Beispiel #1
0
def test_uhf_energy_same_as_rhf(atomzs, dist, energy_true, variational):
    # test to see if uhf energy gets the same energy as rhf for non-polarized systems
    poss = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]], dtype=dtype) * dist
    mol = Mol((atomzs, poss), basis=basis, dtype=dtype)
    qc = HF(mol, restricted=False, variational=variational).run()
    ene = qc.energy()
    assert torch.allclose(ene, ene * 0 + energy_true, rtol=1e-8)
Beispiel #2
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def test_uhf_energy_atoms(atomz, spin, energy_true, variational):
    # check the energy of atoms with non-0 spins
    torch.manual_seed(123)
    poss = torch.tensor([[0.0, 0.0, 0.0]], dtype=dtype)
    mol = Mol(([atomz], poss), basis=basis, dtype=dtype, spin=spin)
    qc = HF(mol, restricted=False, variational=variational).run()
    ene = qc.energy()
    assert torch.allclose(ene, ene * 0 + energy_true, atol=0.0, rtol=1e-7)
Beispiel #3
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 def get_energy(atomz, with_ii=True):
     atomzs = [atomz, atomz]
     poss = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]], dtype=dtype)
     mol = Mol((atomzs, poss), basis=basis, spin=0, dtype=dtype)
     qc = HF(mol, restricted=True).run()
     ene = qc.energy()
     if with_ii:
         ene = ene - mol.get_nuclei_energy()
     return ene
Beispiel #4
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def test_uhf_energy_mols(atomzs, dist, spin, energy_true, variational):
    # check the energy of molecules with non-0 spins
    # NOTE: O2 iteration gets into excited state (probably)
    torch.manual_seed(123)
    poss = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]], dtype=dtype) * dist
    mol = Mol((atomzs, poss), basis=basis, dtype=dtype, spin=spin)
    qc = HF(mol, restricted=False, variational=variational).run()
    ene = qc.energy()
    assert torch.allclose(ene, ene * 0 + energy_true, rtol=1e-8, atol=0.0)
Beispiel #5
0
    def _test_hf_mols():
        poss = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]],
                            dtype=dtype) * dist
        poss = poss.requires_grad_()
        mol = Mol((atomzs, poss), basis="6-311++G**", dtype=dtype, spin=spin)
        qc = HF(mol).run()
        ene = qc.energy()

        # see if grad and backward create memleak
        grads = torch.autograd.grad(ene, (poss, ), create_graph=True)
        ene.backward()  # no create_graph here because of known pytorch's leak
Beispiel #6
0
def test_rhf_energy_overcomplete(atomzs, dist, energy_true, variational):
    # test to see if the energy calculated by DQC agrees with PySCF with
    # overcomplete basis
    torch.manual_seed(123)

    # double the basis to make it overcomplete
    basis0 = loadbasis(f"{atomzs[0]}:{basis}") * 2
    basis1 = loadbasis(f"{atomzs[1]}:{basis}") * 2

    poss = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]], dtype=dtype) * dist
    # orthogonalize_basis = True to handle overcomplete
    mol = Mol((atomzs, poss), basis=[basis0, basis1], dtype=dtype, orthogonalize_basis=True)
