{
 "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",
    "reasonable 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\n",
    "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; is_converged=DFTK.ScfConvergenceEnergy(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 $k$-points 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   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -26.49697566169                   -0.22    8.0         \n",
      "  2   -26.58809489553       -1.04       -0.62    1.0    104ms\n",
      "  3   -26.61295165723       -1.60       -1.41    2.0   33.2ms\n",
      "  4   -26.61325994956       -3.51       -2.05    2.0   32.5ms\n",
      "nkpt = 2 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.79180735067                   -0.09    7.2         \n",
      "  2   -26.23210711687       -0.36       -0.70    2.0    140ms\n",
      "  3   -26.23818372763       -2.22       -1.31    2.0    110ms\n",
      "  4   -26.23847876265       -3.53       -2.33    1.0   88.7ms\n",
      "nkpt = 3 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.78514667592                   -0.09    4.8         \n",
      "  2   -26.23814375684       -0.34       -0.78    2.0    130ms\n",
      "  3   -26.25072828012       -1.90       -1.63    2.0    108ms\n",
      "  4   -26.25103625918       -3.51       -2.16    1.2   92.3ms\n",
      "nkpt = 4 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.91113712482                   -0.11    5.2         \n",
      "  2   -26.29256116555       -0.42       -0.76    1.7    191ms\n",
      "  3   -26.30833828739       -1.80       -1.73    2.3    222ms\n",
      "  4   -26.30842564865       -4.06       -2.69    1.1    177ms\n",
      "nkpt = 5 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90219649335                   -0.11    3.7         \n",
      "  2   -26.26456663744       -0.44       -0.71    1.9    203ms\n",
      "  3   -26.28541578494       -1.68       -1.61    2.1    214ms\n",
      "  4   -26.28570514518       -3.54       -2.23    1.9    201ms\n",
      "nkpt = 6 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.87454741613                   -0.10    5.1         \n",
      "  2   -26.27204075372       -0.40       -0.76    1.9    352ms\n",
      "  3   -26.28807821305       -1.79       -1.71    2.0    401ms\n",
      "  4   -26.28818783747       -3.96       -2.62    1.1    286ms\n",
      "nkpt = 7 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.89690335823                   -0.11    3.4         \n",
      "  2   -26.27685058925       -0.42       -0.74    1.9    360ms\n",
      "  3   -26.29413722796       -1.76       -1.73    2.0    383ms\n",
      "  4   -26.29420658473       -4.16       -2.69    1.0    305ms\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   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.57901185486                   -0.16    3.5         \n",
      "  2   -25.77622950547       -0.71       -0.76    1.2   91.0ms\n",
      "  3   -25.78625100225       -2.00       -1.85    2.0    113ms\n",
      "  4   -25.78631728321       -4.18       -2.86    1.0   80.7ms\n",
      "nkpt = 5 Ecut = 12\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.78666812220                   -0.12    3.5         \n",
      "  2   -26.07622751549       -0.54       -0.71    1.8    105ms\n",
      "  3   -26.09344687385       -1.76       -1.69    2.0    116ms\n",
      "  4   -26.09374141757       -3.53       -2.33    1.4   95.6ms\n",
      "  5   -26.09375426087       -4.89       -2.73    1.9    110ms\n",
      "nkpt = 5 Ecut = 14\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.86795349781                   -0.11    3.6         \n",
      "  2   -26.20689919364       -0.47       -0.71    1.9   79.0ms\n",
      "  3   -26.22668928702       -1.70       -1.64    2.0   87.2ms\n",
      "  4   -26.22700265801       -3.50       -2.25    1.7   77.4ms\n",
      "  5   -26.22702616655       -4.63       -2.71    1.8    103ms\n",
      "nkpt = 5 Ecut = 16\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.89676889738                   -0.11    4.4         \n",
      "  2   -26.25477974615       -0.45       -0.71    2.0    201ms\n",
      "  3   -26.27557090131       -1.68       -1.62    2.1    211ms\n",
      "  4   -26.27586891389       -3.53       -2.24    1.9    192ms\n",
      "  5   -26.27589343085       -4.61       -2.76    1.9    213ms\n",
      "nkpt = 5 Ecut = 18\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90516991294                   -0.11    3.8         \n",
      "  2   -26.27016754368       -0.44       -0.71    2.0    216ms\n",
      "  3   -26.29099531024       -1.68       -1.61    2.2    218ms\n",
      "  4   -26.29127833039       -3.55       -2.23    1.8    192ms\n",
      "  5   -26.29130429363       -4.59       -2.76    1.9    218ms\n",
      "nkpt = 5 Ecut = 20\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90779954816                   -0.11    3.9         \n",
      "  2   -26.27442274511       -0.44       -0.71    1.9    211ms\n",
      "  3   -26.29529615436       -1.68       -1.61    2.2    226ms\n",
      "  4   -26.29557845171       -3.55       -2.22    1.9    208ms\n",
      "  5   -26.29560602136       -4.56       -2.77    1.9    221ms\n",
      "nkpt = 5 Ecut = 22\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90831544087                   -0.11    4.2         \n",
      "  2   -26.27577962157       -0.43       -0.71    2.0    208ms\n",
      "  3   -26.29615880525       -1.69       -1.63    2.0    229ms\n",
      "  4   -26.29641645203       -3.59       -2.27    1.9    213ms\n",
      "  5   -26.29643545100       -4.72       -2.73    1.8    207ms\n",
      "nkpt = 5 Ecut = 24\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90880769801                   -0.11    3.8         \n",
      "  2   -26.27566222164       -0.44       -0.71    2.0    179ms\n",
      "  3   -26.29622326141       -1.69       -1.62    2.2    204ms\n",
      "  4   -26.29648875799       -3.58       -2.24    2.0    179ms\n",
      "  5   -26.29651169665       -4.64       -2.76    2.0    190ms\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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