{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Temperature and metallic systems\n",
"\n",
"In this example we consider the modeling of a magnesium lattice\n",
"as a simple example for a metallic system.\n",
"For our treatment we will use the PBE exchange-correlation functional.\n",
"First we import required packages and setup the lattice.\n",
"Again notice that DFTK uses the convention that lattice vectors are\n",
"specified column by column."
],
"metadata": {}
},
{
"outputs": [],
"cell_type": "code",
"source": [
"using DFTK\n",
"using Plots\n",
"using Unitful\n",
"using UnitfulAtomic\n",
"\n",
"a = 3.01794 # bohr\n",
"b = 5.22722 # bohr\n",
"c = 9.77362 # bohr\n",
"lattice = [[-a -a 0]; [-b b 0]; [0 0 -c]]\n",
"Mg = ElementPsp(:Mg, psp=load_psp(\"hgh/pbe/Mg-q2\"))\n",
"atoms = [Mg => [[2/3, 1/3, 1/4], [1/3, 2/3, 3/4]]];"
],
"metadata": {},
"execution_count": 1
},
{
"cell_type": "markdown",
"source": [
"Next we build the PBE model and discretize it.\n",
"Since magnesium is a metal we apply a small smearing\n",
"temperature to ease convergence using the Fermi-Dirac\n",
"smearing scheme. Note that both the `Ecut` is too small\n",
"as well as the minimal ``k``-point spacing\n",
"`kspacing` far too large to give a converged result.\n",
"These have been selected to obtain a fast execution time.\n",
"By default `PlaneWaveBasis` chooses a `kspacing`\n",
"of `2π * 0.022` inverse Bohrs, which is much more reasonable."
],
"metadata": {}
},
{
"outputs": [],
"cell_type": "code",
"source": [
"kspacing = 0.945 / u\"angstrom\" # Minimal spacing of k-points,\n",
"# in units of wavevectors (inverse Bohrs)\n",
"Ecut = 5 # kinetic energy cutoff in Hartree\n",
"temperature = 0.01 # Smearing temperature in Hartree\n",
"\n",
"model = model_DFT(lattice, atoms, [:gga_x_pbe, :gga_c_pbe];\n",
" temperature=temperature,\n",
" smearing=DFTK.Smearing.FermiDirac())\n",
"kgrid = kgrid_size_from_minimal_spacing(lattice, kspacing)\n",
"basis = PlaneWaveBasis(model; Ecut, kgrid);"
],
"metadata": {},
"execution_count": 2
},
{
"cell_type": "markdown",
"source": [
"Finally we run the SCF. Two magnesium atoms in\n",
"our pseudopotential model result in four valence electrons being explicitly\n",
"treated. Nevertheless this SCF will solve for eight bands by default\n",
"in order to capture partial occupations beyond the Fermi level due to\n",
"the employed smearing scheme. In this example we use a damping of `0.8`\n",
"and Kerker mixing to ease convergence."
],
"metadata": {}
},
{
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"n Free energy Eₙ-Eₙ₋₁ ρout-ρin α Diag\n",
"--- --------------- --------- -------- ---- ----\n",
" 1 -1.761879548929 NaN 5.05e-02 0.80 4.3\n",
" 2 -1.762138075450 -2.59e-04 1.90e-02 0.80 1.0\n",
" 3 -1.762238936013 -1.01e-04 1.56e-03 0.80 3.7\n",
" 4 -1.762241599314 -2.66e-06 3.23e-04 0.80 2.7\n",
" 5 -1.762241619953 -2.06e-08 1.94e-05 0.80 2.7\n"
]
}
],
"cell_type": "code",
"source": [
"scfres = self_consistent_field(basis, damping=0.8, mixing=KerkerMixing());"
],
"metadata": {},
"execution_count": 3
},
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "9-element Vector{Float64}:\n 1.9999999999077946\n 1.9999975862859352\n 0.004016907143064947\n 3.0004695346573174e-15\n 1.1115393375331326e-18\n 1.1114569430734638e-18\n 7.978877217343196e-19\n 7.978003207937892e-19\n 3.2806677544244535e-22"
},
"metadata": {},
"execution_count": 4
}
],
"cell_type": "code",
"source": [
"scfres.occupation[1]"
],
"metadata": {},
"execution_count": 4
},
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "Energy breakdown:\n Kinetic 0.7180592 \n AtomicLocal 0.3145402 \n AtomicNonlocal 0.3265797 \n Ewald -2.1544222\n PspCorrection -0.1026056\n Hartree 0.0055002 \n Xc -0.8610486\n Entropy -0.0088446\n\n total -1.762241619953\n"
},
"metadata": {},
"execution_count": 5
}
],
"cell_type": "code",
"source": [
"scfres.energies"
],
"metadata": {},
"execution_count": 5
},
{
"cell_type": "markdown",
"source": [
"The fact that magnesium is a metal is confirmed\n",
"by plotting the density of states around the Fermi level."
],
"metadata": {}
},
{
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "Plot{Plots.GRBackend() n=2}",
"image/png": "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",
"text/html": [
"\n",
"\n"
],
"image/svg+xml": [
"\n",
"\n"
]
},
"metadata": {},
"execution_count": 6
}
],
"cell_type": "code",
"source": [
"plot_dos(scfres)"
],
"metadata": {},
"execution_count": 6
}
],
"nbformat_minor": 3,
"metadata": {
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.6.2"
},
"kernelspec": {
"name": "julia-1.6",
"display_name": "Julia 1.6.2",
"language": "julia"
}
},
"nbformat": 4
}