class_docstr: Exchange Correlation Neural Network - Self Consistent Iterations
  
  In the Kohn-Sham theory, the inclusion of the noninteracting kinetic energy     functional results in a set of one-particle equations with Kohn-Sham
  orbitals as their solutions after functional differentiation. It is a
  variational approach that determines the lowest energy and the related
  molecular orbitals and orbital energies by using the electron-electron
  interaction potential. To learn more about Density Functional Theory
  and the Kohn-Sham approach please use the references below.
  
  The XCNNSCF is used for performing self-consistent iterations. The
  XC functional in the Kohn-Sham model implementation is replaced by a
  neural network.
  
  Examples
  --------
  >>> from deepchem.models.dft.scf import XCNNSCF
  >>> import torch
  >>> from deepchem.feat.dft_data import DFTEntry, DFTSystem
  >>> from deepchem.models.dft.nnxc import HybridXC
  >>> nnmodel = (torch.nn.Sequential(
  ...         torch.nn.Linear(2, 10),
  ...         torch.nn.Tanh(),
  ...         torch.nn.Linear(10, 1))).to(torch.double)
  >>> hybridxc = HybridXC("lda_x", nnmodel, aweight0=0.0)
  >>> e_type = 'dm'
  >>> true_val = 'deepchem/feat/tests/data/dftHF_output.npy'
  >>> systems = [{
  ...     'moldesc': 'H 0.86625 0 0; F -0.86625 0 0',
  ...     'basis': '6-311++G(3df,3pd)'
  ... }]
  >>> entry = DFTEntry.create(e_type, true_val, systems)
  >>> evl = XCNNSCF(hybridxc, entry)
  >>> system = DFTSystem(systems[0])
  >>> run = evl.run(system)
  >>> output = run.energy()
  
  Notes
  -----
  This code is derived from https://github.com/mfkasim1/xcnn/blob/f2cb9777da2961ac553f256ecdcca3e314a538ca/xcdnn2/evaluator.py
  
  References
  ----------
  deepchem.models.dft.nnxc
  Kohn, W. and Sham, L.J., 1965. Self-consistent equations including
  exchange and correlation effects. Physical review, 140(4A), p.A1133.
docstr: This method calculates and retuns the density matrix of a system.
  The matrix will vary depending on the atomic numbers, positions, and
  spins.
  
  Parameters
  ----------
  system: DFTSystem
docstr_tok: ["This","method","calculates","and","retuns","the","density","matrix","of","a","system",".","The","matrix","will","vary","depending","on","the","atomic","numbers",",","positions",",","and","spins",".","Parameters","--","--","--","--","--","system",":","DFTSystem"]
code: def _get_dm0(self, system: DFTSystem):
      """
      This method calculates and retuns the density matrix of a system.
      The matrix will vary depending on the atomic numbers, positions, and
      spins.
  
      Parameters
      ----------
      system: DFTSystem
      """
      dm_name = self._dm0_name(system)
      dm0: torch.Tensor
      get_dm = np.array(getattr(self, dm_name, None), dtype=bool)
      dm0 = torch.Tensor(get_dm)
      dm_exists = dm0 is not None
      dm_written = dm_exists and torch.any(dm0 != 0.0)
      if not dm_written:
          dm0_res: Union[
              None, torch.Tensor, SpinParam[torch.Tensor]
          ] = None
      elif system.get_dqc_mol().spin != 0:
          dm0_res = SpinParam(u=dm0[0].detach(), d=dm0[1].detach())
      else:
          dm0_res = dm0
  
      return dm0_res, (dm_name if dm_exists else None)
  
file_path: deepchem/deepchem/models/dft/scf.py
imports: from __future__ import annotations
  from abc import abstractmethod
  from typing import Union
  import torch
  import numpy
  from dqc.qccalc.ks import KS
  from deepchem.feat.dft_data import DFTEntry, DFTSystem
  from deepchem.utils.dftutils import KSCalc, hashstr, SpinParam
  from deepchem.models.dft.nnxc import BaseNNXC, HybridXC
label_desc: Use image node_id 5 for calling the XCNNSCF obj's underlying member method code with example usage: obj._get_dm0(system) and returns: dm0_res,  unknown