.. _intro:

DiffiQult

DiffiQult is an open source autodifferentiable quantum chemistry package.

.. figure:: docs/h2o_3g_MO_1.gif

Method:

• RHF

Features:

• Single point calculations
• Energy gradients with respect to any parameter of the one-particle basis functions.
• Energy optimization with respect of any parameter of the Gaussian basis functions.

=============== Getting started with DiffiQult

Requirements

• Numpy

• Algopy

  • Official releases and installation:

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

    pip install algopy

• Python 2.7 (so far tested).

Installation

• From source:

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

  python setup.py install

=============== Usage

Molecular system

We define the parameters of a molecular systems with an System_mol object:

• molecular geometry in xyz format and atomic units
• basis sets (data base so far sto_3G
• number of electrons

For example:

.. code-block:: python

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

Tasks

The jobs in Diffiqult are managed by a Tasks object,

.. code-block:: python

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

where we defined the molecular system to optimize with the object system, and output options with verbose.

The class Task contains the method Tasks.runtask, it computes one the following options:

+----------------------+--------------+-------------------------------------------------------------------+ | Task | Key | Description | +======================+==============+===================================================================+ | Single point energies| Energy | It calculates the RHF energy and updates some attibute in system | +----------------------+--------------+-------------------------------------------------------------------+ | Optimization | Opt | It optimizes a given parameter and updates the basis set in system| +----------------------+--------------+-------------------------------------------------------------------+

Single point calculation

.. code-block:: python

        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 file (Compatible with molden)
                     output=True)

**Notes:** 

* We currently don'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.

Optimization

To optimize one or many input parameters, we use the option Opt. After a succesful optimization or If the optimization reaches the maximum number of steps or convergence, it updates the attributes of the system_mol object.

.. code-block:: python

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

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

+--------------------------+------------+ | Parameter | argnum | +==========================+============+ | Widths | 0 | +--------------------------+------------+ | Contraction coefficients | 1 | +--------------------------+------------+ | Gaussian centers | 2 | +--------------------------+------------+

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

.. code-block:: python

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

Additionally, if output is set to True, a molden file of each optimization step is printed.