descriptors

The ciderpress.pyscf.descriptors module provides the function get_descriptors(), which computes a set of requested CIDER features from a completed PySCF calculation. This is the utility used to generate the features for training CIDER models

This module is for fast evaluation of features and their derivatives with respect to orbital occupations and rotations. Only for b and d features

ciderpress.pyscf.descriptors.get_descriptors(analyzer, settings, orbs=None, **kwargs)

Get the descriptors for version b or d (l=0 only). Analyzer must contain mo_occ, mo_energy, and mo_coeff attributes. Can get feature derivatives with respect to orbital occupation using orbs. Spin polarization is determined automatically from the analyzer class type.

orbs format: orbs is a dictionary of lists or (for a spin-polarized system) a 2-tuple of dictionary of lists. Each key is either ‘B’, ‘O’, or ‘U’, with the value being a list of orbital indexes with respect to which to compute occupation derivatives of the features. ‘B’ indexes up from the bottom, i.e. the lowest-energy core states. ‘O’ indexes down from the highest occupied molecular orbital. ‘U’ indexes up from the lowest unoccupied molecular orbital. 0-indexing is used in all cases. For the spin-polarized case, if orbs is a single dictionary rather than a 2-tuple, orbital indexing is performed over the combined set of orbitals.

Parameters:

  • analyzer (RHFAnalyzer or UHFAnalyzer) – analyzer object containing the desired molecule and density matrix/orbitals for which to compute features and their derivatives.

  • version – ‘b’, ‘d’, or ‘l’, feature version to evaluate. ‘l’ stands for local and computes [rho, drho_x, drho_y, drho_z, tau]

  • orbs – Orbital index dictionary for computing orbital derivatives.

  • **kwargs – Any settings one wants to change from the default for the PyscfNLDFGenerator object.

Returns:

The routine returns a tuple of length 3. The first item in the tuple is desc, an np.ndarray of shape (nspin, nfeat, ngrid), which contains the descriptors for the system. The second item is ddesc, a dict(str : dict(int : np.ndarray(nfeat, ngrds))), which contains derivatives of the features with respect to the orbitals given by orbs, stored in a nested dictionary first by indexing style (B, O, or U) and then by an orbital index. If orbs was a 2-tuple, ddesc is a 2-tuple. Index format is the same as orbs, so a U-indexed orbital in orbs is U-indexed in ddesc. If calculation was spin-polarized but orbs was not spin-separated, each value in the ddesc dictionary is a 2-tuple (spin, deriv_array). The final element in the array is eigvals, which is a (dict(str : dict(int : float)) or (dict, dict), containing the eigenvalues of the relevant orbitals. These are important as baseline energy derivatives, assuming the baseline functional is the same as the reference functional for the eigenvalues.

Return type:

tuple