    qc = HF(mol, restricted=True, variational=variational).run()
    ene = qc.energy()
    assert torch.allclose(ene, ene * 0 + energy_true, rtol=1e-7)
Beispiel #7
0
def test_rhf_energy(atomzs, dist, energy_true, variational):
    # test to see if the energy calculated by DQC agrees with PySCF
    torch.manual_seed(123)

    # only set debugging mode only in one case to save time
    if atomzs == [1, 1]:
        xt.set_debug_mode(True)

    poss = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]], dtype=dtype) * dist
    mol = Mol((atomzs, poss), basis=basis, dtype=dtype)
    qc = HF(mol, restricted=True, variational=variational).run()
    ene = qc.energy()
    assert torch.allclose(ene, ene * 0 + energy_true, rtol=1e-7)

    if atomzs == [1, 1]:
        xt.set_debug_mode(False)
Beispiel #8
0
 def get_energy(dist_tensor):
     poss_tensor = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]], dtype=dtype) * dist_tensor
     mol = Mol((atomzs, poss_tensor), basis=basis, dtype=dtype)
     qc = HF(mol, restricted=True, variational=variational).run(
         fwd_options=fwd_options, bck_options=bck_options)
     return qc.energy()
Beispiel #9
0
 def get_energy(atomzs):
     poss = torch.tensor([[-0.5, 0.0, 0.0], [0.5, 0.0, 0.0]], dtype=dtype)
     mol = Mol((atomzs, poss), basis=basis, spin=0, dtype=dtype)
     qc = HF(mol, restricted=True).run()
     ene = qc.energy() - mol.get_nuclei_energy()
     return ene
Beispiel #10
0
def test_instability_check():
    # check if is_orb_min returns False for a known excited state

    atomzs = [8, 8]  # O2
    atomposs = torch.tensor([[-1.0, 0., 0.], [1.0, 0., 0.]], dtype=dtype)
    mol = Mol(moldesc=(atomzs, atomposs), basis="3-21G", dtype=dtype, spin=2)

    # known excited state of O2
    dm0 = SpinParam(u=torch.tensor([[ 1.4374e-02, -6.5933e-17, -1.3556e-17,  1.1258e-17, -1.8834e-03,
          2.1021e-16, -2.3638e-13, -2.6444e-12,  2.2637e-02, -4.9366e-17,
         -5.9629e-02,  1.8549e-17,  5.0367e-17, -1.5571e-16, -5.6398e-12,
         -6.9681e-12, -1.0048e-01,  1.2257e-15],
        [-6.5933e-17,  2.3236e-02, -2.1167e-12, -5.2740e-13, -7.3181e-17,
         -1.0873e-02, -8.3590e-17,  1.2056e-17, -9.3082e-17,  8.0595e-02,
          3.6757e-16,  9.1094e-12, -3.9485e-13,  1.2539e-01, -9.1129e-18,
         -2.8306e-17, -7.9300e-17, -1.8937e-02],
        [-1.3556e-17, -2.1167e-12,  4.2572e-02,  8.2444e-04, -1.2280e-16,
          1.5572e-12, -1.5306e-17,  1.7276e-16,  2.7191e-18,  6.4115e-13,
          1.1101e-16, -1.9820e-01, -3.8413e-02, -1.7302e-12, -7.2395e-16,
         -5.6557e-16,  1.6237e-17,  2.3304e-13],
        [ 1.1258e-17, -5.2740e-13,  8.2444e-04,  3.9468e-02,  6.1344e-17,
          3.8795e-13,  1.2361e-16, -6.1389e-18,  8.2929e-17,  1.5994e-13,
         -1.2242e-16, -4.0719e-02,  1.9040e-01, -4.3109e-13,  2.8388e-17,
         -3.7710e-16,  9.6793e-17,  5.8088e-14],
        [-1.8834e-03, -7.3181e-17, -1.2280e-16,  6.1344e-17,  2.6433e-04,
         -1.9302e-19, -1.0905e-13, -1.2203e-12, -1.8316e-03, -1.1234e-16,
          4.4601e-03,  5.1546e-16,  4.1761e-16, -5.2628e-16, -7.2761e-12,
         -8.9879e-12,  1.5410e-02, -1.5680e-16],
