qmctorch.wavefunction.jastrows.elec_elec.jastrow_factor_electron_electron module¶
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class
qmctorch.wavefunction.jastrows.elec_elec.jastrow_factor_electron_electron.JastrowFactorElectronElectron(nup, ndown, jastrow_kernel, kernel_kwargs={}, orbital_dependent_kernel=False, number_of_orbitals=None, scale=False, scale_factor=0.6, cuda=False)[source]¶ Bases:
sphinx.ext.autodoc.importer._MockObjectElectron-Electron Jastrow factor.
\[J = \prod_{i<j} \exp(\text{Kernel}(r_{ij}))\]Parameters: - nup (int) – number of spin up electons
- ndow (int) – number of spin down electons
- jastrow_kernel (kernel) – class of a electron-electron Jastrow kernel
- kernel_kwargs (dict, optional) – keyword argument of the kernel. Defaults to {}.
- orbital_dependent_kernel (bool, optional) – Make the kernel orbital dependent. Defaults to False.
- number_of_orbitals (int, optional) – number of orbitals for orbital dependent kernels. Defaults to None.
- scale (bool, optional) – use scaled electron-electron distance. Defaults to False.
- scale_factor (float, optional) – scaling factor. Defaults to 0.6.
- cuda (bool, optional) – use cuda. Defaults to False.
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get_mask_tri_up()[source]¶ Get the mask to select the triangular up matrix
Returns: mask of the tri up matrix Return type: torch.tensor
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extract_tri_up(inp)[source]¶ extract the upper triangular elements
Parameters: inp (torch.tensor) – input matrices (nbatch, n, n) Returns: triangular up element (nbatch, -1) Return type: torch.tensor
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get_edist_unique(pos, derivative=0)[source]¶ Get the unique elements of the electron-electron distance matrix.
Parameters: - pos (torch.tensor) – positions of the electrons (Nbatch, Nelec*3)
- derivative (int, optional) – order of the derivative
Returns: unique values of the electron-electron distance matrix
Return type: torch.tensor
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forward(pos, derivative=0, sum_grad=True)[source]¶ 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
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jastrow_factor_derivative(r, dr, jast, sum_grad)[source]¶ Compute the value of the derivative of the Jastrow factor
Parameters: - r (torch.tensor) – distance matrix Nbatch x Nelec x Nelec
- dr (torch.tensor) – derivative of the distance matrix Nbatch x Nelec x Nelec x 3
- jast (torch.tensor) – values of the jastrow elements Nbatch x Nelec x Nelec
- sum_grad (bool) – return the sum of the gradient or the individual components
Returns: - gradient of the jastrow factors
Nbatch x Nelec x Ndim
Return type: torch.tensor
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jastrow_factor_second_derivative(r, dr, d2r, jast)[source]¶ Compute the value of the pure 2nd derivative of the Jastrow factor
Parameters: - r (torch.tensor) – distance matrix Nbatch x Nelec x Nelec
- dr (torch.tensor) – derivative of the distance matrix Nbatch x Nelec x Nelec x 3
- d2r (torch.tensor) – 2nd derivative of the distance matrix Nbatch x Nelec x Nelec x 3
- 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