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Kick-R.in
Kick-R.out
Kick-R.py
README.md
kicksum.py

README.md

Kick-R


Description

Kick-R is a Python program for carrying out a stochastic search for any type of molecular structure. The general approach follows Saunders' seminal "Kick" procedure, but adds "Restrictions" (-R) on what constitues an acceptable guess structure. These restrictions are that no pair of atoms in a guess structure can exhibit distances less than the sum of their combined covalent radii, and that each atom in a guess structure must be connected to every other atom by a network of bonds (where bonds are defined based on geometric criteria). Graph Theory is employed to check for adherence to the latter restriction.

Compared to Suanders' orginal Kick method, Kick-R delivers substantial improvements in search efficiency due to its truncation of the molecular configuration space searched to regions on the potential energy surface which are chemically relevant. In the present implementation, Kick-R is designed to interface with the Gaussian package for performing ab initio quantum chemistry optimizations. However, if use of another optimization package is desired, this code can be used simply for guess structure geometry initialization.

Please note that this program randomly initiates geometries, and then filters the resulting guess structures based on the two criteria outlined above. Due to the random nature of the geometry intialization, filtering times increase rapidly with system size. For this reason, use of this code is not recommended for systems larger than ~10 atoms.

Author: Chad McKee

Languages and Technologies used

  • Python 2.7

Dependencies and Plugins

None, but note that the present implementation of Kick-R is designed to interface with the Gaussian software suite for performing ab initio quantum mechanical optimizations.

Usage Example

The following assumes that Kick-R.py, Kick-R.in, and kicksum.py are in your current working directory.

An example input file (Kick-R.in) is provided below:

kick_number 250
box_size 8.68x8.68x8.68
multiplicity 2
dft_type PBEPBE
basis_set LANL2DZ
charge 0
structure 7
Au        0.000000000      0.000000000      0.000000000
Au        0.000000000      0.000000000      0.000000000
Au        0.000000000      0.000000000      0.000000000
Au        0.000000000      0.000000000      0.000000000
Au        0.000000000      0.000000000      0.000000000
Au        0.000000000      0.000000000      0.000000000
Au        0.000000000      0.000000000      0.000000000

This input will produce 250 candidate structures inside of a 8.68x8.68x8.68 Angstrom^3 box. The multiplicity, DFT method, basis set, and charge are then set as they would appear in a typical Gaussian input file. The structure keyword gives the number of atoms, and the atoms and their initial coodinates are then listed.

From the command line, Kick-R can be used to generate acceptable candidate structures by executing the following command:

python Kick-R.py

This will produce a summary file (Kick-R.out), and 250 (in this example) Gaussian input files which are to be submitted for optimization. An example of one such input file is:

%mem=4gb
%lindaworkers=LINDA
# UPBEPBE/LANL2DZ Opt=maxcycles=50

Title

0 2
Au  2.03907558319       -1.2628997452   1.18246662681
Au  -1.14620277447      2.47635533133   0.610008900135
Au  0.594469179038      1.09074156964   0.00957865796746
Au  -1.27735656662      -0.516589194542 -0.916367620249
Au  -0.435523996789     -1.34989418473  1.6629152073
Au  -2.01855389511      -1.94726269008  -2.8221232037
Au  -0.961636128879     -3.08273676614  -0.12877012753

END

Assuming Gaussian has been used to optimize the candidate structures, the script 'kicksum.py' can then be called to collect all unique structures located and order them according to their energy. A usage example is:

python kicksum.py 250 > results

This will write the results to a file called 'results'.

Code Snippet

Checking for Connectivity in the Molecular Graph

Kick-R checks to ensure that in all candidate structures every atom is connected to every other atom by a network of bonds. This is common task in graph theory. In Python, graphs can be easily represented via a dictionary, where the key-value pairs represent atoms which are bonded. Given a molecular graph, the follwoing function checks to see if two atoms are connected by a path consisting of bonds between atoms

def isConnected(graph, start, end, path=[]):
    path = path + [start]
    if start == end:
        return True
    for node in graph[start]:
        if node not in path:
            newpath = isConnected(graph, node, end, path)
            if newpath: return True
    return False

Using the above function, it is now easy to check to see if all atoms in a candidate structure are connected by a network of bonds.

def ConnectedCriteria(graph):
    passed, i, j = True, 1, 2
    while i < len(graph.keys()):
        while j <= len(graph.keys()):
            if not isConnected(graph, i, j):
                passed = False
                break
            j = j + 1
        if passed == False:
            break
        i = i + 1
        j = i + 1
    return passed