        [ 2.1021e-16, -1.0873e-02,  1.5572e-12,  3.8795e-13, -1.9302e-19,
          1.9720e-02, -4.5050e-18,  4.1017e-17,  5.5749e-17, -4.4163e-02,
         -3.6973e-16, -6.9926e-12,  3.0281e-13, -3.5499e-02,  5.1310e-16,
         -3.6489e-17, -2.1794e-16,  1.2650e-01],
        [-2.3638e-13, -8.3590e-17, -1.5306e-17,  1.2361e-16, -1.0905e-13,
         -4.5050e-18,  1.7231e-02, -1.7505e-04, -9.2770e-14, -3.9129e-16,
          3.4332e-13, -4.4355e-18,  8.8915e-16, -4.0986e-16, -9.3919e-02,
          9.0076e-02,  1.0851e-12,  1.3598e-16],
        [-2.6444e-12,  1.2056e-17,  1.7276e-16, -6.1389e-18, -1.2203e-12,
          4.1017e-17, -1.7505e-04,  1.5289e-02, -1.0377e-12,  1.2434e-17,
          3.8408e-12, -1.3267e-15, -1.7575e-16,  1.1638e-16,  8.5865e-02,
          8.7648e-02,  1.2149e-11,  4.9825e-17],
        [ 2.2637e-02, -9.3082e-17,  2.7191e-18,  8.2929e-17, -1.8316e-03,
          5.5749e-17, -9.2770e-14, -1.0377e-12,  1.0891e-01,  5.4901e-16,
         -3.1041e-01, -2.1589e-16,  9.7090e-17, -2.0236e-16,  2.5871e-12,
          3.1957e-12, -1.3275e-02, -3.7240e-16],
        [-4.9366e-17,  8.0595e-02,  6.4115e-13,  1.5994e-13, -1.1234e-16,
         -4.4163e-02, -3.9129e-16,  1.2434e-17,  5.4901e-16,  9.9128e-01,
          6.4760e-16, -9.9216e-13,  4.2760e-14, -1.2043e-02,  1.5942e-16,
         -1.0898e-16,  1.7759e-16,  7.3878e-03],
        [-5.9629e-02,  3.6757e-16,  1.1101e-16, -1.2242e-16,  4.4601e-03,
         -3.6973e-16,  3.4332e-13,  3.8408e-12, -3.1041e-01,  6.4760e-16,
          8.8722e-01, -4.5809e-17,  1.7987e-16, -2.7821e-16,  2.1268e-13,
          2.6265e-13, -1.1616e-02, -1.0344e-16],
        [ 1.8549e-17,  9.1094e-12, -1.9820e-01, -4.0719e-02,  5.1546e-16,
         -6.9926e-12, -4.4355e-18, -1.3267e-15, -2.1589e-16, -9.9216e-13,
         -4.5809e-17,  9.5723e-01,  1.5197e-04, -1.8852e-12,  1.6341e-16,
          1.0968e-16, -7.3105e-17,  1.1978e-12],
        [ 5.0367e-17, -3.9485e-13, -3.8413e-02,  1.9040e-01,  4.1761e-16,
          3.0281e-13,  8.8915e-16, -1.7575e-16,  9.7090e-17,  4.2760e-14,
          1.7987e-16,  1.5197e-04,  9.6073e-01,  8.1569e-14, -4.7992e-16,
          3.4136e-16,  3.3857e-17, -5.1807e-14],
        [-1.5571e-16,  1.2539e-01, -1.7302e-12, -4.3109e-13, -5.2628e-16,
         -3.5499e-02, -4.0986e-16,  1.1638e-16, -2.0236e-16, -1.2043e-02,
         -2.7821e-16, -1.8852e-12,  8.1569e-14,  9.8251e-01,  1.5091e-16,
         -1.8316e-16, -1.7641e-16,  7.2007e-03],
        [-5.6398e-12, -9.1129e-18, -7.2395e-16,  2.8388e-17, -7.2761e-12,
          5.1310e-16, -9.3919e-02,  8.5865e-02,  2.5871e-12,  1.5942e-16,
          2.1268e-13,  1.6341e-16, -4.7992e-16,  1.5091e-16,  9.8353e-01,
          9.6527e-04, -1.3954e-12,  5.5578e-17],
        [-6.9681e-12, -2.8306e-17, -5.6557e-16, -3.7710e-16, -8.9879e-12,
         -3.6489e-17,  9.0076e-02,  8.7648e-02,  3.1957e-12, -1.0898e-16,
          2.6265e-13,  1.0968e-16,  3.4136e-16, -1.8316e-16,  9.6527e-04,
          9.8395e-01, -1.7236e-12,  1.2375e-16],
        [-1.0048e-01, -7.9300e-17,  1.6237e-17,  9.6793e-17,  1.5410e-02,
         -2.1794e-16,  1.0851e-12,  1.2149e-11, -1.3275e-02,  1.7759e-16,
