{
 "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=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   -26.49667635365                   -0.22    8.0   94.1ms\n",
      "  2   -26.59144090478       -1.02       -0.63    2.0   25.6ms\n",
      "  3   -26.61282164500       -1.67       -1.40    2.0   17.5ms\n",
      "  4   -26.61326111674       -3.36       -2.09    2.0   17.3ms\n",
      "nkpt = 2 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.79140724669                   -0.09    7.0   90.0ms\n",
      "  2   -26.23225684917       -0.36       -0.70    2.0   77.5ms\n",
      "  3   -26.23819993052       -2.23       -1.32    2.0   69.7ms\n",
      "  4   -26.23847927278       -3.55       -2.32    1.0   58.6ms\n",
      "nkpt = 3 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.78406880642                   -0.09    5.2   79.4ms\n",
      "  2   -26.23817888217       -0.34       -0.78    2.0   72.3ms\n",
      "  3   -26.25073465698       -1.90       -1.63    2.2   71.5ms\n",
      "  4   -26.25103881502       -3.52       -2.16    1.0   57.8ms\n",
      "nkpt = 4 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.91103072259                   -0.11    5.6    181ms\n",
      "  2   -26.29298249113       -0.42       -0.76    2.0    126ms\n",
      "  3   -26.30834811356       -1.81       -1.75    2.0    129ms\n",
      "  4   -26.30842698202       -4.10       -2.73    1.0   98.8ms\n",
      "nkpt = 5 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90248454954                   -0.11    3.9    148ms\n",
      "  2   -26.26539955822       -0.44       -0.71    2.0    122ms\n",
      "  3   -26.28545670647       -1.70       -1.64    2.0    128ms\n",
      "  4   -26.28571328188       -3.59       -2.29    1.0   99.2ms\n",
      "nkpt = 6 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.87463769099                   -0.10    5.5    337ms\n",
      "  2   -26.27227468382       -0.40       -0.76    1.9    208ms\n",
      "  3   -26.28808436813       -1.80       -1.72    2.0    228ms\n",
      "  4   -26.28818847810       -3.98       -2.62    1.0    166ms\n",
      "nkpt = 7 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.89637878002                   -0.11    3.4    257ms\n",
      "  2   -26.27756865413       -0.42       -0.74    2.0    214ms\n",
      "  3   -26.29415486952       -1.78       -1.75    2.0    226ms\n",
      "  4   -26.29420733855       -4.28       -2.69    1.0    165ms\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   -25.57842938370                   -0.16    3.5   74.6ms\n",
      "  2   -25.77693926285       -0.70       -0.76    1.8   74.5ms\n",
      "  3   -25.78627221476       -2.03       -1.86    2.0   65.8ms\n",
      "  4   -25.78631735850       -4.35       -2.87    1.0   49.3ms\n",
      "nkpt = 5 Ecut = 12\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.78610272295                   -0.12    3.6   79.1ms\n",
      "  2   -26.07613845632       -0.54       -0.71    1.9   72.1ms\n",
      "  3   -26.09344129212       -1.76       -1.69    2.1   68.4ms\n",
      "  4   -26.09374048457       -3.52       -2.34    1.0   49.6ms\n",
      "  5   -26.09375410743       -4.87       -2.73    1.1   51.3ms\n",
      "nkpt = 5 Ecut = 14\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.86747717024                   -0.11    3.8   69.6ms\n",
      "  2   -26.20739542391       -0.47       -0.71    2.0   50.8ms\n",
      "  3   -26.22671452427       -1.71       -1.64    2.1   55.4ms\n",
      "  4   -26.22700676122       -3.53       -2.29    1.0   43.7ms\n",
      "  5   -26.22702607513       -4.71       -2.71    1.0   41.3ms\n",
      "nkpt = 5 Ecut = 16\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.89718394576                   -0.11    3.9    147ms\n",
      "  2   -26.25578021743       -0.45       -0.71    2.0    121ms\n",
      "  3   -26.27562096126       -1.70       -1.65    2.0    126ms\n",
      "  4   -26.27587720089       -3.59       -2.30    1.0    123ms\n",
      "  5   -26.27589314438       -4.80       -2.73    1.0    122ms\n",
      "nkpt = 5 Ecut = 18\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90520112420                   -0.11    4.5    171ms\n",
      "  2   -26.27111142483       -0.44       -0.71    2.0    123ms\n",
      "  3   -26.29104405028       -1.70       -1.65    2.1    189ms\n",
      "  4   -26.29128892156       -3.61       -2.30    1.0    123ms\n",
      "  5   -26.29130396987       -4.82       -2.72    1.0    123ms\n",
      "nkpt = 5 Ecut = 20\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90794023387                   -0.11    4.1    273ms\n",
      "  2   -26.27531864396       -0.43       -0.71    2.0    175ms\n",
      "  3   -26.29534543197       -1.70       -1.64    2.0    189ms\n",
      "  4   -26.29558981512       -3.61       -2.30    1.0    123ms\n",
      "  5   -26.29560556570       -4.80       -2.72    1.0    128ms\n",
      "nkpt = 5 Ecut = 22\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90855566692                   -0.11    4.2    290ms\n",
      "  2   -26.27626072203       -0.43       -0.71    2.0    182ms\n",
      "  3   -26.29618226826       -1.70       -1.65    2.1    197ms\n",
      "  4   -26.29642032910       -3.62       -2.30    1.0    132ms\n",
      "  5   -26.29643526613       -4.83       -2.72    1.0    125ms\n",
      "nkpt = 5 Ecut = 24\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90858359773                   -0.11    4.0    268ms\n",
      "  2   -26.27651109032       -0.43       -0.72    2.0    175ms\n",
      "  3   -26.29626822916       -1.70       -1.65    2.2    194ms\n",
      "  4   -26.29649687298       -3.64       -2.31    1.0    117ms\n",
      "  5   -26.29651137033       -4.84       -2.74    1.0    128ms\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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