DiffiQult is an open source autodifferentiable Quantum Chemistry package, that optimized RHF energies of small molecules with respect of the parameters a given intial the basis function. It generates energy gradients with automatic differentiation tools.

• Numpy

• Algopy

  • Official releases and installation:

    Available at: http://pypi.python.org/pypi/algopy

    pip install algopy

• Python 2.7 (so far).

Installation

• From source:

  git clone https://github.com/ttamayo/DiffiQult.git

  python setup.py install

We define the molecular systems by creating an object of the class System_mol, where we specify the molecular geometry in atomic units, basis sets and number of electrons, optionally we can specify a tag. For example:

  # Basis set is sto_3G
  from diffiqult.Basis import basis_set_3G_STO as basis
     # Our molecule H_2
     d = -1.64601435
     mol = [(1,(0.0,0.0,0.20165898)),(1,(0.0,0.0,d))]
     
     # Number of electrons
     ne = 2
     system = System_mol(mol,                                ## Geometry
                         basis,                              ## Basis set (if shifted it should have the coordinates too)
                         ne,                                 ## Number of electrons
                         shifted=False,                      ## If the basis is going to be on the atoms coordinates 
                         mol_name='agua')                    ## Units -> Bohr

The jobs in Diffiqult are managed by an Tasks object. For example, if we want to obtain a file with the results.

   manager = Tasks(system,
                name='h2_sto_3g',      ## Prefix for all optput files
                verbose=True)          ## If there is going to be an output

where we define the molecular system to optimize with system and output options with verbose.

The method Tasks.runtask computes one the following tasks:

To calculate the RHF of a molecule we use the option Energy, optionally we can produce a molden file with the information of the geometry, basis and MO.

        manager.runtask('Energy',
                     max_scf=50,                        # Maximum number of SCF cycles
                     printcoef=True,                    # This will produce a npy file with the molecular coefficients
                     name='Output.molden',              # Name of the output
                     output=True)

Notes:

• It currently doesn't have convergence options for the SCF .
• The molden file also contains an input section that can be used as input for system with the option shifted
• The geometry and MOs can be vizualized with molden,and the molden file.

To optimize one or many input parameters, we use the option ```Opt` ``, it updates the attributes of the molecular system object.

For example:

    manager.runtask('Opt',
                     max_scf=50,
                     printcoef=False,
                     argnum=[2],  # Optimization of centers
                     output=True) # We optimized all the steps

where argnum recieves a list with the parameters to optimize with the following convention:

For example, we can optimize the atomic centered basis function with respect of their widths and contraction coefficients in the following way.

 manager.runtask('Opt',
                     max_scf=50,
                     printcoef=False,
                     argnum=[0,1],  # Optimization of centers
                     output=True)   # We print a molden file of all steps