         -1.1616e-02, -7.3105e-17,  3.3857e-17, -1.7641e-16, -1.3954e-12,
         -1.7236e-12,  9.8924e-01, -1.2411e-16],
        [ 1.2257e-15, -1.8937e-02,  2.3304e-13,  5.8088e-14, -1.5680e-16,
          1.2650e-01,  1.3598e-16,  4.9825e-17, -3.7240e-16,  7.3878e-03,
         -1.0344e-16,  1.1978e-12, -5.1807e-14,  7.2007e-03,  5.5578e-17,
          1.2375e-16, -1.2411e-16,  9.8325e-01]], dtype=torch.float64),
        d=torch.tensor([[ 2.2958e-02,  5.9133e-17, -4.3465e-17, -3.5203e-17, -5.2141e-03,
          1.9909e-16,  1.6717e-14,  1.8684e-13,  3.9446e-02,  2.7848e-16,
         -6.6246e-02,  1.2336e-17, -2.6465e-16,  8.6160e-16, -2.8860e-11,
         -3.5653e-11, -1.2829e-01,  1.5006e-15],
        [ 5.9133e-17,  4.6415e-03, -1.3965e-10, -3.4794e-11, -2.6531e-17,
         -7.5184e-03,  6.8007e-17, -1.8251e-17,  1.2508e-16,  4.6059e-02,
         -5.7129e-18,  6.4528e-10, -2.7957e-11, -7.4002e-03, -7.1789e-16,
          5.1820e-16, -9.0260e-16, -4.8860e-02],
        [-4.3465e-17, -1.3965e-10,  4.3859e-02,  1.0928e-02, -6.8315e-17,
          6.4583e-11,  1.4620e-17,  1.7630e-16, -1.1049e-16, -2.1608e-10,
          2.5101e-16, -2.0430e-01,  8.8512e-03, -7.6096e-10, -7.9051e-16,
         -5.0478e-16,  1.2440e-16, -8.9813e-11],
        [-3.5203e-17, -3.4794e-11,  1.0928e-02,  2.7228e-03, -1.0420e-17,
          1.6091e-11,  3.7273e-17,  1.7772e-17, -2.7800e-17, -5.3837e-11,
          5.6712e-18, -5.0902e-02,  2.2053e-03, -1.8960e-10, -7.8317e-16,
          8.0795e-17,  2.4562e-16, -2.2377e-11],
        [-5.2141e-03, -2.6531e-17, -6.8315e-17, -1.0420e-17,  1.2314e-03,
         -5.6992e-18, -1.4157e-13, -1.5837e-12, -7.1148e-03,  7.0004e-17,
          9.4083e-03,  3.5543e-16,  4.2740e-17, -2.7677e-16, -2.1264e-12,
         -2.6263e-12,  3.2613e-02,  1.1833e-18],
        [ 1.9909e-16, -7.5184e-03,  6.4583e-11,  1.6091e-11, -5.6992e-18,
          1.6029e-02, -2.4000e-17,  2.0307e-17,  1.2229e-16, -3.2365e-02,
         -2.4677e-16, -2.8977e-10,  1.2554e-11, -5.3257e-03,  2.8397e-16,
         -1.7180e-16, -8.3203e-17,  1.2099e-01],
        [ 1.6717e-14,  6.8007e-17,  1.4620e-17,  3.7273e-17, -1.4157e-13,
         -2.4000e-17,  3.7952e-03,  7.4301e-04, -4.3281e-13,  4.4101e-16,
         -1.6173e-12, -4.2060e-17,  2.1463e-16,  2.1636e-16, -4.1059e-02,
          4.5765e-02, -2.8999e-12,  1.0801e-16],
        [ 1.8684e-13, -1.8251e-17,  1.7630e-16,  1.7772e-17, -1.5837e-12,
          2.0307e-17,  7.4301e-04,  1.2040e-02, -4.8418e-12, -1.2562e-16,
         -1.8091e-11, -1.2667e-15, -4.7435e-17, -1.7249e-16,  7.2754e-02,
          8.1250e-02, -3.2434e-11,  8.1554e-17],
        [ 3.9446e-02,  1.2508e-16, -1.1049e-16, -2.7800e-17, -7.1148e-03,
          1.2229e-16, -4.3281e-13, -4.8418e-12,  1.3971e-01,  1.1579e-15,
         -3.3375e-01, -4.4814e-17, -5.2377e-16,  1.4905e-15, -7.4596e-11,
         -9.2152e-11, -8.4825e-02,  5.5948e-16],
        [ 2.7848e-16,  4.6059e-02, -2.1608e-10, -5.3837e-11,  7.0004e-17,
         -3.2365e-02,  4.4101e-16, -1.2562e-16,  1.1579e-15,  9.2048e-01,
         -1.0153e-16,  9.8356e-10, -4.2613e-11, -2.6337e-01, -1.3755e-15,
          6.0548e-16, -1.5088e-15, -2.5713e-02],
