コード例 #1
0
ファイル: test_molecule.py プロジェクト: ScorpJD/QMCTorch
    def test1_create(self):

        # molecule
        mol = Molecule(atom='H 0. 0. 0.; H 0. 0. 1.',
                       unit='bohr',
                       calculator='pyscf',
                       basis='sto-3g',
                       redo_scf=True)

        mol.print_total_energy()
コード例 #2
0
ファイル: test_h2.py プロジェクト: ScorpJD/QMCTorch
    def setUp(self):

        torch.manual_seed(0)
        np.random.seed(0)

        # optimal parameters
        self.opt_r = 0.69  # the two h are at +0.69 and -0.69
        self.opt_sigma = 1.24

        # molecule
        self.mol = Molecule(atom='H 0 0 -0.69; H 0 0 0.69',
                            unit='bohr',
                            calculator='pyscf',
                            basis='sto-3g')

        # wave function
        self.wf = Orbital(self.mol,
                          kinetic='auto',
                          configs='single(2,2)',
                          use_jastrow=True)

        # sampler
        self.sampler = Metropolis(nwalkers=1000,
                                  nstep=2000,
                                  step_size=0.5,
                                  ndim=self.wf.ndim,
                                  nelec=self.wf.nelec,
                                  init=self.mol.domain('normal'),
                                  move={
                                      'type': 'all-elec',
                                      'proba': 'normal'
                                  })

        self.hmc_sampler = Hamiltonian(nwalkers=100,
                                       nstep=200,
                                       step_size=0.1,
                                       ndim=self.wf.ndim,
                                       nelec=self.wf.nelec,
                                       init=self.mol.domain('normal'))

        # optimizer
        self.opt = optim.Adam(self.wf.parameters(), lr=0.01)

        # solver
        self.solver = SolverOrbital(wf=self.wf,
                                    sampler=self.sampler,
                                    optimizer=self.opt)

        # ground state energy
        self.ground_state_energy = -1.16

        # ground state pos
        self.ground_state_pos = 0.69
コード例 #3
0
    def setUp(self):

        torch.manual_seed(0)
        np.random.seed(0)
        set_torch_double_precision()

        # molecule
        self.mol = Molecule(atom='Li 0 0 0; H 0 0 3.015',
                            unit='bohr',
                            calculator='pyscf',
                            basis='sto-3g')

        # wave function
        self.wf = SlaterJastrowBackFlow(self.mol,
                                        kinetic='jacobi',
                                        configs='single_double(2,2)',
                                        backflow_kernel=BackFlowKernelPowerSum,
                                        orbital_dependent_backflow=False,
                                        include_all_mo=True)

        # fc weights
        self.wf.fc.weight.data = torch.rand(self.wf.fc.weight.shape)

        # jastrow weights
        self.wf.jastrow.jastrow_kernel.weight.data = torch.rand(
            self.wf.jastrow.jastrow_kernel.weight.shape)

        # sampler
        self.sampler = Metropolis(nwalkers=500,
                                  nstep=200,
                                  step_size=0.05,
                                  ndim=self.wf.ndim,
                                  nelec=self.wf.nelec,
                                  init=self.mol.domain('normal'),
                                  move={
                                      'type': 'all-elec',
                                      'proba': 'normal'
                                  })

        # optimizer
        self.opt = optim.Adam(self.wf.parameters(), lr=0.01)

        # solver
        self.solver = SolverSlaterJastrow(wf=self.wf,
                                          sampler=self.sampler,
                                          optimizer=self.opt)

        # artificial pos
        self.nbatch = 10
        self.pos = torch.as_tensor(
            np.random.rand(self.nbatch, self.wf.nelec * 3))
        self.pos.requires_grad = True
コード例 #4
0
    def setUp(self):

        torch.manual_seed(0)
        np.random.seed(0)
        set_torch_double_precision()

        # optimal parameters
        self.opt_r = 0.69  # the two h are at +0.69 and -0.69
        self.opt_sigma = 1.24

        # molecule
        self.mol = Molecule(atom='H 0 0 -0.69; H 0 0 0.69',
                            unit='bohr',
                            calculator='pyscf',
                            basis='sto-3g')

        # wave function
        self.wf = CorrelatedOrbital(
            self.mol,
            kinetic='auto',
            configs='cas(2,2)',
            jastrow_type=FullyConnectedJastrow,
            # jastrow_type='pade_jastrow',
            include_all_mo=True)

