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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ionisation potential of a porous material\n",
"\n",
"In this example we use `MacroDensity` with `VASP` to align the energy levels of a porous material.\n",
"\n",
"The procedure involves one DFT calculaion, yielding different important values:\n",
"\n",
"* $\\epsilon_{vbm}$ - the valence band maximum\n",
"* $V_{vac}$ - the vacuum potential\n",
"\n",
"The ionisation potential ($IP$) is then obtained from:\n",
"\n",
"$IP = V_{vac} - \\epsilon_{vbm}$\n",
"\n",
"The difference to a bulk calculation is that here the material itself has a vacuum within it. This allows us to sample the vacuum level from the vacuum region, as explained in [this paper](http://pubs.acs.org/doi/abs/10.1021/ja4110073).\n",
"\n",
"## Our system \n",
"\n",
"We will demonstrate this procedure for the porous system ZIF-8.\n",
"\n",
"\n",
"\n",
"## Procedure\n",
"\n",
"1. Optimise the structure\n",
"2. Calculate the electronic structure at your chosen level of theory, adding the `LVHAR` flag to the `INCAR` file:\n",
"\n",
" ``LVHAR = .TRUE. # This generates a LOCPOT file with the potential`` \n",
" \n",
"3. Locate the centre of the largest pore - do this \"by eye\" first\n",
"4. Plot the potential in that plane, so see if it plateaus\n",
"5. Plot a profile of the potential across the pore, again to see the plateau\n",
"6. Sample the potential from the pore centre"
]
},
{
"cell_type": "code",
"execution_count": 1,
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"name": "stderr",
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"text": [
"/tmp/ipykernel_13766/391802285.py:7: DeprecationWarning: the imp module is deprecated in favour of importlib and slated for removal in Python 3.12; see the module's documentation for alternative uses\n",
" import imp\n"
]
}
],
"source": [
"%matplotlib inline\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import sys\n",
"sys.path.append('../../')\n",
"import imp\n",
"import macrodensity as md\n",
"import math\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read the potential"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"if os.path.isfile('LOCPOT'):\n",
" print('LOCPOT already exists')\n",
"else:\n",
" os.system('bunzip2 LOCPOT.bz2')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading header information...\n",
"Reading 3D data using Pandas...\n",
"Average of the potential = 1.9559870102655787e-14\n"
]
}
],
"source": [
"input_file = 'LOCPOT'\n",
"\n",
"#=== No need to edit below\n",
"vasp_pot, NGX, NGY, NGZ, Lattice = md.read_vasp_density(input_file)\n",
"vector_a,vector_b,vector_c,av,bv,cv = md.matrix_2_abc(Lattice)\n",
"resolution_x = vector_a/NGX\n",
"resolution_y = vector_b/NGY\n",
"resolution_z = vector_c/NGZ\n",
"grid_pot, electrons = md.density_2_grid(vasp_pot, NGX, NGY, NGZ)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Look for pore centre points\n",
"\n",
"* For this we will use `VESTA`.\n",
" * Open the LOCPOT in `VESTA`.\n",
" * Expand to 2x2x2 cell, by choosing the Boundary option on the left hand side.\n",
" * Look for a pore centre - [1,1,1] is at a pore centre here.\n",
" * Now draw a lattice plane through that point.\n",
" * Choose Edit > Lattice Planes.\n",
" * Click New.\n",
" * Put in the Miller index (e.g. 1,1,1).\n",
" * Move the plane up and down using the d parameter, until it passes through the point you think is the centre.\n",
" * It should look like the picture below.\n",
"\n",
"\n",
"* Now we look at a contour plot of this plane to see if we are at a plateau.\n",
" * Utiltiy > 2D Data Display.\n",
" * Click Slice and enter the same parameters as in the 3D view.\n",
" * Now choose contours to play with the settings\n",
" * Z(max) and Z(min) tell you the potential max and min.\n",
" * Set contour max = Z(max) and contour min = 0 and the interval to 0.1.\n",
" * With some playing with the General settings, you can get something like this:\n",
" \n",
"\n",
"\n",
"* We can see the [1,1,1], at the centre of the picture is a maximum and is a plateau, so we can now use it for sampling.\n",
"\n",
"## Sampling the potential\n",
"\n",
"* We now set the point to sample at [1,1,1]\n",
"* We must also set the travelled parameter, for this type of analysis it is always [0,0,0]."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"cube_origin = [1,1,1]\n",
"travelled = [0,0,0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"216"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"int(cube_origin[0]*NGX)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* We want to try a range of sampling area sizes and analyse how this affects the potential.\n",
"* We also want low variance (plateau condidtions).\n",
"* Ideally we should have as large an area as possible, with low (< 1e-5) variance."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dimension Potential Variance\n",
"--------------------------------\n",
" 1 2.3068 0.000000\n",
" 10 2.3068 0.000001\n",
" 20 2.3068 0.000003\n",
" 40 2.3068 0.000019\n",
" 60 2.3067 0.000108\n",
" 80 2.3048 0.001151\n",
" 100 2.2883 0.015872\n"
]
}
],
"source": [
"dim = [1, 10, 20, 40, 60, 80, 100]\n",
"print(\"Dimension Potential Variance\")\n",
"print(\"--------------------------------\")\n",
"for d in dim:\n",
" cube = [d, d, d]\n",
" cube_potential, cube_var = md.volume_average(cube_origin, cube,grid_pot, NGX, NGY, NGZ, travelled=travelled)\n",
" print(\" %3i %10.4f %10.6f\"%(d, cube_potential, cube_var))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we take the potential value of `2.3068`. For the VBM value, we take it from the `OUTCAR` file (VBM: -2.4396 eV)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IP: 4.7464 eV\n"
]
}
],
"source": [
"print(\"IP: %3.4f eV\" % (2.3068 -- 2.4396 ))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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