        [-6.6246e-02, -5.7129e-18,  2.5101e-16,  5.6712e-18,  9.4083e-03,
         -2.4677e-16, -1.6173e-12, -1.8091e-11, -3.3375e-01, -1.0153e-16,
          8.6354e-01,  9.1119e-17,  9.7805e-16, -3.0709e-15, -3.0487e-11,
         -3.7662e-11, -4.4392e-02, -2.2345e-16],
        [ 1.2336e-17,  6.4528e-10, -2.0430e-01, -5.0902e-02,  3.5543e-16,
         -2.8977e-10, -4.2060e-17, -1.2667e-15, -4.4814e-17,  9.8356e-10,
          9.1119e-17,  9.5163e-01, -4.1229e-02,  3.5411e-09,  3.4000e-16,
         -7.3179e-17,  4.4148e-17,  5.0171e-10],
        [-2.6465e-16, -2.7957e-11,  8.8512e-03,  2.2053e-03,  4.2740e-17,
          1.2554e-11,  2.1463e-16, -4.7435e-17, -5.2377e-16, -4.2613e-11,
          9.7805e-16, -4.1229e-02,  1.7862e-03, -1.5342e-10, -3.2950e-15,
          1.6944e-15,  1.3384e-15, -2.1736e-11],
        [ 8.6160e-16, -7.4002e-03, -7.6096e-10, -1.8960e-10, -2.7677e-16,
         -5.3257e-03,  2.1636e-16, -1.7249e-16,  1.4905e-15, -2.6337e-01,
         -3.0709e-15,  3.5411e-09, -1.5342e-10,  8.9643e-02, -5.2073e-15,
          2.4346e-15, -5.8143e-15, -1.1027e-01],
        [-2.8860e-11, -7.1789e-16, -7.9051e-16, -7.8317e-16, -2.1264e-12,
          2.8397e-16, -4.1059e-02,  7.2754e-02, -7.4596e-11, -1.3755e-15,
         -3.0487e-11,  3.4000e-16, -3.2950e-15, -5.2073e-15,  9.9295e-01,
         -4.0971e-03, -9.4472e-12, -6.9572e-16],
        [-3.5653e-11,  5.1820e-16, -5.0478e-16,  8.0795e-17, -2.6263e-12,
         -1.7180e-16,  4.5765e-02,  8.1250e-02, -9.2152e-11,  6.0548e-16,
         -3.7662e-11, -7.3179e-17,  1.6944e-15,  2.4346e-15, -4.0971e-03,
          9.9121e-01, -1.1671e-11,  5.1711e-16],
        [-1.2829e-01, -9.0260e-16,  1.2440e-16,  2.4562e-16,  3.2613e-02,
         -8.3203e-17, -2.8999e-12, -3.2434e-11, -8.4825e-02, -1.5088e-15,
         -4.4392e-02,  4.4148e-17,  1.3384e-15, -5.8143e-15, -9.4472e-12,
         -1.1671e-11,  9.7256e-01, -8.6388e-16],
        [ 1.5006e-15, -4.8860e-02, -8.9813e-11, -2.2377e-11,  1.1833e-18,
          1.2099e-01,  1.0801e-16,  8.1554e-17,  5.5948e-16, -2.5713e-02,
         -2.2345e-16,  5.0171e-10, -2.1736e-11, -1.1027e-01, -6.9572e-16,
          5.1711e-16, -8.6388e-16,  9.6920e-01]], dtype=torch.float64))
    qc = HF(mol).run(dm0=dm0)
    ene = qc.energy()
    assert not is_orb_min(qc)

    # known ground state of O2
    dm1 = SpinParam(u=torch.tensor([[ 2.0683e-02, -1.4420e-16,  1.8209e-17, -8.9218e-18, -1.1292e-03,
          2.4994e-16,  1.3424e-17, -1.3778e-17,  3.1936e-02, -2.0260e-16,
         -6.6341e-02,  5.2691e-17, -3.3777e-17, -6.2702e-16, -1.9655e-17,
         -1.3381e-15, -1.2179e-01,  1.1911e-15],
        [-1.4420e-16,  2.5205e-02,  1.5440e-18, -1.5470e-18, -1.2490e-16,
         -1.1032e-02, -4.4534e-17, -4.0071e-17, -1.5877e-16,  8.2001e-02,
          3.1857e-16,  3.3331e-16, -2.5398e-16,  1.3203e-01, -1.0306e-16,
          1.1040e-17, -7.2944e-17, -1.7112e-02],
        [ 1.8209e-17,  1.5440e-18,  3.6441e-02,  1.0879e-13, -1.1019e-16,
         -6.7960e-17,  2.3446e-17,  1.4381e-16, -1.3948e-17,  6.9656e-17,
          6.2990e-17, -1.8362e-01, -3.7391e-02,  4.0401e-16, -7.8922e-16,