        # sampler
        self.sampler = Metropolis(nwalkers=1000,
                                  nstep=2000,
                                  step_size=0.5,
                                  ndim=self.wf.ndim,
                                  nelec=self.wf.nelec,
                                  init=self.mol.domain('normal'),
                                  move={
                                      'type': 'all-elec',
                                      'proba': 'normal'
                                  })

        # optimizer
        self.opt = optim.Adam(self.wf.parameters(), lr=0.01)

        # solver
        self.solver = SolverOrbital(wf=self.wf,
                                    sampler=self.sampler,
                                    optimizer=self.opt)

        # ground state energy
        self.ground_state_energy = -1.16

        # ground state pos
        self.ground_state_pos = 0.69
コード例 #5
0
ファイル: test_h2_hvd.py プロジェクト: NLESC-JCER/QMCTorch
    def setUp(self):
        hvd.init()

        torch.manual_seed(0)
        np.random.seed(0)

        set_torch_double_precision()

        # optimal parameters
        self.opt_r = 0.69  # the two h are at +0.69 and -0.69
        self.opt_sigma = 1.24

        # molecule
        self.mol = Molecule(
            atom='H 0 0 -0.69; H 0 0 0.69',
            unit='bohr',
            calculator='pyscf',
            basis='sto-3g',
            rank=hvd.local_rank())

        # wave function
        self.wf = SlaterJastrow(self.mol, kinetic='jacobi',
                                configs='cas(2,2)',
                                cuda=False)

        # sampler
        self.sampler = Metropolis(
            nwalkers=200,
            nstep=200,
            step_size=0.2,
            ndim=self.wf.ndim,
            nelec=self.wf.nelec,
            init=self.mol.domain('atomic'),
            move={
                'type': 'all-elec',
                'proba': 'normal'})

        # optimizer
        self.opt = optim.Adam(self.wf.parameters(), lr=0.01)

        # solver
        self.solver = SolverSlaterJastrowHorovod(wf=self.wf, sampler=self.sampler,
                                                 optimizer=self.opt, rank=hvd.rank())

        # ground state energy
        self.ground_state_energy = -1.16

        # ground state pos
        self.ground_state_pos = 0.69
コード例 #6
0
ファイル: test_sampler.py プロジェクト: ScorpJD/QMCTorch
    def setUp(self):

        torch.manual_seed(101)
        np.random.seed(101)

        set_torch_double_precision()

        # molecule
        self.mol = Molecule(atom='H 0 0 -0.69; H 0 0 0.69',
                            unit='bohr',
                            calculator='pyscf',
                            basis='sto-3g')

        # orbital
        self.wf = Orbital(self.mol)
コード例 #7
0
    def setUp(self):

        torch.manual_seed(0)
        np.random.seed(0)
        set_torch_double_precision()

        # molecule
        path_hdf5 = (PATH_TEST / 'hdf5/LiH_adf_dz.hdf5').absolute().as_posix()
        self.mol = Molecule(load=path_hdf5)

        # wave function
        self.wf = SlaterOrbitalDependentJastrow(self.mol,
                                                kinetic='jacobi',
                                                configs='cas(2,2)',
                                                include_all_mo=True)

        # fc weights
        self.wf.fc.weight.data = torch.rand(self.wf.fc.weight.shape)

        # jastrow weights
        for ker in self.wf.jastrow.jastrow_kernel.jastrow_functions:
            ker.weight.data = torch.rand(1)

        # sampler
        self.sampler = Metropolis(nwalkers=500,
                                  nstep=200,
                                  step_size=0.05,
                                  ndim=self.wf.ndim,
                                  nelec=self.wf.nelec,
                                  init=self.mol.domain('normal'),
                                  move={
                                      'type': 'all-elec',
                                      'proba': 'normal'
                                  })

        # optimizer
        self.opt = optim.Adam(self.wf.parameters(), lr=0.01)

        # solver
        self.solver = SolverSlaterJastrow(wf=self.wf,
                                          sampler=self.sampler,
                                          optimizer=self.opt)

        # artificial pos
        self.nbatch = 10
        self.pos = torch.as_tensor(
            np.random.rand(self.nbatch, self.wf.nelec * 3))
        self.pos.requires_grad = True
コード例 #8
0
    def setUp(self):

        torch.manual_seed(101)
        np.random.seed(101)

        set_torch_double_precision()

