{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Performing a convergence study\n",
    "\n",
    "This example shows how to perform a convergence study to find an appropriate\n",
    "discretisation parameters for the Brillouin zone (`kgrid`) and kinetic energy\n",
    "cutoff (`Ecut`), such that the simulation results are converged to a desired\n",
    "accuracy tolerance.\n",
    "\n",
    "Such a convergence study is generally performed by starting with a\n",
    "reasonalbe base line value for `kgrid` and `Ecut` and then increasing these\n",
    "parameters (i.e. using finer discretisations) until a desired property (such\n",
    "as the energy) changes less than the tolerance.\n",
    "\n",
    "This procedure must be performed for each discretisation parameter. Beyond\n",
    "the `Ecut` and the `kgrid` also convergence in the smearing temperature or\n",
    "other numerical parameters should be checked. For simplicity we will neglect\n",
    "this aspect in this example and concentrate on `Ecut` and `kgrid`. Moreover\n",
    "we will restrict ourselves to using the same number of $k$-points in each\n",
    "dimension of the Brillouin zone.\n",
    "\n",
    "As the objective of this study we consider bulk platinum. For running the SCF conveniently we define a function:"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "using DFTK\n",
    "using LinearAlgebra\n",
    "using Statistics\n",
    "\n",
    "function run_scf(; a=5.0, Ecut, nkpt, tol)\n",
    "    atoms    = [ElementPsp(:Pt, psp = load_psp(\"hgh/lda/Pt-q10\"))]\n",
    "    position = [zeros(3)]\n",
    "    lattice  = a * Matrix(I, 3, 3)\n",
    "\n",
    "    model  = model_LDA(lattice, atoms, position; temperature=1e-2)\n",
    "    basis  = PlaneWaveBasis(model; Ecut, kgrid=(nkpt, nkpt, nkpt))\n",
    "    println(\"nkpt = $nkpt Ecut = $Ecut\")\n",
    "    self_consistent_field(basis; tol)\n",
    "end;"
   ],
   "metadata": {},
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "source": [
    "Moreover we define some parameters. To make the calculations run fast for the\n",
    "automatic generation of this documentation we target only a convergence to\n",
    "1e-2. In practice smaller tolerances (and thus larger upper bounds for\n",
    "`nkpts` and `Ecuts` are likely needed."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "tol   = 1e-2      # Tolerance to which we target to converge\n",
    "nkpts = 1:7       # K-point range checked for convergence\n",
    "Ecuts = 10:2:24;  # Energy cutoff range checked for convergence"
   ],
   "metadata": {},
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "source": [
    "As the first step we converge in the number of kpoints employed in each\n",
    "dimension of the Brillouin zone."
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nkpt = 1 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -26.49741906817                   -0.22    8.0\n",
      "  2   -26.58836237620       -1.04       -0.62    1.0\n",
      "  3   -26.61280068980       -1.61       -1.40    2.0\n",
      "  4   -26.61326646593       -3.33       -2.04    2.0\n",
      "nkpt = 2 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.79178984950                   -0.09    5.2\n",
      "  2   -26.23084159405       -0.36       -0.69    2.0\n",
      "  3   -26.23811363561       -2.14       -1.32    2.0\n",
      "  4   -26.23848448641       -3.43       -2.31    1.8\n",
      "nkpt = 3 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.78310889219                   -0.09    4.8\n",
      "  2   -26.23614628831       -0.34       -0.77    2.0\n",
      "  3   -26.25073967638       -1.84       -1.64    2.5\n",
      "  4   -26.25103354166       -3.53       -2.13    2.0\n",
      "nkpt = 4 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90974905416                   -0.11    4.7\n",
      "  2   -26.29041841880       -0.42       -0.75    2.1\n",
      "  3   -26.30835878292       -1.75       -1.74    2.3\n",
      "  4   -26.30842667771       -4.17       -2.69    2.1\n",
      "nkpt = 5 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90349837347                   -0.11    3.7\n",
      "  2   -26.26204072277       -0.45       -0.69    2.0\n",
      "  3   -26.28543461720       -1.63       -1.63    2.1\n",
      "  4   -26.28570510050       -3.57       -2.21    2.2\n",
      "nkpt = 6 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.87456844697                   -0.10    4.8\n",
      "  2   -26.26994940171       -0.40       -0.74    2.1\n",
      "  3   -26.28809805537       -1.74       -1.71    2.2\n",
      "  4   -26.28818805094       -4.05       -2.57    2.0\n",
      "nkpt = 7 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.89717243209                   -0.11    3.4\n",
      "  2   -26.27487981066       -0.42       -0.73    2.0\n",
      "  3   -26.29415697635       -1.71       -1.75    2.2\n",
      "  4   -26.29420805436       -4.29       -2.65    2.1\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "5"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "cell_type": "code",
   "source": [
    "function converge_kgrid(nkpts; Ecut, tol)\n",
    "    energies = [run_scf(; nkpt, tol=tol/10, Ecut).energies.total for nkpt in nkpts]\n",
    "    errors = abs.(energies[1:end-1] .- energies[end])\n",
    "    iconv = findfirst(errors .< tol)\n",
    "    (; nkpts=nkpts[1:end-1], errors, nkpt_conv=nkpts[iconv])\n",
    "end\n",
    "result = converge_kgrid(nkpts; Ecut=mean(Ecuts), tol)\n",
    "nkpt_conv = result.nkpt_conv"
   ],
   "metadata": {},
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "... and plot the obtained convergence:"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Plot{Plots.GRBackend() n=1}",
      "image/png": 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      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "cell_type": "code",
   "source": [
    "using Plots\n",
    "plot(result.nkpts, result.errors, dpi=300, lw=3, m=:o, yaxis=:log,\n",
    "     xlabel=\"k-grid\", ylabel=\"energy absolute error\")"
   ],
   "metadata": {},
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "We continue to do the convergence in Ecut using the suggested k-point grid."