         -5.2022e-16, -2.4977e-16, -2.3482e-17],
        [-8.9218e-18, -1.5470e-18,  1.0879e-13,  3.6441e-02,  5.4484e-17,
         -1.0500e-16,  8.8368e-17, -3.8825e-17,  3.4381e-17,  2.4618e-16,
         -1.2742e-16, -3.7391e-02,  1.8362e-01,  2.4770e-16, -2.9739e-16,
         -2.9106e-16,  9.1167e-17,  5.5659e-17],
        [-1.1292e-03, -1.2490e-16, -1.1019e-16,  5.4484e-17,  7.1554e-05,
          1.1112e-17,  2.0896e-17, -8.7962e-18, -8.9524e-04, -1.6020e-16,
          1.0596e-03,  4.8917e-16,  3.8244e-16, -8.4877e-16, -1.6487e-16,
          1.5251e-16,  8.2674e-03, -2.0908e-16],
        [ 2.4994e-16, -1.1032e-02, -6.7960e-17, -1.0500e-16,  1.1112e-17,
          1.9013e-02, -3.8712e-18, -2.4730e-18,  1.3092e-16, -4.3091e-02,
         -3.6917e-16,  3.2346e-16, -3.8502e-16, -3.7068e-02,  2.5626e-17,
         -2.1922e-16, -3.1125e-16,  1.2369e-01],
        [ 1.3424e-17, -4.4534e-17,  2.3446e-17,  8.8368e-17,  2.0896e-17,
         -3.8712e-18,  1.7462e-02, -1.4887e-11,  4.7461e-18, -8.8927e-17,
         -4.0357e-17, -1.1873e-17,  5.0543e-16, -3.6232e-16, -9.4065e-02,
          9.1151e-02, -1.0645e-15,  1.8543e-17],
        [-1.3778e-17, -4.0071e-17,  1.4381e-16, -3.8825e-17, -8.7962e-18,
         -2.4730e-18, -1.4887e-11,  1.7462e-02, -1.2512e-17, -1.1642e-16,
          5.8123e-17, -1.3728e-15, -2.0690e-16, -1.4420e-16,  9.1151e-02,
          9.4065e-02, -9.7118e-16,  4.9315e-17],
        [ 3.1936e-02, -1.5877e-16, -1.3948e-17,  3.4381e-17, -8.9524e-04,
          1.3092e-16,  4.7461e-18, -1.2512e-17,  1.2198e-01,  5.5052e-16,
         -3.2192e-01,  7.7566e-18, -6.8423e-17, -6.6652e-16,  1.3687e-16,
         -5.6171e-16, -4.9446e-02, -1.5290e-16],
        [-2.0260e-16,  8.2001e-02,  6.9656e-17,  2.4618e-16, -1.6020e-16,
         -4.3091e-02, -8.8927e-17, -1.1642e-16,  5.5052e-16,  9.9113e-01,
          6.4477e-16, -1.2044e-16, -2.2427e-16, -1.2837e-02,  1.0462e-16,
         -6.5168e-17,  8.9549e-17,  6.9992e-03],
        [-6.6341e-02,  3.1857e-16,  6.2990e-17, -1.2742e-16,  1.0596e-03,
         -3.6917e-16, -4.0357e-17,  5.8123e-17, -3.2192e-01,  6.4477e-16,
          8.7574e-01,  9.2656e-17,  1.0997e-16, -4.7293e-16,  1.4328e-17,
         -2.7222e-16, -2.8003e-02, -4.2238e-17],
        [ 5.2691e-17,  3.3331e-16, -1.8362e-01, -3.7391e-02,  4.8917e-16,
          3.2346e-16, -1.1873e-17, -1.3728e-15,  7.7566e-18, -1.2044e-16,
          9.2656e-17,  9.6356e-01,  1.9387e-14, -1.1383e-16, -3.7055e-16,
          2.4158e-16, -8.2073e-17, -9.3030e-17],
        [-3.3777e-17, -2.5398e-16, -3.7391e-02,  1.8362e-01,  3.8244e-16,
         -3.8502e-16,  5.0543e-16, -2.0690e-16, -6.8423e-17, -2.2427e-16,
          1.0997e-16,  1.9387e-14,  9.6356e-01, -1.2755e-16, -4.3096e-16,
          2.4102e-16, -3.6390e-17,  9.8892e-17],
        [-6.2702e-16,  1.3203e-01,  4.0401e-16,  2.4770e-16, -8.4877e-16,
         -3.7068e-02, -3.6232e-16, -1.4420e-16, -6.6652e-16, -1.2837e-02,
         -4.7293e-16, -1.1383e-16, -1.2755e-16,  9.8060e-01,  5.0343e-17,
          3.6493e-17, -2.1165e-16,  7.1881e-03],
        [-1.9655e-17, -1.0306e-16, -7.8922e-16, -2.9739e-16, -1.6487e-16,