        # molecule
        mol = Molecule(
            atom='Li 0 0 0; H 0 0 3.14',
            unit='bohr',
            calculator='pyscf',
            basis='sto-3g',
            redo_scf=True)

        self.wf = SlaterCombinedJastrow(mol,
                                        kinetic='auto',
                                        include_all_mo=False,
                                        configs='single_double(2,2)',
                                        jastrow_kernel={
                                            'ee': PadeJastrowKernelElecElec,
                                            'en': PadeJastrowKernelElecNuc,
                                            'een': BoysHandyJastrowKernel},
                                        jastrow_kernel_kwargs={
                                            'ee': {'w': 1.},
                                            'en': {'w': 1.},
                                            'een': {}})

        self.random_fc_weight = torch.rand(self.wf.fc.weight.shape)
        self.wf.fc.weight.data = self.random_fc_weight
        self.nbatch = 11
        self.pos = torch.Tensor(np.random.rand(
            self.nbatch,  self.wf.nelec*3))
        self.pos.requires_grad = True
コード例 #9
0
    def setUp(self):

        torch.manual_seed(101)
        np.random.seed(101)

        set_torch_double_precision()

        # molecule
        mol = Molecule(atom='Li 0 0 0; H 0 0 3.015',
                       unit='bohr',
                       calculator='pyscf',
                       basis='sto-3g',
                       redo_scf=True)

        self.wf = CorrelatedOrbital(mol,
                                    kinetic='auto',
                                    jastrow_type='pade_jastrow',
                                    configs='single_double(2,4)',
                                    include_all_mo=True)

        self.random_fc_weight = torch.rand(self.wf.fc.weight.shape)
        self.wf.fc.weight.data = self.random_fc_weight

        self.wf.jastrow.weight.data = torch.rand(self.wf.jastrow.weight.shape)

        self.nbatch = 3
        self.pos = torch.tensor(np.random.rand(self.nbatch, self.wf.nelec * 3))

        self.pos.requires_grad = True
コード例 #10
0
ファイル: test_h2_stats.py プロジェクト: NLESC-JCER/QMCTorch
class TestH2Stat(unittest.TestCase):
    def setUp(self):

        torch.manual_seed(0)
        np.random.seed(0)

        # optimal parameters
        self.opt_r = 0.69  # the two h are at +0.69 and -0.69
        self.opt_sigma = 1.24

        # molecule
        self.mol = Molecule(atom='H 0 0 -0.69; H 0 0 0.69',
                            unit='bohr',
                            calculator='pyscf',
                            basis='sto-3g')

        # wave function
        self.wf = SlaterJastrow(self.mol,
                                kinetic='jacobi',
                                configs='single(2,2)')

        # sampler
        self.sampler = Metropolis(nwalkers=100,
                                  nstep=500,
                                  step_size=0.5,
                                  ndim=self.wf.ndim,
                                  nelec=self.wf.nelec,
                                  ntherm=0,
                                  ndecor=1,
                                  init=self.mol.domain('normal'),
                                  move={
                                      'type': 'all-elec',
                                      'proba': 'normal'
                                  })

        # optimizer
        self.opt = optim.Adam(self.wf.parameters(), lr=0.01)

        # solver
        self.solver = SolverSlaterJastrow(wf=self.wf,
                                          sampler=self.sampler,
                                          optimizer=self.opt)

    def test_sampling_traj(self):

        pos = self.solver.sampler(self.solver.wf.pdf)
        obs = self.solver.sampling_traj(pos)

        plot_walkers_traj(obs.local_energy)
        plot_block(obs.local_energy)

    def test_stat(self):

        pos = self.solver.sampler(self.solver.wf.pdf)
        obs = self.solver.sampling_traj(pos)

        if __PLOT__:
            plot_blocking_energy(obs.local_energy, block_size=10)
            plot_correlation_coefficient(obs.local_energy)
            plot_integrated_autocorrelation_time(obs.local_energy)
コード例 #11
0
    def setUp(self):

        torch.manual_seed(101)
        np.random.seed(101)

        set_torch_double_precision()

        # molecule
        mol = Molecule(atom='Li 0 0 0; H 0 0 3.015',
                       unit='bohr',
                       calculator='pyscf',
                       basis='sto-3g',
                       redo_scf=True)

        self.wf = SlaterOrbitalDependentJastrow(
            mol,
            kinetic='auto',
            jastrow_kernel=FullyConnectedJastrowKernel,
            configs='single_double(2,4)',
            include_all_mo=True)

        self.random_fc_weight = torch.rand(self.wf.fc.weight.shape)
        self.wf.fc.weight.data = self.random_fc_weight

        self.nbatch = 3
        self.pos = torch.as_tensor(
            np.random.rand(self.nbatch, self.wf.nelec * 3))

        self.pos.requires_grad = True
コード例 #12
0
    def setUp(self):

