel()
def mf_func(t, interval):
mf = scf.RHF(mol)
mf.get_hcore = lambda mol_: scf.rhf.get_hcore(mol_) - interval * mol_.intor("int1e_r")[t]
return mf.kernel()
generator = DipoleDerivGenerator(mf_func)
diff = NumericDiff(generator)
dip_nuc = np.einsum('i,ix->x', mol.atom_charges(), mol.atom_coords())
assert(np.allclose(
diff.derivative + dip_nuc,
hf_eng.dip_moment(unit="A.U."),
atol=1e-6, rtol=1e-4
))
generator = DipoleDerivGenerator(mf_func, stencil=5)
diff = NumericDiff(generator)
assert(np.allclose(
diff.derivative + dip_nuc,
hf_eng.dip_moment(unit="A.U."),
atol=1e-6, rtol=1e-4
))
def test_NucCoordDerivGenerator_by_SCFgrad(self):
from pyxdh.Utilities.test_molecules import Mol_H2O2
H2O2 = Mol_H2O2()
mol = H2O2.mol
hf_grad = H2O2.hf_grad
generator = NucCoordDerivGenerator(mol, lambda mol_: scf.RHF(mol_).run())
diff = NumericDiff(generator, lambda mf: mf.kernel())
assert(np.allclose(
hf_grad.kernel(),
diff.derivative.reshape(mol.natm, 3),
atol=1e-6, rtol=1e-4
))
generator = NucCoordDerivGenerator(mol, lambda mol_: scf.RHF(mol_).run(), stencil=5)
diff = NumericDiff(generator, lambda mf: mf.kernel())
assert(np.allclose(
hf_grad.kernel(),
diff.derivative.reshape(mol.natm, 3),
atol=1e-6, rtol=1e-4
))
import pickle
from pyscf import scf, gto, dft
from pyxdh.Utilities import NumericDiff, NucCoordDerivGenerator
def mol_to_eng(mol):
grids = dft.Grids(mol)
grids.atom_grid = (99, 590)
grids.build()
scf_eng = scf.RHF(mol)
scf_eng.conv_tol_grad = 1e-8
scf_eng.run()
nc_eng = dft.RKS(mol, xc="B3LYPg")
nc_eng.grids = grids
return nc_eng.energy_tot(dm=scf_eng.make_rdm1())
if __name__ == '__main__':
name = __file__.split("/")[-1].split(".")[0]
mol = gto.Mole(atom="N 0. 0. 0.; H .9 0. 0.; H 0. 1. 0.; H 0. 0. 1.1",
basis="6-31G",
verbose=0).build()
num_obj = NucCoordDerivGenerator(mol, mol_to_eng)
num_dif = NumericDiff(num_obj).derivative
with open(name + ".dat", "wb") as f:
pickle.dump(num_dif, f, pickle.HIGHEST_PROTOCOL)
import pickle
from pyxdh.Utilities.test_molecules import Mol_H2O2
from pyxdh.Utilities import NumericDiff, NucCoordDerivGenerator
from pyxdh.DerivOnce import GradMP2
def mol_to_grad_helper(mol):
print("Processing...")
H2O2 = Mol_H2O2(mol=mol, xc="0.53*HF + 0.47*B88, 0.73*LYP")
config = {
"scf_eng": H2O2.gga_eng,
"cc": 0.27
}
helper = GradMP2(config)
return helper
if __name__ == '__main__':
result_dict = {}
H2O2 = Mol_H2O2(xc="0.53*HF + 0.47*B88, 0.73*LYP")
mol = H2O2.mol
num_obj = NucCoordDerivGenerator(H2O2.mol, mol_to_grad_helper)
num_dif = NumericDiff(num_obj, lambda helper: helper.E_1.reshape(-1))
result_dict["hess"] = num_dif.derivative
with open("mp2_hessian_b2plyp.dat", "wb") as f:
pickle.dump(result_dict, f, pickle.HIGHEST_PROTOCOL)
mol = H2O2.mol
def get_hcore(mol=mol):
return scf.rhf.get_hcore(
mol) - interval * mol.intor("int1e_r")[component]
H2O2.hf_eng.get_hcore = get_hcore
H2O2.gga_eng.get_hcore = get_hcore
config = {"scf_eng": H2O2.hf_eng, "nc_eng": H2O2.gga_eng}
helper = GradNCDFT(config)
return helper
if __name__ == '__main__':
result_dict = {}
H2O2 = Mol_H2O2()
mol = H2O2.mol
num_obj = NucCoordDerivGenerator(H2O2.mol, mol_to_grad_helper)
num_dif = NumericDiff(num_obj, lambda helper: helper.eng)
result_dict["grad"] = num_dif.derivative
num_obj = DipoleDerivGenerator(dipole_generator)
num_dif = NumericDiff(num_obj, lambda helper: helper.eng)
dip_nuc = np.einsum("A, At -> t", mol.atom_charges(), mol.atom_coords())
result_dict["dipole"] = num_dif.derivative + dip_nuc
with open("ncdft_derivonce_hf_b3lyp.dat", "wb") as f:
pickle.dump(result_dict, f, pickle.HIGHEST_PROTOCOL)
N 0. 0. 0.
H 1.5 0. 0.2
H 0.1 1.2 0.
H 0. 0. 1.
"""
mol.basis = "cc-pVDZ"
mol.verbose = 0
mol.build()
def mol_to_eng_true(mol):
mf_scf = scf.RHF(mol).density_fit(auxbasis="cc-pVDZ-jkfit").run()
mf_mp2 = mp.MP2(mf_scf)
mf_mp2.with_df = df.DF(mol, auxbasis="cc-pVDZ-ri")
return mf_mp2.run().e_tot
def mol_to_eng_false(mol):
mf_scf = scf.RHF(mol).density_fit(auxbasis="cc-pVDZ-jkfit").run()
mf_mp2 = mp.MP2(mf_scf)
return mf_mp2.run().e_tot
# Check deviation of using jkfit and ri auxbasis in MP2 gradient
num_true = NucCoordDerivGenerator(mol, mol_to_eng_true)
num_false = NucCoordDerivGenerator(mol, mol_to_eng_false)
val_true = NumericDiff(num_true).derivative
val_false = NumericDiff(num_false).derivative
assert (np.allclose(val_true, val_false, atol=1e-5, rtol=1e-4) is False)
result_dict = {"grad": val_true}
with open("df_mp2_grad_mp2.dat", "wb") as f:
pickle.dump(result_dict, f, pickle.HIGHEST_PROTOCOL)