          2.5626e-17, -9.4065e-02,  9.1151e-02,  1.3687e-16,  1.0462e-16,
          1.4328e-17, -3.7055e-16, -4.3096e-16,  5.0343e-17,  9.8254e-01,
          8.2092e-11, -1.4172e-18,  2.2854e-17],
        [-1.3381e-15,  1.1040e-17, -5.2022e-16, -2.9106e-16,  1.5251e-16,
         -2.1922e-16,  9.1151e-02,  9.4065e-02, -5.6171e-16, -6.5168e-17,
         -2.7222e-16,  2.4158e-16,  2.4102e-16,  3.6493e-17,  8.2092e-11,
          9.8254e-01,  4.9448e-18,  9.8734e-17],
        [-1.2179e-01, -7.2944e-17, -2.4977e-16,  9.1167e-17,  8.2674e-03,
         -3.1125e-16, -1.0645e-15, -9.7118e-16, -4.9446e-02,  8.9549e-17,
         -2.8003e-02, -8.2073e-17, -3.6390e-17, -2.1165e-16, -1.4172e-18,
          4.9448e-18,  9.8153e-01, -1.4542e-16],
        [ 1.1911e-15, -1.7112e-02, -2.3482e-17,  5.5659e-17, -2.0908e-16,
          1.2369e-01,  1.8543e-17,  4.9315e-17, -1.5290e-16,  6.9992e-03,
         -4.2238e-17, -9.3030e-17,  9.8892e-17,  7.1881e-03,  2.2854e-17,
          9.8734e-17, -1.4542e-16,  9.8405e-01]], dtype=torch.float64),
        d=torch.tensor([[ 1.2641e-02, -1.5358e-16,  4.7752e-17, -8.9111e-18, -4.4876e-03,
          2.2773e-16,  6.1698e-18, -7.4570e-18,  2.0856e-02, -6.6447e-17,
         -5.7084e-02, -4.3042e-17, -5.2418e-17, -4.8881e-16, -1.3178e-17,
         -1.0353e-15, -9.3633e-02,  1.2411e-15],
        [-1.5358e-16,  3.0229e-02,  6.8437e-17,  3.5949e-17, -1.0507e-16,
         -1.5192e-02, -1.7095e-17, -2.0281e-17, -2.3740e-16,  8.8960e-02,
          5.2239e-16,  2.5002e-17, -8.2051e-17,  1.4194e-01,  1.1475e-16,
          1.5330e-17, -3.2950e-17, -3.1999e-02],
        [ 4.7752e-17,  6.8437e-17,  7.0466e-31,  2.7196e-31, -2.0268e-17,
         -3.0557e-17,  6.9564e-18, -2.8971e-17, -1.2805e-17,  1.5775e-16,
          5.3677e-17,  8.9575e-31,  3.5820e-31,  3.5525e-16, -4.2190e-16,
         -2.6774e-16, -5.3815e-16, -4.5207e-17],
        [-8.9111e-18,  3.5949e-17,  2.7196e-31,  3.5707e-31,  4.3021e-18,
         -1.4450e-17,  8.7953e-18, -3.0556e-17,  1.6753e-17,  1.8210e-16,
         -5.2250e-17,  9.0062e-31,  5.3400e-31,  1.3106e-16, -4.6254e-16,
         -2.6536e-16,  1.2935e-16,  4.9788e-18],
        [-4.4876e-03, -1.0507e-16, -2.0268e-17,  4.3021e-18,  1.6888e-03,
         -6.6376e-18,  1.3986e-18,  3.8055e-18, -4.7627e-03, -4.8232e-16,
          1.2498e-02,  1.2663e-17,  1.4987e-17, -5.3503e-16, -2.2757e-17,
          4.8352e-16,  3.8560e-02, -3.3090e-16],
        [ 2.2773e-16, -1.5192e-02, -3.0557e-17, -1.4450e-17, -6.6376e-18,
          1.9762e-02,  1.6945e-17,  2.4527e-17,  1.1483e-16, -4.5236e-02,
         -3.5267e-16, -2.8982e-18,  4.2041e-17, -4.5713e-02, -8.0265e-17,
          1.0546e-18, -2.5558e-16,  1.2249e-01],
        [ 6.1698e-18, -1.7095e-17,  6.9564e-18,  8.7953e-18,  1.3986e-18,
          1.6945e-17,  3.5428e-03,  4.3824e-11,  1.0206e-17, -2.4135e-17,
         -2.5997e-17, -9.4414e-18,  9.8201e-17, -6.4590e-17, -4.2669e-02,
          4.1347e-02, -4.9636e-16,  7.9868e-17],
        [-7.4570e-18, -2.0281e-17, -2.8971e-17, -3.0556e-17,  3.8055e-18,
          2.4527e-17,  4.3824e-11,  3.5428e-03, -1.7468e-17, -1.2992e-16,
          4.3756e-17, -1.0961e-16, -4.9473e-17, -4.8268e-17,  4.1347e-02,