        # define the molecule
        at = 'C 0 0 0'
        basis = 'dzp'
        self.mol = Molecule(atom=at,
                            calculator='pyscf',
                            basis=basis,
                            unit='bohr')

        self.m = gto.M(atom=at, basis=basis, unit='bohr')

        # define the wave function
        self.wf = SlaterJastrow(self.mol)

        self.pos = torch.zeros(100, self.mol.nelec * 3)

        self.pos[:, 0] = torch.linspace(-5, 5, 100)
        self.pos[:, 1] = torch.linspace(-5, 5, 100)
        self.pos[:, 2] = torch.linspace(-5, 5, 100)

        self.pos = Variable(self.pos)
        self.pos.requires_grad = True

        self.x = self.pos[:, 0].detach().numpy()
コード例 #13
0
    def setUp(self):

        torch.manual_seed(101)
        np.random.seed(101)

        set_torch_double_precision()

        # molecule
        mol = Molecule(
            atom='Li 0 0 0; H 0 0 1.',
            unit='bohr',
            calculator='pyscf',
            basis='sto-3g',
            redo_scf=True)

        self.wf = SlaterJastrow(mol,
                                kinetic='auto',
                                configs='ground_state',
                                jastrow_kernel=FullyConnectedJastrowKernel)

        self.random_fc_weight = torch.rand(self.wf.fc.weight.shape)
        self.wf.fc.weight.data = self.random_fc_weight

        self.nbatch = 10
        self.pos = 1E-2 * torch.as_tensor(np.random.rand(
            self.nbatch, self.wf.nelec*3))
        self.pos.requires_grad = True
コード例 #14
0
    def setUp(self):

        torch.manual_seed(101)
        np.random.seed(101)

        set_torch_double_precision()

        # molecule
        mol = Molecule(
            atom='Li 0 0 0; H 0 0 1.',
            unit='bohr',
            calculator='pyscf',
            basis='sto-3g',
            redo_scf=True)

        self.wf = SlaterJastrow(mol,
                                kinetic='auto',
                                include_all_mo=True,
                                configs='cas(2,2)')

        self.random_fc_weight = torch.rand(self.wf.fc.weight.shape)
        self.wf.fc.weight.data = self.random_fc_weight

        self.nbatch = 10
        self.pos = torch.Tensor(
            np.random.rand(self.nbatch, mol.nelec*3))
        self.pos.requires_grad = True
コード例 #15
0
    def setUp(self):

        torch.manual_seed(101)
        np.random.seed(101)

        set_torch_double_precision()

        # molecule
        mol = Molecule(atom='Li 0 0 0; H 0 0 3.015',
                       unit='bohr',
                       calculator='pyscf',
                       basis='sto-3g',
                       redo_scf=True)

        self.wf = SlaterJastrowBackFlow(mol,
                                        kinetic='jacobi',
                                        jastrow_kernel=PadeJastrowKernel,
                                        include_all_mo=True,
                                        configs='single_double(2,2)',
                                        backflow_kernel=BackFlowKernelInverse,
                                        orbital_dependent_backflow=False)

        self.random_fc_weight = torch.rand(self.wf.fc.weight.shape)
        self.wf.fc.weight.data = self.random_fc_weight

        self.nbatch = 5
        self.pos = torch.Tensor(np.random.rand(self.nbatch, self.wf.nelec * 3))
        self.pos.requires_grad = True
コード例 #16
0
ファイル: test_h2_stats.py プロジェクト: NLESC-JCER/QMCTorch
    def setUp(self):

        torch.manual_seed(0)
        np.random.seed(0)

        # optimal parameters
        self.opt_r = 0.69  # the two h are at +0.69 and -0.69
        self.opt_sigma = 1.24

        # molecule
        self.mol = Molecule(atom='H 0 0 -0.69; H 0 0 0.69',
                            unit='bohr',
                            calculator='pyscf',
                            basis='sto-3g')

        # wave function
        self.wf = SlaterJastrow(self.mol,
                                kinetic='jacobi',
                                configs='single(2,2)')

        # sampler
        self.sampler = Metropolis(nwalkers=100,
                                  nstep=500,
                                  step_size=0.5,
                                  ndim=self.wf.ndim,
                                  nelec=self.wf.nelec,
                                  ntherm=0,
                                  ndecor=1,
                                  init=self.mol.domain('normal'),
                                  move={
                                      'type': 'all-elec',
                                      'proba': 'normal'
                                  })

