qmctorch.wavefunction.slater_jastrow_base module

class qmctorch.wavefunction.slater_jastrow_base.SlaterJastrowBase(*args: Any, **kwargs: Any)[source]

Bases: WaveFunction

Implementation of the QMC Network.

Parameters:

mol (Molecule) – a QMCTorch molecule object
configs (str, optional) – defines the CI configurations to be used. Defaults to ‘ground_state’. - ground_state : only the ground state determinant in the wave function - single(n,m) : only single excitation with n electrons and m orbitals - single_double(n,m) : single and double excitation with n electrons and m orbitals - cas(n, m) : all possible configuration using n eletrons and m orbitals
kinetic (str, optional) – method to compute the kinetic energy. Defaults to ‘jacobi’. - jacobi : use the Jacobi formula to compute the kinetic energy - auto : use automatic differentiation to compute the kinetic energy
cuda (bool, optional) – turns GPU ON/OFF Defaults to False.
include_all_mo (bool, optional) – include either all molecular orbitals or only the ones that are popualted in the configs. Defaults to False

log_data()[source]: Print information abut the wave function.

get_mo_coeffs()[source]

update_mo_coeffs()[source]

geometry(pos)[source]

Returns the gemoetry of the system in xyz format

Parameters:: pos (torch.tensor) – sampling points (Nbatch, 3*Nelec)
Returns:: list where each element is one line of the xyz file
Return type:: list

gto2sto(plot=False)[source]: Fits the AO GTO to AO STO. The SZ sto that have only one basis function per ao

forward(x, ao=None)[source]

computes the value of the wave function for the sampling points

\[\Psi(R) = \sum_{n} c_n D^{u}_n(r^u) \times D^{d}_n(r^d)\]

Parameters:

x (torch.tensor) – sampling points (Nbatch, 3*Nelec)
ao (torch.tensor, optional) – values of the atomic orbitals (Nbatch, Nelec, Nao)

Returns:

values of the wave functions at each sampling point (Nbatch, 1)

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(pos)

ao2mo(ao)[source]: Get the values of the MO from the values of AO.

pos2mo(x, derivative=0)[source]

Get the values of MOs from the positions

Parameters:

[nbatch (x {torch.tensor} -- positions of the electrons) –
nelec*ndim] –

Keyword Arguments:

(default (derivative {int} -- order of the derivative) – {0})

Returns:

torch.tensor – MO matrix [nbatch, nelec, nmo]

kinetic_energy_jacobi(x, **kwargs)[source]

Compute the value of the kinetic enery using the Jacobi Formula. C. Filippi, Simple Formalism for Efficient Derivatives .

\[\frac{K(R)}{\Psi(R)} = Tr(A^{-1} B_{kin})\]

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

gradients_jacobi(x, pdf=False)[source]

Compute the gradients of the wave function (or density) using the Jacobi Formula C. Filippi, Simple Formalism for Efficient Derivatives.

\[\frac{K(R)}{\Psi(R)} = Tr(A^{-1} B_{grad})\]

Parameters:

x (torch.tensor) – sampling points (Nbatch, 3*Nelec)
pdf (bool, optional) – if true compute the grads of the density

Returns:

values of the gradients wrt the walker pos at each sampling points

Return type:

torch.tensor

get_gradient_operator(x, ao, grad_ao, mo)[source]

Compute the gradient operator

Parameters:

x ([type]) – [description]
ao ([type]) – [description]
dao ([type]) – [description]

get_hessian_operator(x, ao, dao, d2ao, mo)[source]

Compute the Bkin matrix

Parameters:

x (torch.tensor) – sampling points (Nbatch, 3*Nelec)
mo (torch.tensor, optional) – precomputed values of the MOs

Returns:

matrix of the kinetic operator

Return type:

torch.tensor