          4.2669e-02, -4.2779e-16,  1.5287e-16],
        [ 2.0856e-02, -2.3740e-16, -1.2805e-17,  1.6753e-17, -4.7627e-03,
          1.1483e-16,  1.0206e-17, -1.7468e-17,  1.0739e-01,  3.0990e-16,
         -3.0878e-01, -1.4332e-16, -1.8655e-16, -6.0195e-16, -1.9554e-17,
         -1.0795e-16, -7.5289e-03, -1.6864e-16],
        [-6.6447e-17,  8.8960e-02,  1.5775e-16,  1.8210e-16, -4.8232e-16,
         -4.5236e-02, -2.4135e-17, -1.2992e-16,  3.0990e-16,  9.8962e-01,
          6.2003e-16, -4.3189e-16, -5.5535e-16, -1.5301e-02,  1.3215e-16,
         -6.0640e-17,  9.2815e-17,  8.7875e-03],
        [-5.7084e-02,  5.2239e-16,  5.3677e-17, -5.2250e-17,  1.2498e-02,
         -3.5267e-16, -2.5997e-17,  4.3756e-17, -3.0878e-01,  6.2003e-16,
          8.8879e-01,  4.0697e-16,  5.3094e-16, -4.3659e-16, -2.9932e-17,
         -6.8804e-17, -9.2814e-03, -2.3331e-17],
        [-4.3042e-17,  2.5002e-17,  8.9575e-31,  9.0062e-31,  1.2663e-17,
         -2.8982e-18, -9.4414e-18, -1.0961e-16, -1.4332e-16, -4.3189e-16,
          4.0697e-16,  4.0132e-30,  1.6971e-30,  4.3752e-16, -1.1655e-15,
         -1.4303e-15,  1.7323e-16, -1.6309e-17],
        [-5.2418e-17, -8.2051e-17,  3.5820e-31,  5.3400e-31,  1.4987e-17,
          4.2041e-17,  9.8201e-17, -4.9473e-17, -1.8655e-16, -5.5535e-16,
          5.3094e-16,  1.6971e-30,  4.1255e-30, -1.9731e-16, -1.7601e-15,
          5.5023e-16,  1.8679e-16,  4.7534e-17],
        [-4.8881e-16,  1.4194e-01,  3.5525e-16,  1.3106e-16, -5.3503e-16,
         -4.5713e-02, -6.4590e-17, -4.8268e-17, -6.0195e-16, -1.5301e-02,
         -4.3659e-16,  4.3752e-16, -1.9731e-16,  9.7688e-01,  1.6012e-16,
          1.7160e-17, -1.5571e-16,  1.0700e-02],
        [-1.3178e-17,  1.1475e-16, -4.2190e-16, -4.6254e-16, -2.2757e-17,
         -8.0265e-17, -4.2669e-02,  4.1347e-02, -1.9554e-17,  1.3215e-16,
         -2.9932e-17, -1.1655e-15, -1.7601e-15,  1.6012e-16,  9.9646e-01,
         -2.4166e-10,  1.4456e-17,  2.1470e-17],
        [-1.0353e-15,  1.5330e-17, -2.6774e-16, -2.6536e-16,  4.8352e-16,
          1.0546e-18,  4.1347e-02,  4.2669e-02, -1.0795e-16, -6.0640e-17,
         -6.8804e-17, -1.4303e-15,  5.5023e-16,  1.7160e-17, -2.4166e-10,
          9.9646e-01, -6.1813e-17,  5.6203e-17],
        [-9.3633e-02, -3.2950e-17, -5.3815e-16,  1.2935e-16,  3.8560e-02,
         -2.5558e-16, -4.9636e-16, -4.2779e-16, -7.5289e-03,  9.2815e-17,
         -9.2814e-03,  1.7323e-16,  1.8679e-16, -1.5571e-16,  1.4456e-17,
         -6.1813e-17,  9.8949e-01, -1.6997e-16],
        [ 1.2411e-15, -3.1999e-02, -4.5207e-17,  4.9788e-18, -3.3090e-16,
          1.2249e-01,  7.9868e-17,  1.5287e-16, -1.6864e-16,  8.7875e-03,
         -2.3331e-17, -1.6309e-17,  4.7534e-17,  1.0700e-02,  2.1470e-17,
          5.6203e-17, -1.6997e-16,  9.8351e-01]], dtype=torch.float64))
    qc = HF(mol).run(dm0=dm1)
    ene = qc.energy()
    assert is_orb_min(qc)
Beispiel #11
0
 def get_energy(atomposs, efield, grad_efield):
     efields = (efield, grad_efield)
     mol = Mol(moldesc=(atomzs, atomposs), basis="3-21G", dtype=dtype, efield=efields)
     qc = HF(mol).run()
     ene = qc.energy()
     return ene