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nkpt = 5 Ecut = 10\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.57958080618                   -0.16    3.4\n",
      "  2   -25.77480454892       -0.71       -0.74    1.9\n",
      "  3   -25.78626211028       -1.94       -1.87    2.0\n",
      "  4   -25.78631880670       -4.25       -2.81    2.0\n",
      "nkpt = 5 Ecut = 12\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.78766408098                   -0.12    3.5\n",
      "  2   -26.07365187343       -0.54       -0.70    2.0\n",
      "  3   -26.09343910867       -1.70       -1.70    2.0\n",
      "  4   -26.09374098797       -3.52       -2.29    2.2\n",
      "  5   -26.09375538806       -4.84       -2.82    2.1\n",
      "nkpt = 5 Ecut = 14\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.86882912494                   -0.11    3.6\n",
      "  2   -26.20473737205       -0.47       -0.69    2.0\n",
      "  3   -26.22672180482       -1.66       -1.66    2.1\n",
      "  4   -26.22700483042       -3.55       -2.24    2.2\n",
      "  5   -26.22702786299       -4.64       -2.82    2.4\n",
      "nkpt = 5 Ecut = 16\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.89833708665                   -0.11    3.7\n",
      "  2   -26.25250685839       -0.45       -0.69    2.0\n",
      "  3   -26.27560211915       -1.64       -1.64    2.1\n",
      "  4   -26.27586992265       -3.57       -2.22    2.2\n",
      "  5   -26.27589483703       -4.60       -2.87    2.5\n",
      "nkpt = 5 Ecut = 18\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90621377534                   -0.11    3.8\n",
      "  2   -26.26805157266       -0.44       -0.70    2.0\n",
      "  3   -26.29103419590       -1.64       -1.64    2.2\n",
      "  4   -26.29128156854       -3.61       -2.22    2.2\n",
      "  5   -26.29130575794       -4.62       -2.87    2.3\n",
      "nkpt = 5 Ecut = 20\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90867209840                   -0.11    3.9\n",
      "  2   -26.27264237845       -0.44       -0.70    2.0\n",
      "  3   -26.29535676157       -1.64       -1.65    2.2\n",
      "  4   -26.29558543326       -3.64       -2.24    2.2\n",
      "  5   -26.29560740647       -4.66       -2.87    2.5\n",
      "nkpt = 5 Ecut = 22\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90876810644                   -0.11    3.8\n",
      "  2   -26.27344653728       -0.44       -0.70    2.0\n",
      "  3   -26.29618671572       -1.64       -1.64    2.1\n",
      "  4   -26.29641445640       -3.64       -2.23    2.3\n",
      "  5   -26.29643720946       -4.64       -2.89    2.5\n",
      "nkpt = 5 Ecut = 24\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90854155019                   -0.11    3.9\n",
      "  2   -26.27374209523       -0.44       -0.70    2.0\n",
      "  3   -26.29627546620       -1.65       -1.65    2.2\n",
      "  4   -26.29649181343       -3.66       -2.25    2.3\n",
      "  5   -26.29651326593       -4.67       -2.91    2.5\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "18"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "cell_type": "code",
   "source": [
    "function converge_Ecut(Ecuts; nkpt, tol)\n",
    "    energies = [run_scf(; nkpt, tol=tol/100, Ecut).energies.total for Ecut in Ecuts]\n",
    "    errors = abs.(energies[1:end-1] .- energies[end])\n",
    "    iconv = findfirst(errors .< tol)\n",
    "    (; Ecuts=Ecuts[1:end-1], errors, Ecut_conv=Ecuts[iconv])\n",
    "end\n",
    "result = converge_Ecut(Ecuts; nkpt=nkpt_conv, tol)\n",
    "Ecut_conv = result.Ecut_conv"
   ],
   "metadata": {},
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "source": [
    "... and plot it:"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Plot{Plots.GRBackend() n=1}",
      "image/png": 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      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "cell_type": "code",
   "source": [
    "plot(result.Ecuts, result.errors, dpi=300, lw=3, m=:o, yaxis=:log,\n",
    "     xlabel=\"Ecut\", ylabel=\"energy absolute error\")"
   ],
   "metadata": {},
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "## A more realistic example.\n",
    "Repeating the above exercise for more realistic settings, namely ..."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "tol   = 1e-4  # Tolerance to which we target to converge\n",
    "nkpts = 1:20  # K-point range checked for convergence\n",
    "Ecuts = 20:1:50;"
   ],
   "metadata": {},
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "...one obtains the following two plots for the convergence in `kpoints` and `Ecut`."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<img src=\"https://docs.dftk.org/stable/assets/convergence_study_kgrid.png\" width=600 height=400 />\n",
    "<img src=\"https://docs.dftk.org/stable/assets/convergence_study_ecut.png\"  width=600 height=400 />"
   ],
   "metadata": {}
  }
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