        # optimizer
        self.opt = optim.Adam(self.wf.parameters(), lr=0.01)

        # solver
        self.solver = SolverSlaterJastrow(wf=self.wf,
                                          sampler=self.sampler,
                                          optimizer=self.opt)
コード例 #17
0
    def setUp(self):

        torch.manual_seed(0)

        # molecule
        path_hdf5 = (PATH_TEST / 'hdf5/H2_adf_dzp.hdf5').absolute().as_posix()
        self.mol = Molecule(load=path_hdf5)

        # wave function
        self.wf = Orbital(self.mol,
                          kinetic='auto',
                          configs='single(2,2)',
                          use_jastrow=True)

        # sampler
        self.sampler = Metropolis(nwalkers=1000,
                                  nstep=2000,
                                  step_size=0.5,
                                  ndim=self.wf.ndim,
                                  nelec=self.wf.nelec,
                                  init=self.mol.domain('normal'),
                                  move={
                                      'type': 'all-elec',
                                      'proba': 'normal'
                                  })

        # optimizer
        self.opt = optim.Adam(self.wf.parameters(), lr=0.01)

        # solver
        self.solver = SolverOrbital(wf=self.wf,
                                    sampler=self.sampler,
                                    optimizer=self.opt)

        # ground state energy
        self.ground_state_energy = -1.16

        # ground state pos
        self.ground_state_pos = 0.69
コード例 #18
0
    def setUp(self):

        atom_str = 'O 0 0 -0.69; C 0 0 0.69'
        self.m = gto.M(atom=atom_str, basis='sto-3g', unit='bohr')
        self.mol = Molecule(atom=atom_str,
                            calculator='pyscf',
                            basis='sto-3g',
                            unit='bohr')

        # define the wave function
        self.wf = OrbitalTest(self.mol)
        self.x = 2 * torch.rand(5, 3 * self.mol.nelec) - 1.
        self.x.requires_grad = True
コード例 #19
0
    def setUp(self):

        torch.manual_seed(0)
        np.random.seed(0)

        path_hdf5 = (
            PATH_TEST / 'hdf5/CO2_adf_dzp.hdf5').absolute().as_posix()
        self.mol = Molecule(load=path_hdf5)

        # wave function
        self.wf = SlaterJastrow(self.mol, kinetic='jacobi',
                                configs='ground_state',
                                include_all_mo=False)
コード例 #20
0
    def setUp(self):

        # define the molecule
        path_hdf5 = PATH_TEST / 'hdf5/C_adf_dzp.hdf5'
        self.mol = Molecule(load=path_hdf5)

        # define the wave function
        self.wf = Orbital(self.mol, include_all_mo=True)

        # define the grid points
        npts = 11
        self.pos = torch.rand(npts, self.mol.nelec * 3)
        self.pos = Variable(self.pos)
        self.pos.requires_grad = True
コード例 #21
0
    def setUp(self):

        torch.manual_seed(101)
        np.random.seed(101)

        set_torch_double_precision()

        # molecule
        mol = Molecule(atom='H 0 0 -0.69; H 0 0 0.69',
                       unit='bohr',
                       calculator='pyscf',
                       basis='sto-3g')

        self.orb_conf = OrbitalConfigurations(mol)
コード例 #22
0
    def setUp(self):

        torch.manual_seed(0)
        np.random.seed(0)

        # molecule
        self.mol = Molecule(atom='Li 0 0 0; H 0 0 3.015',
                            unit='bohr',
                            calculator='pyscf',
                            basis='sto-3g')

        # wave function
        self.wf = Orbital(self.mol,
                          kinetic='jacobi',
                          configs='single(2,2)',
                          use_jastrow=True,
                          include_all_mo=False)

        # sampler
        self.sampler = Metropolis(nwalkers=500,
                                  nstep=200,
                                  step_size=0.05,
                                  ndim=self.wf.ndim,
                                  nelec=self.wf.nelec,
                                  init=self.mol.domain('normal'),
                                  move={
                                      'type': 'all-elec',
                                      'proba': 'normal'
                                  })

        # optimizer
        self.opt = optim.Adam(self.wf.parameters(), lr=0.01)

        # solver
        self.solver = SolverOrbital(wf=self.wf,
                                    sampler=self.sampler,
                                    optimizer=self.opt)
コード例 #23
0
    def setUp(self):

        torch.manual_seed(0)
        np.random.seed(0)

        self.mol = Molecule(atom='C 0 0 0; O 0 0 2.190; O 0 0 -2.190',
                            calculator='pyscf',
                            basis='dzp',
                            unit='bohr')

