qmctorch.wavefunction.jastrows.elec_elec_nuclei.jastrow_factor_electron_electron_nuclei module

class qmctorch.wavefunction.jastrows.elec_elec_nuclei.jastrow_factor_electron_electron_nuclei.JastrowFactorElectronElectronNuclei(*args: Any, **kwargs: Any)

Bases: Module

Jastrow Factor of the elec-elec-nuc term:

\[J = \exp\left( \sum_A \sum_{i<j} K(R_{iA}, r_{jA}, r_{rij}) \right)\]

Parameters:

• nup (int) – number of spin up electons

• ndow (int) – number of spin down electons

• cuda (bool, optional) – Turns GPU ON/OFF. Defaults to False.

get_mask_tri_up()

Get the mask to select the triangular up matrix

Returns:

mask of the tri up matrix

Return type:

torch.tensor

extract_tri_up(inp)

extract the upper triangular elements

Parameters:

input (torch.tensor) – input matrices (…, nelec, nelec)

Returns:

triangular up element (…, nelec_pair)

Return type:

torch.tensor

extract_elec_nuc_dist(en_dist)

Organize the elec nuc distances

Parameters:

en_dist (torch.tensor) – electron-nuclei distances nbatch x nelec x natom or nbatch x 3 x nelec x natom (dr)

Returns:

nbatch x natom x nelec_pair x 2 or torch.tensor: nbatch x 3 x natom x nelec_pair x 2 (dr)

Return type:

torch.tensor

assemble_dist(pos)

Assemle the different distances for easy calculations

Parameters:

pos (torch.tensor) – Positions of the electrons Size : Nbatch, Nelec x Ndim

Returns:

nbatch, natom, nelec_pair, 3

Return type:

torch.tensor

assemble_dist_deriv(pos, derivative=1)

Assemle the different distances for easy calculations

the output has dimension nbatch, 3 x natom, nelec_pair, 3 the last dimension is composed of [r_{e_1n}, r_{e_2n}, r_{ee}]

Parameters:

pos (torch.tensor) – Positions of the electrons Size : Nbatch, Nelec x Ndim

Returns:

nbatch, 3 x natom, nelec_pair, 3

Return type:

torch.tensor

forward(pos, derivative=0, sum_grad=True)

Compute the Jastrow factors.

Parameters:

• pos (torch.tensor) – Positions of the electrons Size : Nbatch, Nelec x Ndim

• derivative (int, optional) – order of the derivative (0,1,2,). Defaults to 0.

• sum_grad (bool, optional) – Return the sum_grad (i.e. the sum of the derivatives) or the individual terms. Defaults to True. False only for derivative=1

Returns:

value of the jastrow parameter for all confs

derivative = 0 (Nmo) x Nbatch x 1 derivative = 1 (Nmo) x Nbatch x Nelec (for sum_grad = True) derivative = 1 (Nmo) x Nbatch x Ndim x Nelec (for sum_grad = False) derivative = 2 (Nmo) x Nbatch x Nelec

Return type:

torch.tensor

jastrow_factor_derivative(r, dr, jast, sum_grad)

Compute the value of the derivative of the Jastrow factor

Parameters:

• r (torch.tensor) – ee distance matrix Nbatch x Nelec x Nelec

• jast (torch.tensor) – values of the jastrow elements Nbatch x Nelec x Nelec

Returns:

gradient of the jastrow factors

Nbatch x Nelec x Ndim

Return type:

torch.tensor

jastrow_factor_second_derivative(r, dr, d2r, jast)

Compute the value of the pure 2nd derivative of the Jastrow factor

Parameters:

• r (torch.tensor) – ee distance matrix Nbatch x Nelec x Nelec

• jast (torch.tensor) – values of the ajstrow elements Nbatch x Nelec x Nelec

Returns:

diagonal hessian of the jastrow factors

Nbatch x Nelec x Ndim

Return type:

torch.tensor

partial_derivative(djast)

[summary]

Parameters:

djast ([type]) – [description]

jastrow_factor_second_derivative_auto(pos, jast=None)

Compute the second derivative of the jastrow factor automatically. This is needed for complicate kernels where the partial derivatives of the kernels are difficult to organize in a total derivaitve e.e Boys-Handy

Parameters:

pos ([type]) – [description]