Package for calculating space-reduced bond-order grids for diatomics
SRBOgrid - Space-Reduced Bond Order Grid
This module calculates the Space-Reduced Bond Order grids for optimal configuration space sampling of the potenial energy curves for diatomics.
The code is based on the work described in: Rampino, S. (2016). Configuration-Space Sampling in Potential Energy Surface Fitting: A Space-Reduced Bond-Order Grid Approach. The Journal of Physical Chemistry A, 120(27), 4683–4692. doi: 10.1021/acs.jpca.5b10018
These instructions will give you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on deploying the project on a live system.
Best way to install
srbogrid is with
pip install srbogrid
from srbogrid.srbo import SRBO
The most straightforward way to compute the grid is to provide the parameters
Re: eqilibrium bond distance in atomic units [bohr]
De: dissociation energy in atomic units [hartree]
ke: force constant in atomic units [hartree / bohr^2]
For hydrogen molecule we can compute a SRBO grid with the following values:
h2 = SRBO(Re=1.4034, De=0.1727, ke=0.3707) h2.grid array([0.63152744, 0.7632642 , 0.90460173, 1.05705019, 1.2225067 , 1.4034 , 1.60290972, 1.82531189, 2.07654928, 2.36522877, 2.70449828, 3.1159375 , 3.63878276, 4.35671283, 5.50882366, 8.7400997 ])
By default the grid will contain:
- 5 points on the repulsive part of the potential energy curve (left from
- 10 points on the attractive part of the potential energy curve (right from
You can get more information by printing the
print(h2.summary()) System info: Re : 1.403400 De : 0.172700 ke : 0.370700 alpha : 1.035977 Boundaries: rmin : 0.631527 rmax : 8.740100 Vfact : 1.500000 Vthrs : 0.001000 Beta : 0.515422 Grid: nrep : 5 natt : 10 npoints : 16 f : 2.000000 Grid points: [0.63152744 0.7632642 0.90460173 1.05705019 1.2225067 1.4034 1.60290972 1.82531189 2.07654928 2.36522877 2.70449828 3.1159375 3.63878276 4.35671283 5.50882366 8.7400997 ]
You can visualize the grid on a model Morse potential with:
A short tutorial is available here as a jupyter notebook.
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