{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Space-Reduced Bond-Order (SRBO) Grid \n",
"\n",
"A method for generating the PES grid for diatomics based on 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. http://doi.org/10.1021/acs.jpca.5b10018"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"from srbogrid.srbo import SRBO"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# H2\n",
"\n",
"To generate the grid for H2 molecule we need to know the [Morse potential](https://en.wikipedia.org/wiki/Morse_potential) parameters:\n",
"\n",
"- $D_{e}$ - dissociation energy in atomic units [hartree]\n",
"- $R_{e}$ - eqilibrium bond distance in atomic units [bohr]\n",
"- $k_{e}$ - force constant in atomic units [hartree / bohr2]\n",
"\n",
"To control the number of grid points generated you can specify:\n",
"\n",
"- `nrep` - number of points in the repulsive part of the potential (left from the equilibrium)\n",
"- `natt` - number of points in the attractive part of the potential (right from the equalibrium)\n",
"\n",
"To control the extent of the generated grid you can specify the parameters related to the potential itself:\n",
"\n",
"- `Vfact`: $V_{fact} = \\frac{V(r_{min})}{D_{e}}$\n",
"- `Vthrs`: $V_{thrs} = \\frac{D_{e} - V(r_{max})}{D_{e}}$\n",
"\n",
"or spefify `rmin` and `rmax` parameters directly."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# initialize the SRBO\n",
"h2grid = SRBO(Re=1.4034, De=0.1727, ke=0.3707, nrep=5, natt=10)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.63152744, 0.7632642 , 0.90460173, 1.05705019, 1.2225067 ,\n",
" 1.4034 , 1.60290972, 1.82531189, 2.07654928, 2.36522877,\n",
" 2.70449828, 3.1159375 , 3.63878276, 4.35671283, 5.50882366,\n",
" 8.7400997 ])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h2grid.grid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can view the parameters related to the created grid object using the `summary` method,"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"System info:\n",
"\tRe : 1.403400\n",
"\tDe : 0.172700\n",
"\tke : 0.370700\n",
"\talpha : 1.035977\n",
"\n",
"Boundaries:\n",
"\trmin : 0.631527\n",
"\trmax : 8.740100\n",
"\tVfact : 1.500000\n",
"\tVthrs : 0.001000\n",
"\n",
"\tBeta : 0.515422\n",
"\n",
"Grid:\n",
"\tnrep : 5\n",
"\tnatt : 10\n",
"\tnpoints : 16\n",
"\tf : 2.000000\n",
"\n",
"Grid points:\n",
"[0.63152744 0.7632642 0.90460173 1.05705019 1.2225067 1.4034\n",
" 1.60290972 1.82531189 2.07654928 2.36522877 2.70449828 3.1159375\n",
" 3.63878276 4.35671283 5.50882366 8.7400997 ]\n"
]
}
],
"source": [
"print(h2grid.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and plot the resulting grid on a Morse curve"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = h2grid.plot_morse()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Be2\n",
"\n",
"The potential parameters were taken from:\n",
"\n",
"**De** from Lesiuk, M., Przybytek, M., Musiał, M., Jeziorski, B., & Moszynski, R. (2015). Reexamination of the calculation of two-center, two-electron integrals over Slater-type orbitals. III. Case study of the beryllium dimer. Physical Review A, 91, 1–12. http://doi.org/10.1103/PhysRevA.91.012510\n",
"\n",
"**Re** from Merritt, J. M., Bondybey, V. E., & Heaven, M. C. (2009). Beryllium dimer--caught in the act of bonding. Science, 324(5934), 1548–51. http://doi.org/10.1126/science.1174326\n",
"\n",
"**ke** from Špirko, V. (2006). Potential energy curve of Be2 in its ground electronic state. Journal of Molecular Spectroscopy, 235(2), 268–270. http://doi.org/10.1016/j.jms.2005.11.012\n",
"\n",
"We will generate a grid from 3.0 to 15.0 bohr."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"System info:\n",
"\tRe : 4.637000\n",
"\tDe : 0.004233\n",
"\tke : 0.012017\n",
"\talpha : 1.191432\n",
"\n",
"Boundaries:\n",
"\trmin : 3.000000\n",
"\trmax : 15.000000\n",
"\tVfact : 36.376759\n",
"\tVthrs : 0.000009\n",
"\n",
"\tBeta : 0.116680\n",
"\n",
"Grid:\n",
"\tnrep : 6\n",
"\tnatt : 20\n",
"\tnpoints : 27\n",
"\tf : 3.333333\n",
"\n",
"Grid points:\n",
"[ 3. 3.25202943 3.51169535 3.779475 4.05589186 4.34152174\n",
" 4.637 4.94303012 5.26039373 5.58996267 5.93271342 6.28974454\n",
" 6.6622979 7.05178479 7.45981816 7.88825298 8.33923725 8.81527714\n",
" 9.31932133 9.85487176 10.42613131 11.03820433 11.69737445 12.41149835\n",
" 13.19057906 14.0476268 15. ]\n"
]
}
],
"source": [
"be2grid = SRBO(Re=4.637, De=0.0042328, ke=0.012017, nrep=6, natt=20, rmin=3.0, rmax=15)\n",
"\n",
"print(be2grid.summary())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(, )"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"be2grid.plot_morse()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Li2\n",
"\n",
"**Re** and **De** from Le Roy, R. J., Dattani, N. S., Coxon, J. A., Ross, A. J., Crozet, P., & Linton, C. (2009). Accurate analytic potentials for Li2(X Σ1g+) and Li2(A Σ1u+) from 2 to 90 Å, and the radiative lifetime of Li(2p). The Journal of Chemical Physics, 131(20), 204309. http://doi.org/10.1063/1.3264688\n",
"\n",
"**ke** from Chandra, A. K., & Sundar, R. (1972). Vibrational force constant and electron relaxation in H2 and Li2. Chemical Physics Letters, 14(5), 577–582. http://doi.org/10.1016/0009-2614(72)87211-7"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"System info:\n",
"\tRe : 5.051200\n",
"\tDe : 0.038805\n",
"\tke : 0.016300\n",
"\talpha : 0.458283\n",
"\n",
"Boundaries:\n",
"\trmin : 3.306333\n",
"\trmax : 26.661151\n",
"\tVfact : 1.500000\n",
"\tVthrs : 0.000100\n",
"\n",
"\tBeta : 0.113697\n",
"\n",
"Grid:\n",
"\tnrep : 6\n",
"\tnatt : 25\n",
"\tnpoints : 32\n",
"\tf : 4.166667\n",
"\n",
"Grid points:\n",
"[ 3.30633252 3.57415031 3.85038001 4.13556722 4.43031241 4.73527854\n",
" 5.0512 5.37889333 5.71926988 6.07335111 6.44228709 6.827379\n",
" 7.23010685 7.65216383 8.09549933 8.56237335 9.05542624 9.57776916\n",
" 10.13310311 10.72587812 11.36151016 12.0466826 12.7897754 13.60149286\n",
" 14.49581163 15.49146745 16.61439571 17.90196976 19.41090514 21.23344651\n",
" 23.53508864 26.66115099]\n"
]
}
],
"source": [
"li2grid = SRBO(Re=5.0512, De=0.0388053, ke=0.0163, Vthrs=0.0001, nrep=6, natt=25)\n",
"\n",
"print(li2grid.summary())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(, )"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"li2grid.plot_morse()"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}