qmctorch.wavefunction.wf_base module

class qmctorch.wavefunction.wf_base.WaveFunction(nelec, ndim, kinetic='auto', cuda=False)

Bases: sphinx.ext.autodoc.importer._MockObject

forward(x)

Compute the value of the wave function. for a multiple conformation of the electrons

Parameters:

• parameters – variational param of the wf
• pos – position of the electrons

Returns: values of psi

electronic_potential(pos)

Computes the electron-electron term

Parameters:x (torch.tensor) – sampling points (Nbatch, 3*Nelec) Returns:values of the electon-electron energy at each sampling points Return type:torch.tensor

nuclear_potential(pos)

Computes the electron-nuclear term

Parameters:x (torch.tensor) – sampling points (Nbatch, 3*Nelec) Returns:values of the electon-nuclear energy at each sampling points Return type:torch.tensor

nuclear_repulsion()

Computes the nuclear-nuclear repulsion term

Returns:values of the nuclear-nuclear energy at each sampling points Return type:torch.tensor

gradients_autograd(pos, pdf=False)

Computes the gradients of the wavefunction (or density) w.r.t the values of the pos.

Parameters:

• pos (torch.tensor) – positions of the walkers
• pdf (bool, optional) – if true compute the grads of the density

Returns:

values of the gradients

Return type:

torch.tensor

kinetic_energy_autograd(pos)

Compute the kinetic energy through the 2nd derivative w.r.t the value of the pos.

Parameters:pos (torch.tensor) – positions of the walkers Returns:values of nabla^2 * Psi

local_energy(pos)

Computes the local energy

\[E = K(R) + V_{ee}(R) + V_{en}(R) + V_{nn}\]

Parameters:pos (torch.tensor) – sampling points (Nbatch, 3*Nelec) Returns:values of the local enrgies at each sampling points Return type:[torch.tensor]

Examples::

>>> mol = Molecule('h2.xyz', calculator='adf', basis = 'dzp')
>>> wf = SlaterJastrow(mol, configs='cas(2,2)')
>>> pos = torch.rand(500,6)
>>> vals = wf.local_energy(pos)

Note

by default kinetic_energy refers to kinetic_energy_autograd users can overwrite it to poit to any other methods see kinetic_energy_jacobi in wf_orbital

energy(pos)

Total energy for the sampling points.

variance(pos)

Variance of the energy at the sampling points.

sampling_error(eloc)

Compute the statistical uncertainty. Assuming the samples are uncorrelated.

pdf(pos, return_grad=False)

density of the wave function.

get_number_parameters()

Computes the total number of parameters.

load(filename, group='wf_opt', model='best')

Load trained parameters

Parameters:

• filename (str) – hdf5 filename
• group (str, optional) – group in the hdf5 file where the model is stored. Defaults to ‘wf_opt’.
• model (str, optional) – ‘best’ or ‘ last’. Defaults to ‘best’.