        # wave function
        self.wf = SlaterJastrow(self.mol,
                                kinetic='jacobi',
                                configs='ground_state',
                                include_all_mo=False)
コード例 #24
0
    def setUp(self):

        # molecule
        self.mol = Molecule(
            atom='H 0 0 -0.69; H 0 0 0.69',
            unit='bohr',
            calculator='pyscf',
            basis='dzp')

        # wave function
        self.wf = SlaterJastrow(self.mol, kinetic='jacobi',
                                configs='single(2,2)')

        npts = 51
        self.pos = torch.zeros(npts, 6)
        self.pos[:, 2] = torch.linspace(-2, 2, npts)
コード例 #25
0
    def setUp(self):

        # define the molecule
        path_hdf5 = (PATH_TEST / 'hdf5/C_adf_dzp.hdf5').absolute().as_posix()
        self.mol = Molecule(load=path_hdf5)

        # define the wave function
        self.wf = Orbital(self.mol, include_all_mo=True)

        # define the grid points
        self.npts = 21
        pts = get_pts(self.npts)

        self.pos = torch.zeros(self.npts**2, self.mol.nelec * 3)
        self.pos[:, :3] = pts
        self.pos = Variable(self.pos)
        self.pos.requires_grad = True
コード例 #26
0
    def setUp(self):

        # define the molecule
        at = 'C 0 0 0'
        basis = 'dzp'
        self.mol = Molecule(atom=at,
                            calculator='pyscf',
                            basis=basis,
                            unit='bohr')

        # define the wave function
        self.ao = AtomicOrbitalsBackFlow(self.mol, BackFlowKernelInverse)

        # define the grid points
        self.npts = 11
        self.pos = torch.rand(self.npts, self.mol.nelec * 3)
        self.pos = Variable(self.pos)
        self.pos.requires_grad = True
コード例 #27
0
ファイル: test_gto2sto_fit.py プロジェクト: ScorpJD/QMCTorch
    def setUp(self):

        torch.manual_seed(101)
        np.random.seed(101)

        set_torch_double_precision()

        # molecule
        mol = Molecule(atom='C 0 0 0',
                       unit='bohr',
                       calculator='pyscf',
                       basis='sto-3g',
                       redo_scf=True)

        self.wf = Orbital(mol, kinetic='auto',
                          configs='ground_state').gto2sto()

        self.pos = -0.25 + 0.5 * torch.tensor(np.random.rand(10, 18))
        self.pos.requires_grad = True
コード例 #28
0
    def setUp(self):

        # define the molecule
        at = 'C 0 0 0'
        basis = 'dzp'
        self.mol = Molecule(atom=at,
                            calculator='pyscf',
                            basis=basis,
                            unit='bohr')

        # define the kernel
        self.kernel = BackFlowKernelInverse(self.mol)
        self.edist = ElectronElectronDistance(self.mol.nelec)

        # define the grid points
        self.npts = 11
        self.pos = torch.rand(self.npts, self.mol.nelec * 3)
        self.pos = Variable(self.pos)
        self.pos.requires_grad = True
コード例 #29
0
    def setUp(self):

        # define the molecule
        at = 'C 0 0 0'
        basis = 'dzp'
        self.mol = Molecule(atom=at,
                            calculator='pyscf',
                            basis=basis,
                            unit='bohr')

        # define the backflow transformation
        self.backflow_trans = BackFlowTransformation(
            self.mol, BackFlowKernelInverse)

        # define the grid points
        self.npts = 11
        self.pos = torch.rand(self.npts, self.mol.nelec * 3)
        self.pos = Variable(self.pos)
        self.pos.requires_grad = True
コード例 #30
0
    def setUp(self):

        # define the molecule
        at = 'C 0 0 0'
        basis = 'dzp'
        self.mol = Molecule(atom=at,
                            calculator='pyscf',
                            basis=basis,
                            unit='bohr')

        self.m = gto.M(atom=at, basis=basis, unit='bohr')

        # define the wave function
        self.wf = Orbital(self.mol, include_all_mo=True)

        # define the grid points
        npts = 11
        self.pos = torch.rand(npts, self.mol.nelec * 3)
        self.pos = Variable(self.pos)
        self.pos.requires_grad = True