Ejemplo n.º 1
0
def test_rhf_func_gen():
    rhf_objective, molecule, parameters, _, _ = make_h6_1_3()
    ansatz, energy, _ = rhf_func_generator(rhf_objective)
    assert np.isclose(molecule.hf_energy, energy(parameters))

    ansatz, energy, _, opdm_func = rhf_func_generator(rhf_objective,
                                                      initial_occ_vec=[1] * 3 +
                                                      [0] * 3,
                                                      get_opdm_func=True)
    assert np.isclose(molecule.hf_energy, energy(parameters))
    test_opdm = opdm_func(parameters)
    u = ansatz(parameters)
    initial_opdm = np.diag([1] * 3 + [0] * 3)
    final_odpm = u @ initial_opdm @ u.T
    assert np.allclose(test_opdm, final_odpm)

    result = rhf_minimization(rhf_objective, initial_guess=parameters)
    assert np.allclose(result.x, parameters)
Ejemplo n.º 2
0
def test_energy_from_opdm():
    """Build test assuming sampling functions work"""

    rhf_objective, molecule, parameters, obi, tbi = make_h6_1_3()
    unitary, energy, _ = rhf_func_generator(rhf_objective)

    parameters = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
    initial_opdm = np.diag([1] * 3 + [0] * 3)
    final_opdm = unitary(parameters) @ initial_opdm @ unitary(
        parameters).conj().T
    test_energy = energy_from_opdm(final_opdm,
                                   constant=molecule.nuclear_repulsion,
                                   one_body_tensor=obi,
                                   two_body_tensor=tbi)
    true_energy = energy(parameters)
    assert np.allclose(test_energy, true_energy)
Ejemplo n.º 3
0
        # # now make a separate folder for each of 50 points along a line
        bond_distances = numpy.linspace(0.5, 2.5, 6)
        for bb in bond_distances:
            print(bb)
            local_dir = 'bond_distance_{:.1f}'.format(bb)
            os.mkdir(local_dir)
            os.chdir(local_dir)
            molecule = molecule_generator[n](bb)

            rhf_objective, S, HCore, TEI = make_rhf_objective(molecule)

            numpy.save("overlap.npy", S)
            numpy.save("h_core.npy", HCore)
            numpy.save("tei.npy", TEI)

            ansatz, energy, gradient = rhf_func_generator(rhf_objective)
            scipy_result = rhf_minimization(rhf_objective)
            print(molecule.hf_energy)
            print(scipy_result.fun)
            assert numpy.isclose(molecule.hf_energy, scipy_result.fun)

            numpy.save("parameters.npy", numpy.asarray(scipy_result.x))
            initial_opdm = numpy.diag([1] * rhf_objective.nocc +
                                      [0] * rhf_objective.nvirt)
            unitary = ansatz(scipy_result.x)
            final_opdm = unitary @ initial_opdm @ numpy.conjugate(unitary).T
            assert numpy.isclose(rhf_objective.energy_from_opdm(final_opdm),
                                 scipy_result.fun)
            numpy.save("true_opdm.npy", numpy.asarray(final_opdm))

            molecule.filename = os.path.join(os.getcwd(), molecule.name)